Publications by authors named "Yajia Gu"

54 Publications

Radiomics of Tumor Heterogeneity in Longitudinal Dynamic Contrast-Enhanced Magnetic Resonance Imaging for Predicting Response to Neoadjuvant Chemotherapy in Breast Cancer.

Front Mol Biosci 2021 22;8:622219. Epub 2021 Mar 22.

Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou, China.

Breast tumor morphological and vascular characteristics can be changed during neoadjuvant chemotherapy (NACT). The early changes in tumor heterogeneity can be quantitatively modeled by longitudinal dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), which is useful in predicting responses to NACT in breast cancer. In this retrospective analysis, 114 female patients with unilateral unifocal primary breast cancer who received NACT were included in a development ( = 61) dataset and a testing dataset ( = 53). DCE-MRI was performed for each patient before and after treatment (two cycles of NACT) to generate baseline and early follow-up images, respectively. Feature-level changes (delta) of the entire tumor were evaluated by calculating the relative net feature change (deltaRAD) between baseline and follow-up images. The voxel-level change inside the tumor was evaluated, which yielded a Jacobian map by registering the follow-up image to the baseline image. Clinical information and the radiomic features were fused to enhance the predictive performance. The area under the curve (AUC) values were assessed to evaluate the prediction performance. Predictive models using radiomics based on pre- and post-treatment images, Jacobian maps and deltaRAD showed AUC values of 0.568, 0.767, 0.630 and 0.726, respectively. When features from these images were fused, the predictive model generated an AUC value of 0.771. After adding the molecular subtype information in the fused model, the performance was increased to an AUC of 0.809 (sensitivity of 0.826 and specificity of 0.800), which is significantly higher than that of the baseline imaging- and Jacobian map-based predictive models ( = 0.028 and 0.019, respectively). The level of tumor heterogeneity reduction (evaluated by texture feature) is higher in the NACT responders than in the nonresponders. The results suggested that changes in DCE-MRI features that reflect a reduction in tumor heterogeneity following NACT could provide early prediction of breast tumor response. The prediction was improved when the molecular subtype information was combined into the model.
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http://dx.doi.org/10.3389/fmolb.2021.622219DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8044916PMC
March 2021

Noninvasive prediction of residual disease for advanced high-grade serous ovarian carcinoma by MRI-based radiomic-clinical nomogram.

Eur Radiol 2021 Apr 16. Epub 2021 Apr 16.

Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China.

Objectives: To develop a preoperative MRI-based radiomic-clinical nomogram for prediction of residual disease (RD) in patients with advanced high-grade serous ovarian carcinoma (HGSOC).

Methods: In total, 217 patients with advanced HGSOC were enrolled from January 2014 to June 2019 and randomly divided into a training set (n = 160) and a validation set (n = 57). Finally, 841 radiomic features were extracted from each tumor on T2-weighted imaging (T2WI) and contrast-enhanced T1-weighted imaging (CE-T1WI) sequence, respectively. We used two fusion methods, the maximal volume of interest (MV) and the maximal feature value (MF), to fuse the radiomic features of bilateral tumors, so that patients with bilateral tumors have the same kind of radiomic features as patients with unilateral tumors. The radiomic signatures were constructed by using mRMR method and LASSO classifier. Multivariable logistic regression analysis was used to develop a radiomic-clinical nomogram incorporating radiomic signature and conventional clinico-radiological features. The performance of the nomogram was evaluated on the validation set.

Results: In total, 342 tumors from 217 patients were analyzed in this study. The MF-based radiomic signature showed significantly better prediction performance than the MV-based radiomic signature (AUC = 0.744 vs. 0.650, p = 0.047). By incorporating clinico-radiological features and MF-based radiomic signature, radiomic-clinical nomogram showed favorable prediction ability with an AUC of 0.803 in the validation set, which was significantly higher than that of clinico-radiological signature and MF-based radiomic signature (AUC = 0.623, 0.744, respectively).

Conclusions: The proposed MRI-based radiomic-clinical nomogram provides a promising way to noninvasively predict the RD status.

Key Points: • MRI-based radiomic-clinical nomogram is feasible to noninvasively predict residual disease in patients with advanced HGSOC. • The radiomic signature based on MF showed significantly better prediction performance than that based on MV. • The radiomic-clinical nomogram showed a favorable prediction ability with an AUC of 0.803.
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http://dx.doi.org/10.1007/s00330-021-07902-0DOI Listing
April 2021

Histogram analysis of quantitative parameters from synthetic MRI: Correlations with prognostic factors and molecular subtypes in invasive ductal breast cancer.

Eur J Radiol 2021 Apr 8;139:109697. Epub 2021 Apr 8.

Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China. Electronic address:

Purpose: To evaluate intra-tumoral heterogeneity through a histogram analysis of quantitative parameters obtained from synthetic MRI (magnetic resonance imaging), and determine correlations of these histogram characteristics with prognostic factors and molecular subtypes of invasive ductal carcinoma (IDC).

Methods: A total of 122 IDC from 122 women who underwent preoperative synthetic MRI and DCE (dynamic contrast enhancement)-MRI were investigated. The synthetic MRI parameters (T1, T2, and PD (proton density)) were obtained. For each parameter, the minimum, 10th percentile, mean, median, 90th percentile, maximum, skewness, and kurtosis values of tumor were calculated, and correlations with prognostic factors and subtypes were assessed. The Mann-Whitney U test or the Students t test were utilized to analyze the association between the histogram features of synthetic MRI parameters and prognostic factors. The Kruskal-Wallis test followed by the post-hoc test was used to analyze differences of synthetic MRI parameters among molecular subtypes.

Results: IDC with high histopathologic grade showed statistically higher PD, T1 and T1 values than those with low grade (p = 0.003, p = 0.007, p = 0.003). The T1 were significantly higher in cancers with PR (progesterone receptor) negativity than those with PR positivity (p = 0.005). ER-negative cancers had significant higher values of T2, T2, and T2 than ER-positive cancers (p = 0.006, 0.002, and 0.006, respectively). The values of PD were significantly higher in IDC with HER2 (human epidermal growth factor receptor 2) positivity than those with HER2 negativity (p = 0.001). When discriminating molecular subtypes of IDC, the T2 achieved the highest performance. The T2 values of TN (triple-negative), luminal B and luminal A types are arranged in descending order (p <  0.0001).

Conclusions: Histogram features derived from synthetic MRI quantifies the distributions of tissue relaxation time and proton density, and may serve as a potential biomarker for discriminating histopathological grade, hormone receptor status, HER2 expression status and breast cancer subtypes.
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http://dx.doi.org/10.1016/j.ejrad.2021.109697DOI Listing
April 2021

Whole-Tumor Histogram and Texture Imaging Features on Magnetic Resonance Imaging Combined With Epstein-Barr Virus Status to Predict Disease Progression in Patients With Nasopharyngeal Carcinoma.

Front Oncol 2021 9;11:610804. Epub 2021 Mar 9.

Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China.

