Publications by authors named "Peipei Pang"

54 Publications

Pre-treatment CT-based radiomics nomogram for predicting microsatellite instability status in colorectal cancer.

Eur Radiol 2021 Jul 13. Epub 2021 Jul 13.

Department of Diagnostic Radiology, City of Hope National Medical Center, Duarte, CA, USA.

Objectives: Stratification of microsatellite instability (MSI) status in patients with colorectal cancer (CRC) improves clinical decision-making for cancer treatment. The present study aimed to develop a radiomics nomogram to predict the pre-treatment MSI status in patients with CRC.

Methods: A total of 762 patients with CRC confirmed by surgical pathology and MSI status determined with polymerase chain reaction (PCR) method were retrospectively recruited between January 2013 and May 2019. Radiomics features were extracted from routine pre-treatment abdominal pelvic computed tomography (CT) scans acquired as part of the patients' clinical care. A radiomics nomogram was constructed using multivariate logistic regression. The performance of the nomogram was evaluated using discrimination, calibration, and decision curves.

Results: The radiomics nomogram incorporating radiomics signatures, tumor location, patient age, high-density lipoprotein expression, and platelet counts showed good discrimination between patients with non-MSI-H and MSI-H, with an area under the curve (AUC) of 0.74 [95% CI, 0.68-0.80] in the training cohort and 0.77 [95% CI, 0.68-0.85] in the validation cohort. Favorable clinical application was observed using decision curve analysis. The addition of pathological characteristics to the nomogram failed to show incremental prognostic value.

Conclusions: We developed a radiomics nomogram incorporating radiomics signatures and clinical indicators, which could potentially be used to facilitate the individualized prediction of MSI status in patients with CRC.

Key Points: • There is an unmet need to non-invasively determine MSI status prior to treatment. However, the traditional radiological evaluation of CT is limited for evaluating MSI status. • Our non-invasive CT imaging-based radiomics method could efficiently distinguish patients with high MSI disease from those with low MSI disease. • Our radiomics approach demonstrated promising diagnostic efficiency for MSI status, similar to the commonly used IHC method.
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http://dx.doi.org/10.1007/s00330-021-08167-3DOI Listing
July 2021

Myosteatosis predicting risk of transition to severe COVID-19 infection.

Clin Nutr 2021 Jun 7. Epub 2021 Jun 7.

Department of Diagnostic Radiology, City of Hope National Medical Center, Duarte, CA, USA.

Background: About 10-20% of patients with Coronavirus disease 2019 (COVID-19) infection progressed to severe illness within a week or so after initially diagnosed as mild infection. Identification of this subgroup of patients was crucial for early aggressive intervention to improve survival. The purpose of this study was to evaluate whether computer tomography (CT) - derived measurements of body composition such as myosteatosis indicating fat deposition inside the muscles could be used to predict the risk of transition to severe illness in patients with initial diagnosis of mild COVID-19 infection.

Methods: Patients with laboratory-confirmed COVID-19 infection presenting initially as having the mild common-subtype illness were retrospectively recruited between January 21, 2020 and February 19, 2020. CT-derived body composition measurements were obtained from the initial chest CT images at the level of the twelfth thoracic vertebra (T12) and were used to build models to predict the risk of transition. A myosteatosis nomogram was constructed using multivariate logistic regression incorporating both clinical variables and myosteatosis measurements. The performance of the prediction models was assessed by receiver operating characteristic (ROC) curve including the area under the curve (AUC). The performance of the nomogram was evaluated by discrimination, calibration curve, and decision curve.

Results: A total of 234 patients were included in this study. Thirty-one of the enrolled patients transitioned to severe illness. Myosteatosis measurements including SM-RA (skeletal muscle radiation attenuation) and SMFI (skeletal muscle fat index) score fitted with SMFI, age and gender, were significantly associated with risk of transition for both the training and validation cohorts (P < 0.01). The nomogram combining the SM-RA, SMFI score and clinical model improved prediction for the transition risk with an AUC of 0.85 [95% CI, 0.75 to 0.95] for the training cohort and 0.84 [95% CI, 0.71 to 0.97] for the validation cohort, as compared to the nomogram of the clinical model with AUC of 0.75 and 0.74 for the training and validation cohorts respectively. Favorable clinical utility was observed using decision curve analysis.

Conclusion: We found CT-derived measurements of thoracic myosteatosis to be associated with higher risk of transition to severe illness in patients affected by COVID-19 who presented initially as having the mild common-subtype infection. Our study showed the relevance of skeletal muscle examination in the overall assessment of disease progression and prognosis of patients with COVID-19 infection.
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http://dx.doi.org/10.1016/j.clnu.2021.05.031DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8180452PMC
June 2021

Preoperative histogram parameters of dynamic contrast-enhanced MRI as a potential imaging biomarker for assessing the expression of Ki-67 in prostate cancer.

Cancer Med 2021 Jul 12;10(13):4240-4249. Epub 2021 Jun 12.

Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China.

Purpose: To investigate whether preoperative histogram parameters of dynamic contrast-enhanced MRI (DCE-MRI) can assess the expression of Ki-67 in prostate cancer (PCa).

Materials And Methods: A consecutive series of 76 patients with pathology-proven PCa who underwent routine DCE-MRI scans were retrospectively recruited. Quantitative parameters including the volume transfer constant (K ), rate contrast (K ), extracellular-extravascular volume fraction (V ), and plasma volume (V ) by outlining the three-dimensional volume of interest (VOI) of all lesions were processed. Then, the histogram analyses of these quantitative parameters were performed. The Spearman rank correlation analysis was used to evaluate the correlation of these parameters and Ki-67 expression of PCa. Receiver operating characteristic (ROC) curve analysis was adopted to evaluate the efficacy of these quantitative histogram parameters in identifying high Ki-67 expression from low Ki-67 expression of PCa.

Results: Eighty-eight PCa lesions were enrolled in this study, including 31 lesions with high Ki-67 expression and 57 lesions with low Ki-67 expression. The median, mean, 75th percentile, and 90th percentile derived from K and K had a moderately positive correlation with Ki-67 expression (r = 0.361-0.450, p < 0.05), in which both the median and mean of K had the highest positive correlation (r = 0.450, p < 0.05). The diagnostic efficacy of the K median, mean, 75th percentile, and 90th percentile, along with the K -based median and mean was assessed by the ROC curve. The area under the curve (AUC) of the mean for K was the highest (0.826). When the cut-off of the mean for K was ≥0.47/min, its Youden index, sensitivity, and specificity were 0.625, 0.871, and 0.754, respectively. The AUC of the median of K was the lowest (0.772).

Conclusion: The histogram of DCE-MRI quantitative parameters is correlated with Ki-67 expression, which has the potential to noninvasively assess the expression of Ki-67 with patients of PCa.
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http://dx.doi.org/10.1002/cam4.3912DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8267123PMC
July 2021

Value of radiomics model based on enhanced computed tomography in risk grade prediction of gastrointestinal stromal tumors.

Sci Rep 2021 Jun 8;11(1):12009. Epub 2021 Jun 8.

Department of Radiology, Jiangxi Provincial People's Hospital Affiliated to Nanchang University, Nanchang, 330006, China.