We aimed to investigate whether Epstein-Barr virus (EBV) could produce differences on MRI by examining the histogram and texture imaging features. We also sought to determine the predictive value of pretreatment MRI texture analyses incorporating with EBV status for disease progression (PD) in patients with primary nasopharyngeal carcinoma (NPC). Eighty-one patients with primary T2-T4 NPC and known EBV status who underwent contrast-enhanced MRI were included in this retrospective study. Whole-tumor-based histogram and texture features were extracted from pretreatment T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and contrast-enhanced (CE)-T1WI images. Mann-Whitney -tests were performed to identify the differences in histogram and texture parameters between EBV DNA-positive and EBV DNA-negative NPC images. The effects of clinical variables as well as histogram and texture features were estimated by using univariate and multivariate logistic regression analyses. Receiver operating characteristic (ROC) curve analysis was used to predict the EBV status and PD. Finally, an integrated model with the best performance was built. Of the 81 patients included, 54 had EBV DNA-positive NPC, and 27 had EBV DNA-negative NPC. Patients who were tested EBV DNA-positive had higher overall stage ( = 0.016), more lymphatic metastases ( < 0.0001), and easier distant metastases ( = 0.026) than the patients who were tested EBV DNA-negative. Tumor volume, T1WI and T2WI showed significant differences between the two groups. The combination of the three features achieved an AUC of 0.783 [95% confidence interval (CI) 0.678-0.888] with a sensitivity and specificity of 70.4 and 74.1%, respectively, in differentiating EBV DNA-positive tumors from EBV DNA-negative tumors. The combination of overall stage and tumor volume of T2WI and EBV status was the most effective model for predicting PD in patients with primary NPC. The overall accuracy was 84.6%, with a sensitivity and specificity of 93.8 and 66.2%, respectively (AUC, 0.800; 95% CI 0.700-0.900). This study demonstrates that MRI-based radiological features and EBV status can be used as an aid tool for the evaluation of PD, in order to develop tailored treatment targeting specific characteristics of individual patients.
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http://dx.doi.org/10.3389/fonc.2021.610804DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7986723PMC
March 2021

An Approach Based on Mammographic Imaging and Radiomics for Distinguishing Male Benign and Malignant Lesions: A Preliminary Study.

Front Oncol 2020 16;10:607235. Epub 2021 Feb 16.

Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China.

Purpose: To develop and validate an imaging-radiomics model for the diagnosis of male benign and malignant breast lesions.

Methods: Ninety male patients who underwent preoperative mammography from January 2011 to December 2018 were enrolled in this study (63 in the training cohort and 27 in the validation cohort). The region of interest was segmented into a mediolateral oblique view, and 104 radiomics features were extracted. The minimum redundancy and maximum relevance (mRMR) and the least absolute shrinkage and selection operator (LASSO) methods were used to exclude radiomics features to establish the radiomics score (rad-score). Mammographic features were evaluated by two radiologists. Univariate logistic regression was used to select for imaging features, and multivariate logistic regression was used to construct an imaging model. An imaging-radiomics model was eventually established, and a nomogram was developed based on the imaging-radiomics model. Area under the curve (AUC) and decision curve analysis (DCA) were applied to assess the clinical value.

Results: The AUC based on the imaging model in the validation cohort was 0.760, the sensitivity was 0.750, and the specificity was 0.727. The AUC, sensitivity and specificity based on the radiomics in the validation cohort were 0.820, 0.750, and 0.867, respectively. The imaging-radiomics model was better than the imaging and radiomics models; the AUC, sensitivity, and specificity of the imaging-radiomics model in the validation cohort were 0.870, 0.824, and 0.900, respectively.

Conclusion: The imaging-radiomics model created by the imaging characteristics and radiomics features exhibited a favorable discriminatory ability for male breast cancer.
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http://dx.doi.org/10.3389/fonc.2020.607235DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7921734PMC
February 2021

The clinicopathological and MRI features of patients with BRCA1/2 mutations in familial breast cancer.

Gland Surg 2021 Jan;10(1):262-272

Department of Breast Surgery, Key Laboratory of Breast Cancer in Shanghai, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.

Background: To determine the histopathological and MRI features of BRCA1/2 mutation-associated familial breast cancers compared with those of BRCA1/2 mutation-negative and sporadic breast cancers and to further compare the imaging features of cancers from BRCA1 and BRCA2 mutation carriers according to lesion type on MRI.

Methods: A retrospective review of medical records was conducted to determine tumour clinicopathologic features and MRI characteristics between June 2011 and July 2017, and 93 lesions with BRCA mutations, 93 lesions without BRCA mutations from familial breast cancers and 93 lesions from sporadic breast cancers were included. Histopathologic data, including immunohistochemistry findings and MRI data according to the BI-RADS lexicon, were reviewed. The association between MRI or histopathologic findings and BRCA mutations was analysed.

Results: BRCA-positive familial breast cancers had a higher number of IDCs with high nuclear grade and lymph node metastasis (all P<0.05), while the BRCA-negative group had a significantly lower Ki-67 index (P<0.001). BPE on MRI was found to be significantly lower for BRCA mutations of familial breast cancer (P=0.024). BRCA1 carriers tended to exhibit the triple-negative phenotype with a more benign shape and margin (P=0.006 and 0.019), whereas BRCA2 mutations were associated with the luminal phenotype and more malignant features.

Conclusions: BRCA mutation carriers had a significantly higher number of IDCs with more aggressive cancer, and BRCA-negative cancers had low proliferation levels. Background features on MRI may help to identify BRCA status, while tumour characteristics can differentiate the BRCA1/2 mutation status, consistent with the differences in their clinicopathologic features.
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http://dx.doi.org/10.21037/gs-20-596DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7882350PMC
January 2021

Computer-aided diagnosis of ground glass pulmonary nodule by fusing deep learning and radiomics features.

Phys Med Biol 2021 Mar 4;66(6):065015. Epub 2021 Mar 4.

Department of Radiology, Huzhou Central Hospital, Affiliated Central Hospital of Huzhou University, 1558 Sanhuan North Road, Huzhou, Zhejiang, 313000, People's Republic of China.

Objectives: This study aims to develop a computer-aided diagnosis (CADx) scheme to classify between benign and malignant ground glass nodules (GGNs), and fuse deep leaning and radiomics imaging features to improve the classification performance.

Methods: We first retrospectively collected 513 surgery histopathology confirmed GGNs from two centers. Among these GGNs, 100 were benign and 413 were malignant. All malignant tumors were stage I lung adenocarcinoma. To segment GGNs, we applied a deep convolutional neural network and residual architecture to train and build a 3D U-Net. Then, based on the pre-trained U-Net, we used a transfer learning approach to build a deep neural network (DNN) to classify between benign and malignant GGNs. With the GGN segmentation results generated by 3D U-Net, we also developed a CT radiomics model by adopting a series of image processing techniques, i.e. radiomics feature extraction, feature selection, synthetic minority over-sampling technique, and support vector machine classifier training/testing, etc. Finally, we applied an information fusion method to fuse the prediction scores generated by DNN based CADx model and CT-radiomics based model. To evaluate the proposed model performance, we conducted a comparison experiment by testing on an independent testing dataset.

Results: Comparing with DNN model and radiomics model, our fusion model yielded a significant higher area under a receiver operating characteristic curve (AUC) value of 0.73 ± 0.06 (P < 0.01). The fusion model generated an accuracy of 75.6%, F1 score of 84.6%, weighted average F1 score of 70.3%, and Matthews correlation coefficient of 43.6%, which were higher than the DNN model and radiomics model individually.