To explore the application of computed tomography (CT)-enhanced radiomics for the risk-grade prediction of gastrointestinal stromal tumors (GIST). GIST patients (n = 292) confirmed by surgery or endoscopic pathology during June 2013-2019 were reviewed and categorized into low-grade (very low to low risk) and high-grade (medium to high risk) groups. The tumor region of interest (ROI) was depicted layer by layer on each patient's enhanced CT venous phase images using the ITK-SNAP. The texture features were extracted using the Analysis Kit (AK) and then randomly divided into the training (n = 205) and test (n = 87) groups in a ratio of 7:3. After dimension reduction by the least absolute shrinkage and the selection operator algorithm (LASSO), a prediction model was constructed using the logistic regression method. The clinical data of the two groups were statistically analyzed, and the multivariate regression prediction model was constructed by using statistically significant features. The ROC curve was applied to evaluate the prediction performance of the proposed model. A radiomics-prediction model was constructed based on 10 characteristic parameters selected from 396 quantitative feature parameters extracted from the CT images. The proposed radiomics model exhibited effective risk-grade prediction of GIST. For the training group, the area under curve (AUC), sensitivity, specificity, and accuracy rate were 0.793 (95%CI: 0.733-0.854), 83.3%, 64.3%, and 72.7%, respectively; the corresponding values for the test group were 0.791 (95%CI: 0.696-0.886), 84.2%, 69.3%, and 75.9%, respectively. There were significant differences in age (t value: - 3.133, P = 0.008), maximum tumor diameter (Z value: - 12.163, P = 0.000) and tumor morphology (χ value:10.409, P = 0.001) between the two groups, which were used to establish a clinical prediction model. The area under the receiver operating characteristic curve of the clinical model was 0.718 (95%CI: 0.659-0.776). The proposed CT-enhanced radiomics model exhibited better accuracy and effective performance than the clinical model, which can be used for the assessment of risk grades of GIST.
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http://dx.doi.org/10.1038/s41598-021-91508-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8187426PMC
June 2021

A radiomic nomogram based on arterial phase of CT for differential diagnosis of ovarian cancer.

Abdom Radiol (NY) 2021 06 4;46(6):2384-2392. Epub 2021 Jun 4.

Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, Lishui, 323000, China.

Purpose: To develop and validate a radiomic nomogram based on arterial phase of CT to discriminate the primary ovarian cancers (POCs) and secondary ovarian cancers (SOCs).

Methods: A total of 110 ovarian cancer patients in our hospital were reviewed from January 2010 to December 2018. Radiomic features based on the arterial phase of CT were extracted by Artificial Intelligence Kit software (A.K. software). The least absolute shrinkage and selection operation regression (LASSO) was employed to select features and construct the radiomics score (Rad-score) for further radiomics signature calculation. Multivariable logistic regression analysis was used to develop the predicting model. The predictive nomogram model was composed of rad-score and clinical data. Nomogram discrimination and calibration were evaluated.

Results: Two radiomic features were selected to build the radiomics signature. The radiomics nomogram that incorporated 2 radiomics signature and 2 clinical factors (CA125 and CEA) showed good discrimination in training cohort (AUC 0.854), yielding the sensitivity of 78.8% and specificity of 90.7%, which outperformed the prediction model based on radiomics signature or clinical data alone. A visualized differential nomogram based on the radiomic score, CEA, and CA125 level was established. The calibration curve demonstrated the clinical usefulness of the proposed nomogram.

Conclusion: The presented nomogram, which incorporated radiomic features of arterial phase of CT with clinical features, could be useful for differentiating the primary and secondary ovarian cancers.
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http://dx.doi.org/10.1007/s00261-021-03120-wDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8205899PMC
June 2021

Diagnostic Performance of Vascular Permeability and Texture Parameters for Evaluating the Response to Neoadjuvant Chemoradiotherapy in Patients With Esophageal Squamous Cell Carcinoma.

Front Oncol 2021 18;11:604480. Epub 2021 May 18.

Department of Radiotherapy, Taizhou Hospital of Zhejiang Province, Taizhou, China.

Background: Esophageal squamous cell carcinoma (ESCC) is an aggressive type of cancer, associated with poor prognosis. The development of an accurate and non-invasive method to evaluate the pathologic response of patients with ESCC to chemoradiotherapy remains a critical issue. Therefore, the aim of this study was to assess the importance of vascular permeability and texture parameters in predicting the response to neoadjuvant chemoradiotherapy (NACRT) in patients with ESCC.

Methods: This prospective analysis included patients with T1-T2 stage of ESCC, without either lymphatic or metastasis, and distant metastasis. All patients underwent surgery having received two rounds of NACRT. All patients underwent dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) twice, i.e., before the first NACRT and after the second NACRT. Patients were assessed for treatment response at 30 days after the second NACRT. Patients were divided into the complete response (CR) and partial response (PR) groups based on their responses to NACRT. Vascular permeability and texture parameters were extracted from the DCE-MRI scans. After assessing the diagnostic performance of individual parameters, a combined model with vascular permeability and texture parameters was generated to predict the response to NACRT.

Results: In this study, the CR and PR groups included 16 patients each. The volume transfer constant (Ktrans), extracellular extravascular volume fraction (ve), and entropy values, as well as changes to each of these parameters, extracted from the second DCE-MRI scans, showed significant differences between the CR and PR groups. The area under the curve (AUC) of Ktrans, ve, and entropy values showed good diagnostic ability (0.813, 0.789, and 0.707, respectively). A logistic regression model combining Ktrans, ve, and entropy had significant diagnostic ability (AUC=0.977).

Conclusions: The use of a combined model with vascular permeability and texture parameters can improve post-NACRT prognostication in patients with ESCC.
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http://dx.doi.org/10.3389/fonc.2021.604480DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8168434PMC
May 2021

A clinical-radiomics model incorporating T2-weighted and diffusion-weighted magnetic resonance images predicts the existence of lymphovascular invasion / perineural invasion in patients with colorectal cancer.

Med Phys 2021 May 27. Epub 2021 May 27.

Shaoxing University School of Medicine, Shaoxing, China.

Purpose: Lymphovascular invasion (LVI) and perineural invasion (PNI) are independent prognostic factors in patients with colorectal cancer (CRC). In this study, we aimed to develop and validate a preoperative predictive model based on high-throughput radiomic features and clinical factors for accurate prediction of LVI/PNI in these patients.

Methods: Two hundred and sixty-three patients who underwent colorectal resection for histologically confirmed CRC between 1 February 2011 and 30 June 2020 were retrospectively enrolled. Between 1 February 2011 and 30 September 2018, 213 patients were randomly divided into a training cohort (n = 149) and a validation cohort (n = 64) by a ratio of 7:3. We used a 10000-iteration bootstrap analysis to estimate the prediction error and confidence interval for two cohorts. The independent test cohort consisted of 50 patients between 1 October 2018 and 30 June 2020. Regions of interest (ROIs) were manually delineated in high-resolution T2-weighted and diffusion-weighted images using ITK-SNAP software on each CRC tumor slice. In total, 3356 radiomic features were extracted from each ROI. Next, we used the maximum relevance minimum redundancy and least absolute shrinkage and selection operator algorithms to select the strongest of these features to establish a clinical-radiomics model for predicting LVI/PNI. Receiver-operating characteristic and calibration curves were then plotted to evaluate the predictive performance of the model in the training, validation, and independent test cohorts.

Results: A multiparametric clinical-radiomics model combining MRI-reported extramural vascular invasion (EMVI) status and a Radiomics score for the LVI/PNI estimation was established. This model had significant predictive power in the training cohort (area under the curve [AUC] 0.91; 95% confidence interval [CI]: 0.85-0.97), validation cohort (AUC: 0.88; 95% CI: 0.79-89), and independent test cohorts (AUC 0.83, 95% CI 0.72-0.95). The model performed well in the independent test cohort with sensitivity of 0.818, specificity of 0.714, and accuracy of 0.760. Calibration curve and decision curve analysis demonstrated clinical benefits.

Conclusion: Multiparametric clinical-radiomics models can accurately predict LVI/PNI in patients with CRC. Our model has predictive ability that should improve preoperative diagnostic performance and allow more individualized treatment decisions.
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http://dx.doi.org/10.1002/mp.15001DOI Listing
May 2021

MRI-Based Radiomics Models for Predicting Risk Classification of Gastrointestinal Stromal Tumors.

Front Oncol 2021 10;11:631927. Epub 2021 May 10.

Department of Radiology, Shaoxing People's Hospital, Shaoxing Hospital, Zhejiang University School of Medicine, Shaoxing, China.

Background: We conduct a study in developing and validating four MRI-based radiomics models to preoperatively predict the risk classification of gastrointestinal stromal tumors (GISTs).

Methods: Forty-one patients (low-risk = 17, intermediate-risk = 13, high-risk = 11) underwent MRI before surgery between September 2013 and March 2019 in this retrospective study. The Kruskal-Wallis test with Bonferonni correction and variance threshold was used to select appropriate features, and the Random Forest model (three classification model) was used to select features among the high-risk, intermediate-risk, and low-risk of GISTs. The predictive performance of the models built by the Random Forest was estimated by a 5-fold cross validation (5FCV). Their performance was estimated using the receiver operating characteristic (ROC) curve, summarized as the area under the ROC curve (AUC). Area under the curve (AUC), accuracy, sensitivity, and specificity for risk classification were reported. Linear discriminant analysis (LDA) was used to assess the discriminative ability of these radiomics models.

Results: The high-risk, intermediate-risk, and low-risk of GISTs were well classified by radiomics models, the micro-average of ROC curves was 0.85, 0.81, 0.87 and 0.94 for T1WI, T2WI, ADC and combined three MR sequences. And ROC curves achieved excellent AUCs for T1WI (0.85, 0.75 and 0.82), T2WI (0.69, 0.78 and 0.78), ADC (0.85, 0.77 and 0.80) and combined three MR sequences (0.96, 0.92, 0.81) for the diagnosis of high-risk, intermediate-risk, and low-risk of GISTs, respectively. In addition, LDA demonstrated the different risk of GISTs were correctly classified by radiomics analysis (61.0% for T1WI, 70.7% for T2WI, 83.3% for ADC, and 78.9% for the combined three MR sequences).

Conclusions: Radiomics models based on a single sequence and combined three MR sequences can be a noninvasive method to evaluate the risk classification of GISTs, which may help the treatment of GISTs patients in the future.
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http://dx.doi.org/10.3389/fonc.2021.631927DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8141866PMC
May 2021

The value of various peritumoral radiomic features in differentiating the invasiveness of adenocarcinoma manifesting as ground-glass nodules.

Eur Radiol 2021 May 26. Epub 2021 May 26.

Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Traditional Chinese Medicine), Hangzhou, China.

Objectives: To evaluate the ability of CT radiomic features extracted from peritumoral parenchyma of 2 mm and 5 mm distinguishing invasive adenocarcinoma (IAC) from adenocarcinoma in situ (AIS)/minimally invasive adenocarcinoma (MIA).

Methods: For this retrospective study, 121 lung adenocarcinomas appearing as ground-glass nodules on thin-section CT were evaluated. Quantitative radiomic features were extracted from the peritumoral parenchymal region of 2 mm and 5 mm on CT imaging, and the radiomic models of External2 and External5 were constructed. The ROC curves were used to evaluate the performance of different models. Differences between the AUCs were evaluated using DeLong's method.

Results: The radiomic scores of IAC were statistically higher than those of MIA/AIS in both the External2 and External5 models. The AUCs of the External2 and External5 models were 0.882, 0.778 in the training cohort and 0.888, 0.804 in the validation cohort, respectively. The AUC of the External2 model was not statistically different from the External5 model both in the training cohort (p = 0.116) and validation cohort (p = 0.423).

Conclusions: The radiomic features extracted from the peritumoral region of 2 mm and 5 mm at thin-section CT showed good predictive values to differentiate the IAC from AIS/MIA. The radiomic features from the peritumoral region of 5 mm provide no additional benefit in distinguishing IAC from MIA/AIS than that of the 2 mm region.

Key Points: • The radiomic models from various peritumoral lung parenchyma were developed and validated to predict invasiveness of adenocarcinoma. • The peritumoral parenchyma of lung adenocarcinoma may contain useful information. • Radiomics from peritumoral lung parenchyma of 5 mm provides no added efficiency of the prediction for invasiveness of lung adenocarcinoma.
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http://dx.doi.org/10.1007/s00330-021-07948-0DOI Listing
May 2021

Can texture analysis based on single unenhanced CT accurately predict the WHO/ISUP grading of localized clear cell renal cell carcinoma?

Abdom Radiol (NY) 2021 Apr 28. Epub 2021 Apr 28.

Institute of Cancer and Basic Medicine, Chinese Academy of Sciences, No 1, Banshan East Road, Hangzhou, 310022, Zhejiang, China.

Objective: The purpose was to investigate the value of texture analysis in predicting the World Health Organization (WHO)/International Society of Urological Pathology (ISUP) grading of localized clear cell renal cell carcinoma (ccRCC) based on unenhanced CT (UECT).

Materials And Methods: Pathologically confirmed subjects (n = 104) with localized ccRCC who received UECT scanning were collected retrospectively for this study. All cases were classified into low grade (n = 53) and high grade (n = 51) according to the WHO/ISUP grading and were randomly divided into training set and test set as a ratio of 7:3. Using 3D-ROI segmentation on UECT images and extracted ninety-three texture features (first-order, gray-level co-occurrence matrix [GLCM], gray-level run length matrix [GLRLM], gray-level size zone matrix [GLSZM], neighboring gray tone difference matrix [NGTDM] and gray-level dependence matrix [GLDM] features). Univariate analysis and the least absolute shrinkage selection operator (LASSO) regression were used for feature dimension reduction, and logistic regression classifier was used to develop the prediction model. Using receiver operating characteristic (ROC) curve, bar chart and calibration curve to evaluate the performance of the prediction model.

Results: Dimension reduction screened out eight optimal texture features (maximum, median, dependence variance [DV], long run emphasis [LRE], run entropy [RE], gray-level non-uniformity [GLN], gray-level variance [GLV] and large area low gray-level emphasis [LALGLE]), and then the prediction model was developed according to the linear combination of these features. The accuracy, sensitivity, specificity, and AUC of the model in training set were 86.1%, 91.4%, 81.1%, and 0.937, respectively. The accuracy, sensitivity, specificity, and AUC of the model in test set were 81.2%, 81.2%, 81.2%, and 0.844, respectively. The calibration curves showed good calibration both in training set and test set (P > 0.05).

Conclusion: This study has demonstrated that the radiomics model based on UECT texture analysis could accurately evaluate the WHO/ISUP grading of localized ccRCC.
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http://dx.doi.org/10.1007/s00261-021-03090-zDOI Listing
April 2021

A radiomics approach for automated diagnosis of ovarian neoplasm malignancy in computed tomography.

Sci Rep 2021 Apr 22;11(1):8730. Epub 2021 Apr 22.

Department of Gynecology, Jiangxi Provincial People's Hospital Affiliated to Nanchang University, Nanchang, 330006, China.