Conclusions: Our experimental results demonstrated that (1) applying a CADx scheme was feasible to diagnosis of early-stage lung adenocarcinoma, (2) deep image features and radiomics features provided complementary information in classifying benign and malignant GGNs, and (3) it was an effective way to build DNN model with limited dataset by using transfer learning. Thus, to build a robust image analysis based CADx model, one can combine different types of image features to decode the imaging phenotypes of GGN.
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http://dx.doi.org/10.1088/1361-6560/abe735DOI Listing
March 2021

A combined radiomics and clinical variables model for prediction of malignancy in T2 hyperintense uterine mesenchymal tumors on MRI.

Eur Radiol 2021 Jan 23. Epub 2021 Jan 23.

Department of Radiology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Chongming Branch, No.25, Nanmen Road, Chongming District, 202150, Shanghai, China.

Objective: This study aims to develop a machine learning model for prediction of malignancy in T2 hyperintense mesenchymal uterine tumors based on T2-weighted image (T2WI) features and clinical information.

Methods: This retrospective study included 134 patients with T2 hyperintense uterine mesenchymal tumors (104 patients in training cohort and 30 in testing cohort). A total of 960 radiomics features were initially computed and extracted from each 3D segmented tumor depicting on T2WI. The support vector machine (SVM) classifier was applied to build computer-aided diagnosis (CAD) models by using selected clinical and radiomics features, respectively. Finally, an observer study was conducted by comparing with two radiologists to evaluate the diagnostic performance. The area under the receiver operating characteristic (ROC) curve (AUC) was computed to assess the performance of each model.

Results: Comparing with the T2WI-based radiomics model (AUC: 0.76 ± 0.09) and the clinical model (AUC: 0.79 ± 0.09), the combined model significantly improved the AUC value to 0.91 ± 0.05 (p < 0.05). The clinical-radiomics combined model yielded equivalent or higher performance than two radiologists (AUC: 0.78 vs. 0.91, p = 0.03; 0.90 vs.0.91, p = 0.13). There was a significant difference between the AUC values of two radiologists (p < 0.05).

Conclusions: It is feasible to predict malignancy risk of T2 hyperintense uterine mesenchymal tumors by combining clinical variables and T2WI-based radiomics features. Machine learning-based classification model may be useful to assist radiologists in decision-making.

Key Points: • Radiomics approach has the potential to distinguish between benign and malignant mesenchymal uterine tumors. • T2WI-based radiomics analysis combined with clinical variables performed well in predicting malignancy risk of T2 hyperintense uterine mesenchymal tumors. • Machine learning-based classification model may be useful to assist radiologists in characterization of a T2 hyperintense uterine mesenchymal tumor.
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http://dx.doi.org/10.1007/s00330-020-07678-9DOI Listing
January 2021

Mass Detection and Segmentation in Digital Breast Tomosynthesis Using 3D-Mask Region-Based Convolutional Neural Network: A Comparative Analysis.

Front Mol Biosci 2020 11;7:599333. Epub 2020 Nov 11.

Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou, China.

Digital breast tomosynthesis (DBT) is an emerging breast cancer screening and diagnostic modality that uses quasi-three-dimensional breast images to provide detailed assessments of the dense tissue within the breast. In this study, a framework of a 3D-Mask region-based convolutional neural network (3D-Mask RCNN) computer-aided diagnosis (CAD) system was developed for mass detection and segmentation with a comparative analysis of performance on patient subgroups with different clinicopathological characteristics. To this end, 364 samples of DBT data were used and separated into a training dataset ( = 201) and a testing dataset ( = 163). The detection and segmentation results were evaluated on the testing set and on subgroups of patients with different characteristics, including different age ranges, lesion sizes, histological types, lesion shapes and breast densities. The results of our 3D-Mask RCNN framework were compared with those of the 2D-Mask RCNN and Faster RCNN methods. For lesion-based mass detection, the sensitivity of 3D-Mask RCNN-based CAD was 90% with 0.8 false positives (FPs) per lesion, whereas the sensitivity of the 2D-Mask RCNN- and Faster RCNN-based CAD was 90% at 1.3 and 2.37 FPs/lesion, respectively. For breast-based mass detection, the 3D-Mask RCNN generated a sensitivity of 90% at 0.83 FPs/breast, and this framework is better than the 2D-Mask RCNN and Faster RCNN, which generated a sensitivity of 90% with 1.24 and 2.38 FPs/breast, respectively. Additionally, the 3D-Mask RCNN achieved significantly ( < 0.05) better performance than the 2D methods on subgroups of samples with characteristics of ages ranged from 40 to 49 years, malignant tumors, spiculate and irregular masses and dense breast, respectively. Lesion segmentation using the 3D-Mask RCNN achieved an average precision (AP) of 0.934 and a false negative rate (FNR) of 0.053, which are better than those achieved by the 2D methods. The results suggest that the 3D-Mask RCNN CAD framework has advantages over 2D-based mass detection on both the whole data and subgroups with different characteristics.
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http://dx.doi.org/10.3389/fmolb.2020.599333DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7686533PMC
November 2020

Is Tc bone scintigraphy necessary in the preoperative workup for patients with cT1N0 subsolid lung cancer? A prospective multicenter cohort study.

Thorac Cancer 2021 02 19;12(4):415-419. Epub 2020 Nov 19.

Departments of Thoracic Surgery and State Key Laboratory of Genetic Engineering, Fudan University Shanghai Cancer Center, Shanghai, China.

Background: Tc bone scintigraphy (BS) is still the most common approach for the evaluation of bone metastasis in China. The purpose of this study was to investigate the necessity of BS as part of a routine preoperative workup for patients with cT1N0 subsolid lung cancer.

Methods: This was a prospective multicenter clinical trial (NCT03689439). Patients with cT1N0 subsolid nodules who were candidates for surgical resection were consecutively enrolled into the study. BS was performed preoperatively. The surgical plan could be changed if a positive result was detected. The primary endpoint was the incidence rate of the surgical plan being changed because of positive BS results. The secondary endpoint was the rate of positive BS findings and the rate of related complications.

Results: From November 2018 to July 2019, 691 patients were enrolled into the study. None of the patients had positive BS results and no surgical plans were changed by BS findings. There were 222 male and 469 female patients. The average age was 54.8 ± 3.7 years old. The average tumor diameter was 14.9 ± 4.2 mm. There were 282 patients with pure GGO nodules and 409 with part-solid nodules. A total of 470 patients had a single nodule, while 221 patients had multifocal lesions. The number of patients whose pathological diagnosis was invasive adenocarcinoma, minimally invasive adenocarcinoma, adenocarcinoma in situ and mucinous adenocarcinoma was 357, 293, 32 and nine, respectively. The number of patients who underwent lobectomy, segmentectomy and wedge resection was 234, 199 and 258, respectively.

Conclusions: Tc bone scintigraphy is unnecessary in the preoperative workup for patients with cT1N0 subsolid lung cancer.

Key Points: SIGNIFICANT FINDINGS OF THE STUDY: In this prospective study of 691 patients with cT1N0 subsolid lung cancer, no surgical plans were affected by positive bone scan findings.

What This Study Adds: We suggest physicians consider canceling BS from preoperative workup for cT1 subsolid lung cancer patients. Clinical trial registry number: NCT03689439.
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http://dx.doi.org/10.1111/1759-7714.13752DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7882389PMC
February 2021

Everolimus-Related Pneumonitis in Patients with Metastatic Breast Cancer: Incidence, Radiographic Patterns, and Relevance to Clinical Outcome.

Oncologist 2021 04 7;26(4):e580-e587. Epub 2020 Dec 7.

Departments of Medical Oncology, Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China.

Background: This study investigated the incidence, radiographic patterns, and relevance to clinical outcome of everolimus-related pneumonitis (ERP) in patients with metastatic breast cancer (MBC).

Materials And Methods: Data of patients with MBC treated with everolimus who had baseline and at least one follow-up chest computed tomography (CT) were obtained from a medical electronic database system. An independent review of the CT scans of these patients was conducted by two radiologists (NCT03730428). Log-rank and Cox proportional hazard regression analyses were used for time-to-event analyses.

Results: ERP was radiographically detected in 45 of 86 patients (52.3%). In more than 80% of these patients, ERP occurred during the first 4 months of everolimus treatment. Only 14 of the 45 patients with ERP were symptomatic (31.1%). Symptoms included cough, fever, and shortness of breath. Bilateral and lower distribution of the pneumonitis was most common. In most of the cases, ground-glass opacities and reticular opacities were noticed. Elderly patients were more likely to develop ERP. Patients with ERP had significantly longer progression-free survival (PFS; 6.8 vs. 4.1 months, p = .024) and overall survival (OS; 42.8 vs. 21.3 months, p = .016). ERP was a predictor of OS improvement confirmed by multivariate Cox analysis (hazard ratio, 0.49; 95% confidence interval, 0.25-0.97; p = .040).

Conclusions: ERP was noted in half of the patients with MBC treated with everolimus. Our data suggested that ERP was associated with improved prognosis and may be used as a biomarker for the efficacy of everolimus in MBC. Close monitoring, prompt diagnosis, and proper treatment for ERP are essential to maintain the quality of life of patients and achieve maximum treatment benefits.

Implications For Practice: Everolimus-related pneumonitis (ERP) is one of the most worrying drug adverse events, especially in Asian patients. However, little has been known about the clinical and radiographic details of ERP in patients with metastatic breast cancers (MBCs) treated with everolimus. The present study investigated the clinical characteristics, radiographic patterns, and its correlation with treatment outcome in patients with MBC. ERP was identified in more than half of patients with MBC during everolimus therapy and was associated with improved outcome. Close monitoring and prompt diagnosis and appropriate treatment for ERP are critical for the preservation of patients' quality of life and achievement of maximal treatment benefits.
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http://dx.doi.org/10.1002/onco.13594DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8018320PMC
April 2021

MRI-Based Radiomic Signature as a Prognostic Biomarker for HER2-Positive Invasive Breast Cancer Treated with NAC.

Cancer Manag Res 2020 27;12:10603-10613. Epub 2020 Oct 27.

Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China.

Purpose: To identify MRI-based radiomics signature (Rad-score) as a biomarker of risk stratification for disease-free survival (DFS) in patients with HER2-positive invasive breast cancer treated with trastuzumab-based neoadjuvant chemotherapy (NAC) and establish a radiomics-clinicoradiologic-based nomogram that combines Rad-score, MRI findings, and clinicopathological variables for DFS estimation.

Patients And Methods: A total of 127 patients were divided into a training set and testing set according to the ratio of 7:3. Radiomic features were extracted from multiphase CE-MRI (CE). Rad-score was calculated using the LASSO (least absolute shrinkage and selection operator) regression analysis. The cutoff point of Rad-score to divide the patients into high- and low-risk groups was determined by receiver operating characteristic curve analysis. A Kaplan-Meier survival curves and the Log rank test were used to investigate the association of the Rad-score with DFS. Univariate and multivariate Cox proportional hazards model were used to determine the association of Rad-score, MRI features, and clinicopathological variables with DFS. A radiomics-clinicoradiologic-based nomogram combining the Rad-score, MRI features, and clinicopathological findings was plotted to validate the radiomic signatures for DFS estimation.

Results: The Rad-score stratified patients into high- and low-risk groups for DFS in the training set (<0.0001) and was validated in the testing set (=0.002). The radiomics-clinicoradiologic-based nomogram estimated DFS (training set: C-index=0.974, 95% confidence interval (CI)=0.954-0.994; testing set: C-index=0.917, 95% CI=0.842-0.991) better than the clinicoradiologic-based nomogram (training set: C-index=0.855, 95% CI=0.739-0.971; testing set: C-index=0.831, 95% CI=0.643-0.999).

Conclusion: The Rad-score is an independent biomarker for the estimation of DFS in invasive HER2-positive breast cancer with NAC and the radiomics-clinicoradiologic-based nomogram improved individualized DFS estimation.
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http://dx.doi.org/10.2147/CMAR.S271876DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7602910PMC
October 2020

Differential diagnosis of benign and malignant male breast lesions in mammography.

Eur J Radiol 2020 Nov 9;132:109339. Epub 2020 Oct 9.

Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China. Electronic address:

Purpose: To investigate the mammographic characteristics in discriminating benign and malignant male breast lesions.

Methods: Male patients with breast lesions detected by preoperative mammography were enrolled in this study from Jan 2011 to Dec 2018. All lesions were confirmed by biopsy and classified into benign group or malignant group. Imaging features included lesions location, lesion type, lesion density, lesion eccentricity, accompanying signs(calcification, nipple retraction, thickened skin and enlarged lymph nodes) were recorded and analysed by statistical methods. The AUC was calculated to assess their diagnostic performance in distinguishing benign and malignant lesions. This model was further validated by 0.632 bootstrap.

Results: A total of 93 men(median age: 60, range 32-81 years) were enrolled, 43 patients in the benign group and 50 patients in the malignant group. In the univariate logistic analysis, age, lesion location, lesion type, lesion density, lesion eccentricity, calcification, nipple retraction and skin thickening were significantly different (p < 0.05). When the lesion showed a mass in mammography, those with a circumscribed margin were likely malignant (p < 0.05). In the multivariate logistic analysis, non-retro-areola lesions (OR: 6.900, 95 % CI: 1.413∼33.691, p < 0.05), eccentric lesions (OR: 14.566, 95 % CI: 2.800∼75.777, p < 0.05), high-density lesions (OR: 11.052, 95 % CI: 2.235∼54.666, p < 0.05), calcification (OR: 12.715 95 % CI: 1.316∼122.848, p < 0.05) and nipple retraction (OR: 24.681, 95 % CI: 2.853∼213.542 p < 0.05) were associated with breast cancer. Those variables were used to build logistic model and the AUC of the imaging model was 0.904. The imaging model was verified by 0.632 bootstrap resampling, and the AUC after 0.632 bootstrap was 0.892.

Conclusion: Mammographic characteristics could contribute to distinguishing malignant and benign male breast lesions, and the imaging model showed excellent diagnostic performance, which may help to guide clinical decision-making.
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http://dx.doi.org/10.1016/j.ejrad.2020.109339DOI Listing
November 2020

Three-Dimensional CT Texture Analysis to Differentiate Colorectal Signet-Ring Cell Carcinoma and Adenocarcinoma.