This paper develops a two-dimensional (2D) radiomics approach with computed tomography (CT) to differentiate between benign and malignant ovarian neoplasms. A retrospective study was conducted from July 2017 to June 2019 for 134 patients with surgically-verified benign or malignant ovarian tumors. The patients were randomly divided in a ratio of 7:3 into two sets, namely a training set (of n = 95) and a test set (of n = 39). The ITK-SNAP software was used to delineate the regions of interest (ROI) associated with lesions of the largest diameters in plain CT image slices. Texture features were extracted by the Analysis Kit (AK) software. The training set was used to select the best features according to the maximum-relevance minimum-redundancy (mRMR) criterion, in addition to the algorithm of the least absolute shrinkage and selection operator (LASSO). Then, we employed a radiomics model for classification via multivariate logistic regression. Finally, we evaluated the overall performance of our method using the receiver operating characteristics (ROC), the DeLong test. and tested in an external validation test sample of patients of ovarian neoplasm. We created a radiomics prediction model from 14 selected features. The radiomic signature was found to be highly discriminative according to the area under the ROC curve (AUC) for both the training set (AUC = 0.88), and the test set (AUC = 0.87). The radiomics nomogram also demonstrated good calibration and differentiation for both the training (AUC = 0.95) and test (AUC = 0.96) samples. External validation tests gave a good performance in radiomic signature (AUC = 0.83) and radiomics nomogram (AUC = 0.95). The decision curve explicitly indicated the clinical usefulness of our nomogram method in the sense that it can influence major clinical events such as the ordering or abortion of other tests, treatments or invasive procedures. Our radiomics model based on plain CT images has a high diagnostic efficiency, which is helpful for the identification and prediction of benign and malignant ovarian neoplasms.
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http://dx.doi.org/10.1038/s41598-021-87775-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8062553PMC
April 2021

The Role of Preoperative Computed Tomography Radiomics in Distinguishing Benign and Malignant Tumors of the Parotid Gland.

Front Oncol 2021 10;11:634452. Epub 2021 Mar 10.

Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital of Hangzhou Medical College, Hangzhou, China.

Objective: This study aimed to develop and validate an integrated prediction model based on clinicoradiological data and computed tomography (CT)-radiomics for differentiating between benign and malignant parotid gland (PG) tumors multicentre cohorts.

Materials And Methods: A cohort of 87 PG tumor patients from hospital #1 who were diagnosed between January 2017 and January 2020 were used for prediction model training. A total of 378 radiomic features were extracted from a single tumor region of interest (ROI) of each patient on each phase of CT images. Imaging features were extracted from plain CT and contrast-enhanced CT (CECT) images. After dimensionality reduction, a radiomics signature was constructed. A combination model was constructed by incorporating the rad-score and CT radiological features. An independent group of 38 patients from hospital #2 was used to validate the prediction models. The model performances were evaluated by receiver operating characteristic (ROC) curve analysis, and decision curve analysis (DCA) was used to evaluate the clinical effectiveness of the models. The radiomics signature model was constructed and the rad-score was calculated based on selected imaging features from plain CT and CECT images.

Results: Analysis of variance and multivariable logistic regression analysis showed that location, lymph node metastases, and rad-score were independent predictors of tumor malignant status. The ROC curves showed that the accuracy of the support vector machine (SVM)-based prediction model, radiomics signature, location and lymph node status in the training set was 0.854, 0.772, 0.679, and 0.632, respectively; specificity was 0.869, 0.878, 0.734, and 0.773; and sensitivity was 0.731, 0.808, 0.723, and 0.742. In the test set, the accuracy was 0.835, 0.771, 0.653, and 0.608, respectively; the specificity was 0.741, 0.889, 0.852, and 0.812; and the sensitivity was 0.818, 0.790, 0.731, and 0.716.

Conclusions: The combination model based on the radiomics signature and CT radiological features is capable of evaluating the malignancy of PG tumors and can help clinicians guide clinical tumor management.
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http://dx.doi.org/10.3389/fonc.2021.634452DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7988088PMC
March 2021

Non-contrast CT-based radiomic signature for screening thoracic aortic dissections: a multicenter study.

Eur Radiol 2021 Mar 23. Epub 2021 Mar 23.

Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, 54 Youdian Road, Hangzhou, 310000, China.

Objective: To develop a non-contrast CT-based radiomic signature to effectively screen for thoracic aortic dissections (ADs).

Methods: We retrospectively enrolled 378 patients who underwent non-contrast chest CT scans along with CT angiography or MRI from 4 medical centers. The training and validation sets were from 3 centers, while the external test set was from a 4th center. Radiomic features were extracted from non-contrast CT images. The radiomic signature was created on the basis of selected features by a logistic regression algorithm. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve, accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were conducted to assess the predictive ability of radiomic signature.

Results: The radiomic signature demonstrated AUCs of 0.91 (95% confidence interval [CI], 0.86-0.95) in the training set, 0.92 (95% CI, 0.86-0.98) in the validation set, and 0.90 (95% CI, 0.82-0.98) in the external test set. The predicted diagnosis was in good agreement with the probability of thoracic AD. In the external test group, the diagnostic accuracy, sensitivity, specificity, PPV, and NPV were 90.5%, 85.7%, 91.7%, 70.6%, and 96.5%, respectively.

Conclusions: A radiomic signature based on non-contrast CT images can effectively predict thoracic ADs. This method may serve as a potential screening tool for thoracic ADs.

Key Points: • The non-contrast CT-based radiomic signature can effectively predict the thoracic aortic dissections. • This radiomic signature shows better predictive performance compared to the current clinical model. • This prediction method may be a potential tool for screening thoracic aortic dissections.
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http://dx.doi.org/10.1007/s00330-021-07768-2DOI Listing
March 2021

Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma Multi-Parametric MRI Radiomics.

Front Oncol 2021 3;11:633596. Epub 2021 Mar 3.

Department of Radiology, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, China.

Objectives: To systematically evaluate and compare the predictive capability for microvascular invasion (MVI) in hepatocellular carcinoma (HCC) patients based on radiomics from multi-parametric MRI (mp-MRI) including six sequences when used individually or combined, and to establish and validate the optimal combined model.

Methods: A total of 195 patients confirmed HCC were divided into training (n = 136) and validation (n = 59) datasets. All volumes of interest of tumors were respectively segmented on T-weighted imaging, diffusion-weighted imaging, apparent diffusion coefficient, artery phase, portal venous phase, and delay phase sequences, from which quantitative radiomics features were extracted and analyzed individually or combined. Multivariate logistic regression analyses were undertaken to construct clinical model, respective single-sequence radiomics models, fusion radiomics models based on different sequences and combined model. The accuracy, sensitivity, specificity and area under the receiver operating characteristic curve (AUC) were calculated to evaluate the performance of different models.

Results: Among nine radiomics models, the model from all sequences performed best with AUCs 0.889 and 0.822 in the training and validation datasets, respectively. The combined model incorporating radiomics from all sequences and effective clinical features achieved satisfactory preoperative prediction of MVI with AUCs 0.901 and 0.840, respectively, and could identify the higher risk population of MVI (P < 0.001). The Delong test manifested significant differences with P < 0.001 in the training dataset and P = 0.005 in the validation dataset between the combined model and clinical model.

Conclusions: The combined model can preoperatively and noninvasively predict MVI in HCC patients and may act as a usefully clinical tool to guide subsequent individualized treatment.
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http://dx.doi.org/10.3389/fonc.2021.633596DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7968223PMC
March 2021

MRI-Based Radiomics: Nomograms predicting the short-term response after transcatheter arterial chemoembolization (TACE) in hepatocellular carcinoma patients with diameter less than 5 cm.

Abdom Radiol (NY) 2021 Aug 13;46(8):3772-3789. Epub 2021 Mar 13.

Taizhou Hospital of Zhejiang Province affiliated to Wenzhou Medical University, No. 150 Ximen Street, Linhai, Zhejiang, China.

Purpose: To construct MRI radiomics nomograms that can predict short-term response after TACE in HCC patients with diameter less than 5 cm.