Cancer Manag Res 2019 13;11:10445-10453. Epub 2019 Dec 13.

Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai 200032, People's Republic of China.

Purpose: The objective of this research was to validate the diagnostic value of three-dimensional texture parameters and clinical characteristics in the differentiation of colorectal signet-ring cell carcinoma (SRCC) and adenocarcinoma (AC).

Methods: We retrospectively analyzed data from 102 patients with SRCC or AC confirmed by pathology, including 51 SRCC (from January 2015 to July 2019) and 51 AC patients (from January 2019 to July 2019). CT findings and clinical data, including age, gender, clinical symptoms, serological biomarkers, tumor size, and tumor location, were compared between SRCC and AC. CT texture features were quantified on portal phase images using three-dimensional analysis. A list of texture parameters was generated with MaZda software for the classification of tumors. The texture features, clinical data and CT findings were statistically analyzed for the discrimination ability of SRCC and AC, and the potential predictive parameters that may be used to differentiate the two groups were subsequently tested using the least absolute shrinkage and selection operator (LASSO) and logistic regression analyses. The receiver operating characteristic curve (ROC) provided a range of values for establishing the cutoff value, as well as the sensitivity and specificity of prediction for each significant variable.

Results: SRCC occurred more often in men than AC did (80.39% vs 49.02%, P < 0.01). The patients were younger in the SRCC group than in the AC group, without a statistically significant difference (55.84 vs 59.20 years, P = 0.216). There were no significant differences in the clinical symptoms, tumor size, or tumor location between the two groups (P=0.505, P=0.19, P=0.843, respectively). The elevation of serological biomarker CA724 was more common in SRCC than in AC (P< 0.001). Perc.01%3D, Perc.10%3D and s(1,0,0) SumAverg were lower in the SRCC group than in the AC group during the portal phase, with the areas under curve (AUCs) of 0.892-0.929, sensitivity of 76.5-84.3% and specificity of 88.2-96.1%. In the differentiation between SRCC and AC, the 1-NN minimal classification error (MCR) was 29.41%.

Conclusion: Three-dimensional texture parameters, including Perc.01%3D, Perc.10%3D and s(1,0,0) SumAverg, exhibited a favorable discriminatory ability to distinguish SRCC from AC.
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http://dx.doi.org/10.2147/CMAR.S233595DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6918095PMC
December 2019

Applications of rib sparing technique in internal mammary vessels exposure of abdominal free flap breast reconstructions: a 12-year single-center experience of 215 cases.

Gland Surg 2019 Oct;8(5):477-485

Department of Breast Surgery, Breast Cancer Institute, Fudan University Shanghai Cancer Center, Shanghai 200032, China.

Background: Internal mammary vessels (IMVs) are widely used recipient vessels in abdominal free flap breast reconstructions. Rib sparing technique is an alternative method with less damage in IMVs exposure. This study aims to investigate the factors influencing the selection of IMVs, as well as analyze the applicability and related factors of rib sparing technique in abdominal breast reconstruction.

Methods: Medical records of 215 patients who underwent abdominal free flap reconstruction from November 2006 to December 2017 in Fudan University Shanghai Cancer Center (FUSCC) were analyzed. Intercostal space (ICS) was measured from preoperative chest computed tomography scan. Factors influencing the choice of recipient vessels and rib sparing were analyzed. Surgery time, hospitalization and complications were assessed.

Results: Among all 218 flaps, 172 flaps used IMVs as the recipient vessels while 46 used other vessels. patients with immediate reconstruction (P=0.005) and axillary lymph nodes dissection (ALND) (P<0.001) were less likely to use IMVs. Patients' body mass index (BMI) and radiotherapy history showed no statistically significant differences between the two groups (P=0.338 and 0.811). In IMVs group, 62% cases used rib sparing technique. Compared with rib resection group, patients with rib sparing were taller (P=0.047) and with a wider ICS (2.65±0.54 2.25±0.38 cm, P<0.001). Rib sparing group had a shorter surgery and postoperative hospitalization time, as well as a lower complication rate, but the differences were not statistically significant (P=0.120, 0.450 and 0.612).

Conclusions: IMVs were used more frequently as the recipient vessels in abdominal free flap breast reconstructions, especially when axillary operation was not performed at the same time. Rib sparing technique had the potential to decrease surgery time, hospitalization days and complications rate. It could be applied in most of the patients with IMVs exposure, particularly in taller patients and patients with a wider ICS. Preoperative chest computed tomography scan can be used to assess the ICS width to provide operational suggestions.
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http://dx.doi.org/10.21037/gs.2019.08.08DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6842765PMC
October 2019

Comparison of the diagnostic performance of synthesized two-dimensional mammography and full-field digital mammography alone or in combination with digital breast tomosynthesis.

Breast Cancer 2020 Jan 13;27(1):47-53. Epub 2019 Jul 13.

Department of Radiology, Fudan University Cancer Center, Shanghai, People's Republic of China.

Purpose: To investigate whether digital breast tomosynthesis (DBT) and subsequently generated synthesized mammography (SM) images show a better performance than full-field digital mammography (FFDM) images for diagnosing malignant breast lesions. In addition, the radiation doses for SM using different procedures were compared.

Materials And Methods: This prospective study enrolled 212 women (age ≥ 25 years) with clinically suspicious breast lesions. All participants underwent FFDM and DBT with the same breast compression. Finally, 222 lesions were confirmed by pathological analysis. The mammogram results were evaluated according to the BI-RADS criteria and compared with the pathological results. The diagnostic performances, morphological features and average glandular doses (AGDs) were compared.

Results: In total, 141 malignant lesions and 81 benign lesions were confirmed by pathological analysis. The overall AGD showed no significant difference between FFDM and DBT. Compared with 2D imaging, the AUC values of FFDM plus DBT and SM plus DBT were both significantly different overall (P = 0.0002) and remained significantly different in dense breasts (P < 0.0001). In terms of morphologic characteristics, lesions showed similar morphology between FFDM and SM, while the lesion characteristics were discordant from 2D imaging to DBT in 33 lesions in dense breasts.

Conclusions: Compared to FFDM, 2D SM images generated from DBT had significantly improved diagnostic efficacy for detecting malignant breast lesions without increasing radiation doses. This new procedure is useful for characterizing breast lesions, particularly in dense breasts.
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http://dx.doi.org/10.1007/s12282-019-00992-1DOI Listing
January 2020

The volumetric-tumour histogram-based analysis of intravoxel incoherent motion and non-Gaussian diffusion MRI: association with prognostic factors in HER2-positive breast cancer.

J Transl Med 2019 07 2;17(1):182. Epub 2019 Jul 2.

Department of Radiology, Fudan University Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, No. 270, Dongan Road, Shanghai, 200032, People's Republic of China.

Background: To evaluate the imaging biomarkers of human epidermal growth factor receptor 2 (HER2) positive breast cancer in comparison to other molecular subtypes and to determine the feasibility of identifying hormone receptor (HR) status and lymph node metastasis status using volumetric-tumour histogram-based analysis through intravoxel incoherent motion (IVIM) and non-Gaussian diffusion.