Methods: MRI images and clinical data of 153 cases with tumor diameter less than 5 cm before TACE from 3 hospitals were collected retrospectively and divided into 1 internal training set and 1 external validation set. The T2-weighted imaging (T2WI) and dynamic contrast-enhanced MRI arterial phase (DCE-MR AP) images were studied. Multivariable logistic regression was used to construct Radiomics models, Clinics models, and Nomograms based on T2WI and DCE-MR AP, respectively. The receiver characteristic curve (ROC) was used to evaluate the predictive performance of each model.

Results: In this study, 113 eligible cases in Hospital 1 were collected as the training set, and 40 eligible cases in other hospitals were used as the verification set. 11 T2WI features and 11 DCE-MRI AP features with the most predictive value were finally screened. 3 models based on T2WI and 3 models based on DCE-MRI AP were established, respectively. The area under curve (AUC) value of Nomogram based on T2WI of training set and validation set was 0.83 and 0.81, respectively. The AUC value of the models based on T2WI and models based on AP was almost equal, and Nomograms were the most effective models among all three types of models.

Conclusion: MRI-based Nomogram has greater predictive efficacy to predict the response after TACE than Radiomics and Clinics models alone, and the efficacy of T2WI-based models and DCE-MRI AP-based models was almost equal.
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http://dx.doi.org/10.1007/s00261-021-02992-2DOI Listing
August 2021

A convention-radiomics CT nomogram for differentiating fat-poor angiomyolipoma from clear cell renal cell carcinoma.

Sci Rep 2021 Feb 25;11(1):4644. Epub 2021 Feb 25.

GE Healthcare, Hangzhou, 310000, China.

This study aimed to construct convention-radiomics CT nomogram containing conventional CT characteristics and radiomics signature for distinguishing fat-poor angiomyolipoma (fp-AML) from clear-cell renal cell carcinoma (ccRCC). 29 fp-AML and 110 ccRCC patients were enrolled and underwent CT examinations in this study. The radiomics-only logistic model was constructed with selected radiomics features by the analysis of variance (ANOVA)/Mann-Whitney (MW), correlation analysis, and Least Absolute Shrinkage and Selection Operator (LASSO), and the radiomics score (rad-score) was computed. The convention-radiomics logistic model based on independent conventional CT risk factors and rad-score was constructed for differentiating. Then the relevant nomogram was developed. Receiver operation characteristic (ROC) curves were calculated to quantify the accuracy for distinguishing. The rad-score of ccRCC was smaller than that of fp-AML. The convention-radioimics logistic model was constructed containing variables of enhancement pattern, V, and rad-score. To the entire cohort, the area under the curve (AUC) of convention-radiomics model (0.968 [95% CI 0.923-0.990]) was higher than that of radiomics-only model (0.958 [95% CI 0.910-0.985]). Our study indicated that convention-radiomics CT nomogram including conventional CT risk factors and radiomics signature exhibited better performance in distinguishing fp-AML from ccRCC.
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http://dx.doi.org/10.1038/s41598-021-84244-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7907210PMC
February 2021

A CT-Based Tumoral and Mini-Peritumoral Radiomics Approach: Differentiate Fat-Poor Angiomyolipoma from Clear Cell Renal Cell Carcinoma.

Cancer Manag Res 2021 12;13:1417-1425. Epub 2021 Feb 12.

Department of Radiology, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, 310000, People's Republic of China.

Objective: This study aimed to evaluate the role of tumor and mini-peritumor in the context of CT-based radiomics analysis to differentiate fat-poor angiomyolipoma (fp-AML) from clear cell renal cell carcinoma (ccRCC).

Methods: A total of 58 fp-AMLs and 172 ccRCCs were enrolled. The volume of interest (VOI) was manually delineated in the standardized CT images and radiomics features were automatically calculated with software. After methods of feature selection, the CT-based logistic models including tumoral model (Ra-tumor), mini-peritumoral model (Ra-peritumor), perirenal model (Ra-Pr), perifat model (Ra-Pf), and tumoral+perirenal model (Ra-tumor+Pr) were constructed. The area under curves (AUCs) were calculated by DeLong test to evaluate the efficiency of logistic models.

Results: The AUCs of Ra-peritumor of nephrographic phase (NP) were slightly higher than those of corticomedullary phase (CMP). Furthermore, the Ra-Pr showed significant higher efficiency than the Ra-Pf, and relative more optimal radiomics features were selected in the Ra-Pr than Ra-Pf. The Ra-tumor+Pr combined tumoral and perirenal radiomics analysis was of most significant in distinction compared with Ra-tumor and Ra-peritumor.

Conclusion: The validity of NP to differentiate fp-AML from ccRCC was slightly higher than that of CMP. To the NP analysis, the Ra-Pr was superior to the Ra-Pf in distinction, and the lesions invaded to the perirenal tissue more severely than to the perifat tissue. It is important to the individual therapeutic surgeries according to the different lesion location. The pooled tumoral and perirenal radiomics analysis was the most promising approach in distinguishing fp-AML and ccRCC.
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http://dx.doi.org/10.2147/CMAR.S297094DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7886092PMC
February 2021

The Nomogram of MRI-based Radiomics with Complementary Visual Features by Machine Learning Improves Stratification of Glioblastoma Patients: A Multicenter Study.

J Magn Reson Imaging 2021 08 8;54(2):571-583. Epub 2021 Feb 8.

Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital of Hangzhou Medical College, Hangzhou, China.

Background: Glioblastomas (GBMs) represent both the most common and the most highly malignant primary brain tumors. The subjective visual imaging features from MRI make it challenging to predict the overall survival (OS) of GBM. Radiomics can quantify image features objectively as an emerging technique. A pragmatic and objective method in the clinic to assess OS is strongly in need.

Purpose: To construct a radiomics nomogram to stratify GBM patients into long- vs. short-term survival.

Study Type: Retrospective.

Population: One-hundred and fifty-eight GBM patients from Brain Tumor Segmentation Challenge 2018 (BRATS2018) were for model construction and 32 GBM patients from the local hospital for external validation.

Field Strength/sequence: 1.5 T and 3.0 T MRI Scanners, T WI, T WI, T FLAIR, and contrast-enhanced T WI sequences ASSESSMENT: All patients were divided into long-term or short-term based on a survival of greater or fewer than 12 months. All BRATS2018 subjects were divided into training and test sets, and images were assessed for ependymal and pia mater involvement (EPI) and multifocality by three experienced neuroradiologists. All tumor tissues from multiparametric MRI were fully automatically segmented into three subregions to calculate the radiomic features. Based on the training set, the most powerful radiomic features were selected to constitute radiomic signature.

Statistical Tests: Receiver operating characteristic (ROC) curve, sensitivity, specificity, and the Hosmer-Lemeshow test.

Results: The nomogram had a survival prediction accuracy of 0.878 and 0.875, a specificity of 0.875 and 0.583, and a sensitivity of 0.704 and 0.833, respectively, in the training and test set. The ROC curve showed the accuracy of the nomogram, radiomic signature, age, and EPI for external validation set were 0.858, 0.826, 0.664, and 0.66 in the validate set, respectively.

Data Conclusion: Radiomics nomogram integrated with radiomic signature, EPI, and age was found to be robust for the stratification of GBM patients into long- vs. short-term survival.

Level Of Evidence: 3 TECHNICAL EFFICACY STAGE: 2.
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http://dx.doi.org/10.1002/jmri.27536DOI Listing
August 2021

Comprehensive classification models based on amygdala radiomic features for Alzheimer's disease and mild cognitive impairment.

Brain Imaging Behav 2021 Feb 4. Epub 2021 Feb 4.

Department of Radiology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China.

The amygdala is an important part of the medial temporal lobe and plays a pivotal role in the emotional and cognitive function. The aim of this study was to build and validate comprehensive classification models based on amygdala radiomic features for Alzheimer's disease (AD) and amnestic mild cognitive impairment (aMCI). For the amygdala, 3360 radiomic features were extracted from 97 AD patients, 53 aMCI patients and 45 normal controls (NCs) on the three-dimensional T1-weighted magnetization-prepared rapid gradient echo (MPRAGE) images. We used maximum relevance and minimum redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) to select the features. Multivariable logistic regression analysis was performed to build three classification models (AD-NC group, AD-aMCI group, and aMCI-NC group). Finally, internal validation was assessed. After two steps of feature selection, there were 5 radiomic features remained in the AD-NC group, 16 features remained in the AD-aMCI group and the aMCI-NC group, respectively. The proposed logistic classification analysis based on amygdala radiomic features achieves an accuracy of 0.90 and an area under the ROC curve (AUC) of 0.93 for AD vs. NC classification, an accuracy of 0.81 and an AUC of 0.84 for AD vs. aMCI classification, and an accuracy of 0.75 and an AUC of 0.80 for aMCI vs. NC classification. Amygdala radiomic features might be early biomarkers for detecting microstructural brain tissue changes during the AD and aMCI course. Logistic classification analysis demonstrated the promising classification performances for clinical applications among AD, aMCI and NC groups.
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http://dx.doi.org/10.1007/s11682-020-00434-zDOI Listing
February 2021

An Integrative Nomogram for Identifying Early-Stage Parkinson's Disease Using Non-motor Symptoms and White Matter-Based Radiomics Biomarkers From Whole-Brain MRI.

Front Aging Neurosci 2020 17;12:548616. Epub 2020 Dec 17.

Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.

To develop and validate an integrative nomogram based on white matter (WM) radiomics biomarkers and nonmotor symptoms for the identification of early-stage Parkinson's disease (PD). The brain magnetic resonance imaging (MRI) and clinical characteristics of 336 subjects, including 168 patients with PD, were collected from the Parkinson's Progress Markers Initiative (PPMI) database. All subjects were randomly divided into training and test sets. According to the baseline MRI scans of patients in the training set, the WM was segmented to extract the radiomic features of each patient and develop radiomics biomarkers, which were then combined with nonmotor symptoms to build an integrative nomogram using machine learning. Finally, the diagnostic accuracy and reliability of the nomogram were evaluated using a receiver operating characteristic curve and test data, respectively. In addition, we investigated 58 patients with atypical PD who had imaging scans without evidence of dopaminergic deficit (SWEDD) to verify whether the nomogram was able to distinguish patients with typical PD from patients with SWEDD. A decision curve analysis was also performed to validate the clinical practicality of the nomogram. The area under the curve values of the integrative nomogram for the training, testing and verification sets were 0.937, 0.922, and 0.836, respectively; the specificity values were 83.8, 88.2, and 91.38%, respectively; and the sensitivity values were 84.6, 82.4, and 70.69%, respectively. A significant difference in the number of patients with PD was observed between the high-risk group and the low-risk group based on the nomogram ( < 0.05). This integrative nomogram is a new potential method to identify patients with early-stage PD.
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http://dx.doi.org/10.3389/fnagi.2020.548616DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7773758PMC
December 2020

Can computed tomography-based radiomics potentially discriminate between anterior mediastinal cysts and type B1 and B2 thymomas?

Biomed Eng Online 2020 Nov 27;19(1):89. Epub 2020 Nov 27.

Institute of Cancer and Basic Medicine (ICBM), Chinese Academy of Sciences, Hangzhou, China.

Background: Anterior mediastinal cysts (AMC) are often misdiagnosed as thymomas and undergo surgical resection, which caused unnecessary treatment and medical resource waste. The purpose of this study is to explore potential possibility of computed tomography (CT)-based radiomics for the diagnosis of AMC and type B1 and B2 thymomas.

Methods: A group of 188 patients with pathologically confirmed AMC (106 cases misdiagnosed as thymomas in CT) and thymomas (82 cases) and underwent routine chest CT from January 2010 to December 2018 were retrospectively analyzed. The lesions were manually delineated using ITK-SNAP software, and radiomics features were performed using the artificial intelligence kit (AK) software. A total of 180 tumour texture features were extracted from enhanced CT and unenhanced CT, respectively. The general test, correlation analysis, and LASSO were used to features selection and then the radiomics signature (radscore) was obtained. The combined model including radscore and independent clinical factors was developed. The model performances were evaluated on discrimination, calibration curve.

Results: Two radscore models were constructed from the unenhanced and enhanced phases based on the selected four and three features, respectively. The AUC, sensitivity, and specificity of the enhanced radscore model were 0.928, 89.3%, and 83.8% in the training dataset and 0.899, 84.6%, and 87.5% in the test dataset (higher than the unenhanced radscore model). The combined model of enhanced CT including radiomics features and independent clinical factors yielded an AUC, sensitivity and specificity of 0.941, 82.1%, and 94.6% in the training dataset and 0.938, 92.3%, and 87.5% in the test dataset (higher than the unenhanced combined model and enhanced radscore model).

Conclusions: The study suggested the possibility that the combined model in enhanced CT provided a potential tool to facilitate the differential diagnosis of AMC and type B1 and B2 thymomas.
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http://dx.doi.org/10.1186/s12938-020-00833-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7694435PMC
November 2020

The Growth Trend Predictions in Pulmonary Ground Glass Nodules Based on Radiomic CT Features.

Front Oncol 2020 20;10:580809. Epub 2020 Oct 20.

Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Traditional Chinese Medicine), Hangzhou, China.

The management of ground glass nodules (GGNs) remains a distinctive challenge. This study is aimed at comparing the predictive growth trends of radiomic features against current clinical features for the evaluation of GGNs. A total of 110 GGNs in 85 patients were included in this retrospective study, in which follow up occurred over a span ≥2 years. A total of 396 radiomic features were manually segmented by radiologists and quantitatively analyzed using an Analysis Kit software. After feature selection, three models were developed to predict the growth of GGNs. The performance of all three models was evaluated by a receiver operating characteristic (ROC) curve. The best performing model was also assessed by calibration and clinical utility. After using a stepwise multivariate logistic regression analysis and dimensionality reduction, the diameter and five specific radiomic features were included in the clinical model and the radiomic model. The rad-score [odds ratio (OR) = 5.130; < 0.01] and diameter (OR = 1.087; < 0.05) were both considered as predictive indicators for the growth of GGNs. Meanwhile, the area under the ROC curve of the combined model reached 0.801. The high degree of fitting and favorable clinical utility was detected using the calibration curve with the Hosmer-Lemeshow test and the decision curve analysis was utilized for the nomogram. A combined model using the current clinical features alongside the radiomic features can serve as a powerful tool to assist clinicians in guiding the management of GGNs.
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http://dx.doi.org/10.3389/fonc.2020.580809DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7606974PMC
October 2020

Radiomics Analysis and Correlation With Metabolic Parameters in Nasopharyngeal Carcinoma Based on PET/MR Imaging.

Front Oncol 2020 8;10:1619. Epub 2020 Sep 8.

Department of Radiology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China.