Methods: This study included 145 breast cancer patients with 148 lesions between January and November in 2018. Among the 148 lesions, 74 were confirmed to be HER2-positive. The volumetric-tumour histogram-based features were extracted from the combined IVIM and non-Gaussian diffusion model. IVIM and non-Gaussian diffusion parameters obtained from images of the subjects with different molecular prognostic biomarker statuses were compared by Student's t test or the Mann-Whitney U test. The area under the curve (AUC), sensitivity, and specificity at the best cut-off point were reported. The Spearman correlation coefficient was calculated to analyse the correlations of clinical tumor nodule metastasis (TNM) stage and Ki67 with the IVIM and non-Gaussian diffusion parameters.

Results: The entropy of mean kurtosis (MK) was significantly higher in the HER2-positive group than in the HER2-negative group (p = 0.015), with an AUC of 0.629 (95% CI 0.546, 0.707), a sensitivity of 62.6%, and a specificity of 66.2%. For HR status, the MD 5th percentile was higher in the HR-positive group of HER2-positive breast cancer (p = 0.041), with an AUC of 0.643 (95% CI 0.523, 0.751), while for lymph node status, the entropy of mean diffusivity (MK) was lower in the lymph node positive group (p = 0.040), with an AUC of 0.587 (95% CI 0.504, 0.668). The clinical TNM stage and Ki67 index were correlated with several histogram parameters.

Conclusion: Volumetric-lesion histogram analysis of IVIM and the non-Gaussian diffusion model can be used to provide prognostic information about HER2-positive breast cancers and potentially contribute to individualized anti-HER2 targeted therapy plans .
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http://dx.doi.org/10.1186/s12967-019-1911-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6604303PMC
July 2019

Machine Learning-Based Analysis of MR Multiparametric Radiomics for the Subtype Classification of Breast Cancer.

Front Oncol 2019 14;9:505. Epub 2019 Jun 14.

Human Phenome Institute, Fudan University, Shanghai, China.

To investigate whether machine learning analysis of multiparametric MR radiomics can help classify immunohistochemical (IHC) subtypes of breast cancer. One hundred and thirty-four consecutive patients with pathologically-proven invasive ductal carcinoma were retrospectively analyzed. A total of 2,498 features were extracted from the DCE and DWI images, together with the new calculated images, including DCE images changing over six time points (DCE) and DWI images changing over three -values (DWI). We proposed a novel two-stage feature selection method combining traditional statistics and machine learning-based methods. The accuracies of the 4-IHC classification and triple negative (TN) vs. non-TN cancers were assessed. For the 4-IHC classification task, the best accuracy of 72.4% was achieved based on linear discriminant analysis (LDA) or subspace discrimination of assembled learning in conjunction with 20 selected features, and only small dependent emphasis of Kendall-tau-b for sequential features, based on the DWI with the LDA model, yielding an accuracy of 53.7%. The linear support vector machine (SVM) and medium k-nearest neighbor using eight features yielded the highest accuracy of 91.0% for comparing TN to non-TN cancers, and the maximum variance for DWI alone, together with a linear SVM model, achieved an accuracy of 83.6%. Whole-tumor radiomics on MR multiparametric images, DCE images changing over time points, and DWI images changing over different -values provide a non-invasive analytical approach for breast cancer subtype classification and TN cancer identification.
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http://dx.doi.org/10.3389/fonc.2019.00505DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6587031PMC
June 2019

Characteristics of Mammographic Breast Density and Associated Factors for Chinese Women: Results from an Automated Measurement.

J Oncol 2019 19;2019:4910854. Epub 2019 Mar 19.

Medical Radiation Sciences, Faculty of Health Sciences, The University of Sydney, Cumberland Campus, 75 East Street, Lidcombe, NSW, Australia.

Background: Characteristics of mammographic density for Chinese women are understudied. This study aims to identify factors associated with mammographic density in China using a quantitative method.

Methods: Mammographic density was measured for a total of 1071 (84 with and 987 without breast cancer) women using an automatic algorithm AutoDensity. Pearson tests examined relationships between density and continuous variables and t-tests compared differences of mean density values between groupings of categorical variables. Linear models were built using multiple regression.

Results: Percentage density and dense area were positively associated with each other for cancer-free (r=0.487, p<0.001) and cancer groups (r=0.446, p<0.001), respectively. For women without breast cancer, weight and BMI (p<0.001) were found to be negatively associated (r=-0.237, r=-0.272) with percentage density whereas they were found to be positively associated (r=0.110, r=0.099) with dense area; age at mammography was found to be associated with percentage density (r=-0.202, p<0.001) and dense area (r=-0.086, p<0.001) but did not add any prediction within multivariate models; lower percentage density was found within women with secondary education background or below compared to women with tertiary education. For women with breast cancer, percentage density demonstrated similar relationships with that of cancer-free women whilst breast area was the only factor associated with dense area (r=0.739, p<0.001).

Conclusion: This is the first time that mammographic density was measured by a quantitative method for women in China and identified associations should be useful to health policy makers who are responsible for introducing effective models of breast cancer prevention and diagnosis.
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http://dx.doi.org/10.1155/2019/4910854DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6444251PMC
March 2019

Feasibility study of dual parametric 2D histogram analysis of breast lesions with dynamic contrast-enhanced and diffusion-weighted MRI.

J Transl Med 2018 11 23;16(1):325. Epub 2018 Nov 23.

Fudan University Shanghai Cancer Center, No. 270, Dong'an Rd, Shanghai, 200032, China.

Background: This study aimed to investigate the diagnostic value of a dual-parametric 2D histogram classification method for breast lesions.

Methods: This study included 116 patients with 72 malignant and 44 benign breast lesions who underwent CAIPIRINHA-Dixon-TWIST-VIBE dynamic contrast-enhanced (CDT-VIBE DCE) and readout-segmented diffusion-weighted magnetic resonance examination. The volume of interest (VOI), which encompassed the entire lesion, was segmented from the last phase of DCE images. For each VOI, a 1D histogram analysis (mean, median, 10th percentile, 90th percentile, kurtosis and skewness) was performed on apparent diffusion coefficient (ADC) and volume transfer constant (Ktrans) maps; a 2D histogram image (Ktrans-ADC) was generated from the pixelwise aligned maps, and its kurtosis and skewness were calculated. Each parameter was correlated with pathological results using the Mann-Whitney test and receiver operating characteristic curve analysis.

Results: For the Ktrans histogram, the area under the curve (AUC) of the mean, median, 90th percentile and kurtosis had statistically diagnostic values (mean: 0.760; median: 0.661; 90th percentile: 0.781; and kurtosis: 0.620). For the ADC histogram, the AUC of the mean, median, 10th percentile, skewness and kurtosis had statistically diagnostic values (mean: 0.661; median: 0.677; 10th percentile: 0.656; skewness: 0.664; and kurtosis: 0.620). For the 2D Ktrans-ADC histogram, the skewness and kurtosis had statistically higher diagnostic values (skewness: 0.831, kurtosis: 0.828) than those of the 1D histogram (all P < 0.05).

Conclusions: The dual-parametric 2D histogram analysis revealed better diagnostic accuracy for breast lesions than single parametric histogram analysis of either Ktrans or ADC maps.
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http://dx.doi.org/10.1186/s12967-018-1698-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6260880PMC
November 2018

Differentiation of triple-negative breast cancer from other subtypes through whole-tumor histogram analysis on multiparametric MR imaging.