Accurate staging is of great importance in treatment selection for patients with nasopharyngeal carcinoma (NPC). The aims of this study were to construct radiomic models of NPC staging based on positron emission tomography (PET) and magnetic resonance (MR) images and to investigate the correlation between metabolic parameters and radiomic features. A total of 100 consecutive cases of NPC (70 in training and 30 in the testing cohort) with undifferentiated carcinoma confirmed pathologically were recruited. Metabolic parameters of the local lesions of NPC were measured. A total of 396 radiomic features based on PET and MRI images were calculated [including histogram, Haralick, shape factor, gray level co-occurrence matrix (GLCM), and run length matrix (RLM)] and selected [using maximum relevance and minimum redundancy (mRMR) and least shrinkage and selection operator (LASSO)], respectively. The logistic regression models were established according to these features. Finally, the relationship between the metabolic parameters and radiomic features was analyzed. We selected the nine most relevant radiomic features (six from MR images and three from PET images) from local NPC lesions. In the PET model, the area under the receiver operating characteristic (ROC) curve (AUC), accuracy, sensitivity, and the specificity of the training group were 0.84, 0.75, 0.90, and 0.69, respectively. In the MR model, those metrics were 0.85, 0.83, 0.75, and 0.86, respectively. Pearson's correlation analysis showed that the metabolic parameters had different degrees of correlation with the selected radiomic features. The PET and MR radiomic models were helpful in the diagnosis of NPC staging. There were correlations between the metabolic parameters and radiomic features of primary NPC based on PET/MR. In the future, PET/MR-based radiomic models, with further improvement and validation, can be a more useful and economical tool for predicting local invasion and distant metastasis of NPC.
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http://dx.doi.org/10.3389/fonc.2020.01619DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7506153PMC
September 2020

Pretreatment MR-based radiomics nomogram as potential imaging biomarker for individualized assessment of perineural invasion status in rectal cancer.

Abdom Radiol (NY) 2021 03 1;46(3):847-857. Epub 2020 Sep 1.

Department of Radiology, Fujian Cancer Hospital & Fujian Medical University Cancer Hospital, Fuzhou, 350014, Fujian, China.

Purpose: To investigate whether pretreatment magnetic resonance (MR)-based radiomics nomogram can individualize prediction of perineural invasion (PNI) status in rectal cancer (RC).

Material And Methods: A total of 122 RC patients with pathologically confirmed were classified as training cohort (n = 87) and test cohort (n = 35). 180 radiomics features were extracted from all lesions based on oblique axial TWI TSE images. The dimensionality reduction and feature selection in training cohort were realized by the maximum relevance minimum redundancy (mRMR) algorithm and the least absolute shrinkage and selection operator (LASSO) regression model. A predictive model combining radiomics features and clinical risk factors (pathological N stage, pathological LVI status) was established by multivariate logistic regression analysis. The performance of the model was assessed based on its receiver operating characteristic (ROC) curve, nomogram, and calibration.

Results: The developed radiomics nomogram that integrated the radiomics signature and clinical risk factors could provide discrimination in the training and test cohorts. The accuracy and the area under the curve (AUC) for assessing PNI status were 0.82, 0.86, respectively, in the training cohort, while they were 0.71 and 0.85 in the test cohort. The goodness-of-fit of the nomogram was evaluated using the Hosmer-Lemeshow test (p = 0.52 in training cohort and p = 0.24 in test cohort). Decision curve analysis (DCA) showed that the radiomics nomogram was clinically useful.

Conclusion: The developed radiomics nomogram might be helpful in the individualized assessment PNI status in patients with RC. This stratification of RC patients according to their PNI status may provide the basis for individualized adjuvant therapy, especially for stage II patients.
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http://dx.doi.org/10.1007/s00261-020-02710-4DOI Listing
March 2021

Radiomics predict postoperative survival of patients with primary liver cancer with different pathological types.

Ann Transl Med 2020 Jul;8(13):820

Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.

Background: Radiomics can be used to determine the prognosis of liver cancer, but it might vary among cancer types. This study aimed to explore the clinicopathological features, radiomics, and survival of patients with hepatocellular carcinoma (HCC), mass-type cholangiocarcinoma (MCC), and combined hepatocellular-cholangiocarcinoma (CHCC).

Methods: This was a retrospective cohort study of patients with primary liver cancer operated at the department of hepatobiliary surgery of the First Affiliated Hospital of Zhejiang University from 07/2013 to 11/2015. All patients underwent preoperative liver enhanced MRI scans and diffusion-weighted imaging (DWI). The radiomics characteristics of DWI and the enhanced equilibrium phase (EP) images were extracted. The mRMR (minimum redundancy maximum relevance) was applied to filter the parameters.

Results: There were 44 patients with MCC, 59 with HCC, and 33 with CHCC. Macrovascular invasion, tumor diameter, positive ferritin preoperatively, positive AFP preoperatively, positive CEA preoperatively, Correlation, Inverse Difference Moment, and Cluster Prominence in model A (DWI and clinicopathological parameters) were independently associated with overall survival (OS) (P<0.05). Lymphadenopathy, gender, positive ferritin preoperatively, positive AFP preoperatively, positive CEA preoperatively, Uniformity, and Cluster Prominence in model B (EP and clinicopathological parameters) were independently associated with OS (P<0.05). Macrovascular invasion, lymphadenopathy, gender, positive ferritin preoperatively, positive CEA preoperatively, Uniformity_EP, GLCMEnergy_DWI, and Cluster Prominence_EP in model C (image texture and clinicopathological parameters) were independently associated with OS (P<0.05). Those factors were used to construct three nomograms to predict OS.

Conclusions: Clinicopathological and radiomics features are independently associated with the OS of patients with primary liver cancer.
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http://dx.doi.org/10.21037/atm-19-4668DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7396247PMC
July 2020

Risk stratification of thymic epithelial tumors by using a nomogram combined with radiomic features and TNM staging.

Eur Radiol 2021 Jan 5;31(1):423-435. Epub 2020 Aug 5.

Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 25 New Chardon St., 400C, Boston, MA, 02114, USA.

Objectives: To construct and validate a nomogram model that integrated the CT radiomic features and the TNM staging for risk stratification of thymic epithelial tumors (TETs).

Methods: A total of 136 patients with pathology-confirmed TETs who underwent CT examination were collected from two institutions. According to the WHO pathological classification criteria, patients were classified into low-risk and high-risk groups. The TNM staging was determined in terms of the 8th edition AJCC/UICC staging criteria. LASSO regression was performed to extract the optimal features correlated to risk stratification among the 704 radiomic features calculated. A nomogram model was constructed by combining the Radscore and the TNM staging. The clinical performance was evaluated by ROC analysis, calibration curve, and decision curve analysis (DCA). The Kaplan-Meier (KM) analysis was employed for survival analysis.

Results: Five optimal features identified by LASSO regression were employed to calculate the Radscore correlated to risk stratification. The nomogram model showed a better performance in both training cohort (AUC = 0.84, 95%CI 0.75-0.91) and external validation cohort (AUC = 0.79, 95%CI 0.69-0.88). The calibration curve and DCA analysis indicated a better accuracy of the nomogram model for risk stratification than either Radscore or the TNM staging alone. The KM analysis showed a significant difference between the two groups stratified by the nomogram model (p = 0.02).

Conclusions: A nomogram model that integrated the radiomic signatures and the TNM staging could serve as a reliable model of risk stratification in predicting the prognosis of patients with TETs.

Key Points: • The radiomic features could be associated with the TET pathophysiology. • TNM staging and Radscore could independently stratify the risk of TETs. • The nomogram model is more objective and more comprehensive than previous methods.
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http://dx.doi.org/10.1007/s00330-020-07100-4DOI Listing
January 2021

Development of a Novel, Multi-Parametric, MRI-Based Radiomic Nomogram for Differentiating Between Clinically Significant and Insignificant Prostate Cancer.

Front Oncol 2020 30;10:888. Epub 2020 Jun 30.

The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China.