Eur Radiol 2019 May 6;29(5):2535-2544. Epub 2018 Nov 6.

Department of Radiology, Fudan University Shanghai Cancer Center, No. 270 Dong'an Road, Shanghai, 200032, People's Republic of China.

Purpose: To identify triple-negative (TN) breast cancer imaging biomarkers in comparison to other molecular subtypes using multiparametric MR imaging maps and whole-tumor histogram analysis.

Materials And Methods: This retrospective study included 134 patients with invasive ductal carcinoma. Whole-tumor histogram-based texture features were extracted from a quantitative ADC map and DCE semi-quantitative maps (washin and washout). Univariate analysis using the Student's t test or Mann-Whitney U test was performed to identify significant variables for differentiating TN cancer from other subtypes. The ROC curves were generated based on the significant variables identified from the univariate analysis. The AUC, sensitivity, and specificity for subtype differentiation were reported.

Results: The significant parameters on the univariate analysis achieved an AUC of 0.710 (95% confidence interval [CI] 0.562, 0.858) with a sensitivity of 63.6% and a specificity of 73.1% at the best cutoff point for differentiating TN cancers from Luminal A cancers. An AUC of 0.763 (95% CI 0.608, 0.917) with a sensitivity of 86.4% and a specificity of 72.2% was achieved for differentiating TN cancers from human epidermal growth factor receptor 2 (HER2) positive cancers. Also, an AUC of 0.683 (95% CI 0.556, 0.809) with a sensitivity of 54.5% and a specificity of 83.9% was achieved for differentiating TN cancers from non-TN cancers. There was no significant feature on the univariate analysis for TN cancers versus Luminal B cancers.

Conclusions: Whole-tumor histogram-based imaging features derived from ADC, along with washin and washout maps, provide a non-invasive analytical approach for discriminating TN cancers from other subtypes.

Key Points: • Whole-tumor histogram-based features on MR multiparametric maps can help to assess biological characterization of breast cancer. • Histogram-based texture analysis may predict the molecular subtypes of breast cancer. • Combined DWI and DCE evaluation helps to identify triple-negative breast cancer.
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http://dx.doi.org/10.1007/s00330-018-5804-5DOI Listing
May 2019

Evaluation of Salivary Gland Function Using Diffusion-Weighted Magnetic Resonance Imaging for Follow-Up of Radiation-Induced Xerostomia.

Korean J Radiol 2018 Jul-Aug;19(4):758-766. Epub 2018 Jun 14.

Department of Radiology, Shanghai Proton and Heavy Ion Center, Shanghai 201321, China.

Objective: To investigate the value of diffusion-weighted magnetic resonance imaging (DW-MRI) as a noninvasive tool to assess salivary gland function for follow-up of patients with radiation-induced xerostomia.

Materials And Methods: This study included 23 patients with nasopharyngeal carcinoma who had been treated with parotid-sparing radiotherapy (RT). Salivary function was assessed by DW-MRI pre-treatment and one week and one year post-RT, respectively. The maximum apparent diffusion coefficient (ADC) of parotid glands (pADCmax) and the time to peak ADC of parotid glands (pTmax) during stimulation were obtained. Multivariate analysis was used to analyze factors correlated with the severity of radiation-induced xerostomia.

Results: The ADCs of parotid and submandibular glands (1.26 ± 0.10 × 10 mm/s and 1.32 ± 0.07 × 10 mm/s pre-RT, respectively) both showed an increase in all patients at one week post-RT (1.75 ± 0.16 × 10 mm/s, < 0.001 and 1.70 ± 0.16 × 10 mm/s, < 0.001, respectively), followed by a decrease in parotid glands at one year post-RT(1.57 ± 0.15 × 10 mm/s, < 0.001) but not in submandibular glands (1.69 ± 0.18 × 10 mm/s, = 0.581). An improvement in xerostomia was found in 13 patients at one year post-RT. Multivariate analysis revealed 4 significant predictors for the improvement of xerostomia, including dose to parotid glands ( = 0.009, odds ratio [OR] = 0.639), the ADC of submandibular glands ( = 0.013, OR = 3.295), pADCmax ( = 0.024, OR = 0.474), and pTmax ( = 0.017, OR = 0.729) at one week post-RT.

Conclusion: The ADC value is a sensitive indicator for salivary gland dysfunction. DW-MRI is potentially useful for noninvasively predicting the severity of radiation-induced xerostomia.
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http://dx.doi.org/10.3348/kjr.2018.19.4.758DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6005952PMC
April 2019

Diffusion kurtosis imaging in the characterisation of rectal cancer: utilizing the most repeatable region-of-interest strategy for diffusion parameters on a 3T scanner.

Eur Radiol 2018 Dec 24;28(12):5211-5220. Epub 2018 May 24.

Department of Radiology, Fudan University Shanghai Cancer Center, 270 Dong An Road, Shanghai, 200032, China.

Objectives: Our goal was to investigate the correlation between histopathology and diffusion parameters by utilising the most repeatable region-of-interest (ROI) strategy for diffusion parameters in rectal cancer on a 3T scanner.

Methods: 113 patients underwent DKI-MR and 66 of these patients received surgery without neoadjuvant chemoradiotherapy. Two readers independently measured the parameters using three slice protocols including single slice, three slices and whole-tumour slice (WTS), combined with one of two ROIs, including outline and round ROI. ANOVA, Kruskal-Wallis, a paired sample t-test, interclass correlation coefficient (ICC), Bland-Altman, Student's t-tests, receiver operating characteristic curves and z statistic were used for statistical analysis.

Results: There were no significant differences among the three slice protocols in ADC values (p = 0.822, 0.987), K values (p = 0.842, 0.859) and D values (p = 0.917, 0.988) using round and outline ROI, respectively. The ADC and D values derived from outline ROIs were higher than those from round ROIs (all p < 0.001 for ADC, all p < 0.001 for D), while K values derived from outline ROIs were lower than those from round ROIs (p < 0.001, p = 0.001, p < 0.001) using three slice protocols, respectively. The WTS-outline ROI resulted in the best intra- and inter-observer ICC. Utilising the WTS-outline ROI method, the AUC for assessment of well-differentiated tumours was 0.871 by K and 0.809 by ADC; and the AUC for T2 was 0.768 by K.

Conclusions: The most repeatable strategy was the WTS-outline ROI method. In addition to DWI, DKI also have diagnostic value for rectal cancer histopathological characteristics utilising the WTS-outline ROI on a 3T scanner.

Key Points: • DKI using a 3T scanner is feasible for assessing rectal cancer. • ROI and slice protocol show considerable influence on DKI parameters. • DKI parameters exhibit excellent repeatability using whole-tumour slice-outline ROI on 3T scanner. • DKI has considerable diagnostic value for the estimation of rectal cancer characteristics.
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http://dx.doi.org/10.1007/s00330-018-5495-yDOI Listing
December 2018

Magnetic Resonance Imaging Features of Breast Encapsulated Papillary Carcinoma.