To develop and validate a predictive model for discriminating clinically significant prostate cancer (csPCa) from clinically insignificant prostate cancer (ciPCa). This retrospective study was performed with 159 consecutively enrolled pathologically confirmed PCa patients from two medical centers. The dataset was allocated to a training group ( = 54) and an internal validation group ( = 22) from one center along with an external independent validation group ( = 83) from another center. A total of 1,188 radiomic features were extracted from T2WI, diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) images derived from DWI for each patient. Multivariable logistic regression analysis was performed to develop the model, incorporating the radiomic signature, ADC value, and independent clinical risk factors. This was presented using a radiomic nomogram. The receiver operating characteristic (ROC) curve was utilized to assess the predictive efficacy of the radiomic nomogram in both the training and validation groups. The decision curve analysis was used to evaluate which model achieved the most net benefit. The radiomic signature, which was made up of 10 selected features, was significantly associated with csPCa ( < 0.001 for both training and internal validation groups). The area under the curve (AUC) values of discriminating csPCa for the radiomics signature were 0.95 (training group), 0.86 (internal validation group), and 0.81 (external validation group). Multivariate logistic analysis identified the radiomic signature and ADC value as independent parameters of predicting csPCa. Then, the combination nomogram incorporating the radiomic signature and ADC value demonstrated a favorable classification capability with the AUC of 0.95 (training group), 0.93 (internal validation group), and 0.84 (external validation group). Appreciable clinical utility of this model was illustrated using the decision curve analysis for the nomogram. The nomogram, incorporating radiomic signature and ADC value, provided an individualized, potential approach for discriminating csPCa from ciPCa.
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http://dx.doi.org/10.3389/fonc.2020.00888DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7339043PMC
June 2020

Non-invasive evaluation for benign and malignant subcentimeter pulmonary ground-glass nodules (≤1 cm) based on CT texture analysis.

Br J Radiol 2020 Oct 20;93(1114):20190762. Epub 2020 Jul 20.

Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, the Fifth Affiliated Hospital of Wenzhou Medical University /Affiliated Lishui Hospital of Zhejiang University/ The Central Hospital of Zhejiang Lishui, Lishui 323000, China.

Objectives: To investigate potential diagnostic model for predicting benign or malignant status of subcentimeter pulmonary ground-glass nodules (SPGGNs) (≤1 cm) based on CT texture analysis.

Methods: A total of 89 SPGGNs from 89 patients were included; 51 patients were diagnosed with adenocarcinoma, and 38 were diagnosed with inflamed or infected benign SPGGNs. Analysis Kit software was used to manually delineate the volume of interest of lesions and extract a total of 396 quantitative texture parameters. The statistical analysis was performed using R software. The SPGGNs were randomly divided into a training set ( = 59) and a validation set ( = 30). All pre-normalized (Z-score) feature values were subjected to dimension reduction using the LASSO algorithm,and the most useful features in the training set were selected. The selected imaging features were then combined into a Rad-score, which was further assessed by ROC curve analysis in the training and validation sets.

Results: Four characteristic parameters (ClusterShade_AllDirection_offset4_SD, ShortRunEmphasis_angle45_offset1, Maximum3DDiameter, SurfaceVolumeRatio) were further selected by LASSO ( < 0.05). As a cluster of imaging biomarkers, the above four parameters were used to form the Rad-score. The AUC for differentiating between benign and malignant SPGGNs in the training set was 0.792 (95% CI: 0.671, 0.913), and the sensitivity and specificity were 86.10 and 65.20%, respectively. The AUC in the validation set was 72.9% (95% CI: 0.545, 0.913), and the sensitivity and specificity were 86.70 and 60%, respectively.

Conclusion: The present diagnostic model based on the cluster of imaging biomarkers can preferably distinguish benign and malignant SPGGNs (≤1 cm).

Advances In Knowledge: Texture analysis based on CT images provide a new and credible technique for accurate identification of subcentimeter pulmonary ground-glass nodules.
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http://dx.doi.org/10.1259/bjr.20190762DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7548366PMC
October 2020

A Nomogram Model of Radiomics and Satellite Sign Number as Imaging Predictor for Intracranial Hematoma Expansion.

Front Neurosci 2020 4;14:491. Epub 2020 Jun 4.

Department of Radiology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China.

Background: We aimed to construct and validate a nomogram model based on the combination of radiomic features and satellite sign number for predicting intracerebral hematoma expansion.

Methods: A total of 129 patients from two institutions were enrolled in this study. The preprocessed initial CT images were used for radiomic feature extraction. The ANOVA-Kruskal-Wallis test and least absolute shrinkage and selection operator regression were applied to identify candidate radiomic features and construct the Radscore. A nomogram model was developed by integrating the Radscore with a satellite sign number. The discrimination performance of the proposed model was evaluated by receiver operating characteristic (ROC) analysis, and the predictive accuracy was assessed a calibration curve. Decision curve analysis (DCA) and Kaplan-Meier (KM) survival analysis were performed to evaluate the clinical value of the model.

Results: Four optimal features were ultimately selected and contributed to the Radscore construction. A positive correlation was observed between the satellite sign number and Radscore (Pearson's : 0.451). The nomogram model showed the best performance with high area under the curves in both training cohort (0.881, sensitivity: 0.973; specificity: 0.787) and external validation cohort (0.857, sensitivity: 0.950; specificity: 0.766). The calibration curve, DCA, and KM analysis indicated the high accuracy and clinical usefulness of the nomogram model for hematoma expansion prediction.

Conclusion: A nomogram model of integrated radiomic signature and satellite sign number based on noncontrast CT images could serve as a reliable and convenient measurement of hematoma expansion prediction.
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http://dx.doi.org/10.3389/fnins.2020.00491DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7287169PMC
June 2020

Differentiating pneumonia with and without COVID-19 using chest CT images: from qualitative to quantitative.

J Xray Sci Technol 2020 ;28(4):583-589

Department of Radiology, Jiangxi Provincial People's Hospital, Nanchang, China.

Background: Pneumonia caused by COVID-19 shares overlapping imaging manifestations with other types of pneumonia. How to objectively and quantitatively differentiate pneumonia patients with and without COVID-19 virus remains clinical challenge.

Objective: To formulate standardized scoring criteria and an objective quantization standard to guide decision making in detection and diagnosis of COVID-19 virus induced pneumonia in clinical practice.

Methods: A retrospective dataset includes computed tomography (CT) images acquired from 43 pneumonia patients with COVID-19 virus detected by reverse transcription-polymerase chain reaction (RT-PCR) tests and 49 pneumonia patients without COVID-19 virus. All patients were treated during the same time period in two hospitals. Key indicators of differential diagnosis were identified in relevant literature and the scores were quantified namely, patients with more than 8 points were identified as high risk, those with 6-8 points as moderate risk, and those with fewer than 6 points as low risk for COVID-19 virus. In the study, 3 radiologists determined the scores for all patients. Diagnostic sensitivity and specificity were subsequently calculated.

Results: A total of 61 patients were determined as high risk, among which 42 were COVID-19 positive by RT-PCR tests. Next, 9 were identified as moderate risk, one of whom was COVID-19 positive. Last, 22 were classified into the low-risk group, all of them are COVID-19 negative. Based on these results, the sensitivity of detection COVID-19 positive cases between the high-risk group and the non-high-risk group was 0.98 with 95% confidence interval [0.88, 1.00], and the specificity was 0.61 [0.46, 0.75]. The detection sensitivity between the moderate-/high-risk group and the low-risk group was 1.00 [0.92, 1.00], and the specificity was 0.45 [0.31, 0.60].

Conclusion: The proposed quantitative scoring criteria showed high sensitivity and moderate specificity in detecting COVID-19 using CT images, which indicates that these criteria may be beneficial for screening in real-world practice and helpful for long-term disease control.
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http://dx.doi.org/10.3233/XST-200689DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7505000PMC
August 2020
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