J Comput Assist Tomogr 2018 Jul/Aug;42(4):536-541

Objective: This study aimed to describe the magnetic resonance imaging (MRI) features of pure breast encapsulated papillary carcinoma (EPC).

Materials And Methods: Ten patients with histopathologically confirmed breast pure EPC were reviewed. Two radiologists evaluated lesion MRI characteristics.

Results: The EPC presented oval or round mass with circumscribed margin on MRI. In addition, 4 cases exhibited a cystic-solid mixed mass with mural nodules, and 4 cases exhibited a liquid level that indicated the possibility of hemorrhage.

Conclusions: A well-defined cystic-solid mixed mass with mural nodules, or a circumscribed mass exhibiting the possibility of hemorrhage, may suggest the diagnosis of EPC.
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http://dx.doi.org/10.1097/RCT.0000000000000737DOI Listing
August 2018

Predictive value of MRI-detected extramural vascular invasion in stage T3 rectal cancer patients before neoadjuvant chemoradiation.

Diagn Interv Radiol 2018 May-Jun;24(3):128-134

Shanghai Institute of Medical Imaging, Fudan University, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.

Purpose: We set out to explore the probability of MRI-detected extramural vascular invasion (mr-EMVI) before chemoradiation to predict responses to chemoradiation and survival in stage T3 rectal cancer patients.

Methods: A total of 100 patients with T3 rectal cancer who underwent MRI examination and received neoadjuvant chemoradiation and surgery were enrolled. The correlation between mr-EMVI and other clinical factors were analyzed by chi-square. Logistic regression model was performed to select the potential factors influencing tumor responses to neoadjuvant chemoradiation. A Cox proportional hazards regression model was performed to explore potential predictors of survival.

Results: The positive mr-EMVI result was more likely to be present in patients with a higher T3 subgroup (T3a+b = 7.1% vs. T3c+d = 90.1%, P < 0.001) and more likely in patients with mesorectal fascia involvement than in those without MRF (65% vs. 38.8%, P = 0.034). Compared with mr-EMVI (+) patients, more mr-EMVI (-) patients showed a good response (staged ≤ ypT2N0) (odds ratio [OR], 3.020; 95% confidence interval [CI], 1.071-8.517; P = 0.037). In univariate analysis, mr-EMVI (+) (hazard ratio [HR], 5.374; 95% CI, 1.210-23.872; P = 0.027) and lower rectal cancers (HR, 3.326; 95% CI, 1.135-9.743; P = 0.028) were significantly associated with decreased disease-free survival. A positive mr-EMVI status (HR, 5.727; 95% CI, 1.286-25.594; P = 0.022) and lower rectal cancers (HR, 3.137; 95% CI, 1.127-8.729; P = 0.029) also served as prognostic factors related to decreased disease-free survival in multivariate analysis.

Conclusion: The mr-EMVI status before chemoradiation is a significant prognostic factor and could be used for identifying T3 rectal cancer patients who might benefit from neoadjuvant chemoradiation.
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http://dx.doi.org/10.5152/dir.2018.17286DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5951200PMC
October 2018

Solid Papillary Carcinoma of the Breast: Magnetic Resonance Mammography, Digital Mammography, and Ultrasound Findings.

J Comput Assist Tomogr 2018 Sep/Oct;42(5):771-775

Background: The aim of this study was to investigate the MR mammography (MRM), digital mammography (DM), and ultrasound (US) findings of solid papillary carcinoma (SPC) of breast and to raise awareness of this rare breast tumor.

Material And Methods: Thirty patients diagnosed with breast SPC (age range, 21-72; mean age, 60.27 years) from January 2013 to August 2015 were enrolled. Their clinical presentation and MRM, DM, and US findings were retrospectively reviewed. All patients underwent both MRM and US, and 20 of them underwent DM. The research primarily investigated MRM features correlated with clinicopathological characteristics.

Results: Of all the patients, 13 were pure SPC in suit, whereas 17 were microinvasive SPC. The detection rates of US, DM, and magnetic resonance imaging for SPC were 30%, 50%, and 100%, respectively, and there were no specific imaging features on DM and US. The most common MRM appearances were located in the retroareolar area (16/30, 53.34%) with T2WI hyperintensity (24/30, 80%) and ductal ectasia (18/30, 60%). Non-mass enhancement of a linear or segmental distribution (17/18, 94.44%) together with clumped enhancement (12/18, 66.66%) and mass with a rim (6/12, 50%) or heterogeneous (6/12, 50%) enhancement were 2 of the typical enhancement features of SPC. Compared with pure SPC, SPC with microinvasive showed larger size of the lesion (t = 1.083, P = 0.026).

Conclusion: Although SPC was difficult to detect in both DM and US, MRM gave better detection of this rare tumor. The MRM characteristics of SPC were distinct and highly similar to its clinicopathological features.
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http://dx.doi.org/10.1097/RCT.0000000000000745DOI Listing
September 2018

Radiomic features of pretreatment MRI could identify T stage in patients with rectal cancer: Preliminary findings.

J Magn Reson Imaging 2018 Feb 13. Epub 2018 Feb 13.

Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.

Background: Recent studies have shown that magnetic resonance (MR) radiomic analysis is feasible and has some value in identifying tumor characteristics, but there are few data regarding the role of MR-based radiomic features in rectal cancer.

Purpose: The aim of this study was to determine whether radiomic features extracted from T -weighted imaging (T WI) can identify pathological features in rectal cancer.

Study Type: Retrospective study.

Population/subjects: A cohort comprising 119 rectal cancer patients who underwent surgery between January 2015 and November 2016.

Field Strength/sequence: 3.0T, axial high-resolution T -weighted turbo spin echo (TSE) sequence.

Assessment: Patients were classified according to pathological features such as T stage, N stage, perineural invasion, histological grade, lymph-vascular invasion, tumor deposits, and circumferential resection margin (CRM). The whole tumor volume (WTV) was distinguished, and segments were quantified on axial high-resolution T WI by a radiologist. A total of 256 radiomic features were extracted.

Statistical Tests: To achieve reliable results, cluster analysis and least absolute shrinkage and selection operator (LASSO) were implemented. In the cluster analysis, the patients were divided into two groups, and chi-square tests were performed to investigate the relationship between the pathological features and the radiomic-based clusters. The area under the curve (AUC) was calculated to evaluate the predictability of the model in the LASSO analysis.

Results: The cluster results revealed that patients could be stratified into two groups, and the chi-square test results indicated that the pT stage was correlated with the radiomic feature cluster results (P = 0.002). The prediction model AUC for the diagnostic T stage was 0.852 (95% confidence interval: 0.677-1; sensitivity: 79.0%, specificity: 82.0%).

Data Conclusion: The use of MRI-derived radiomic features to identify the T stage is feasible in rectal cancer.

Level Of Evidence: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018.
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http://dx.doi.org/10.1002/jmri.25969DOI Listing
February 2018

The Assessment of Background Parenchymal Enhancement (BPE) in a High-Risk Population: What Causes BPE?

Transl Oncol 2018 Apr 28;11(2):243-249. Epub 2018 Jan 28.

Institute of Clinical Radiology and Nuclear Medicine, University Medical Centre Mannheim, Medical Faculty Mannheim-University of Heidelberg. Electronic address:

Objective: To investigate promoting factors for background parenchymal enhancement (BPE) in MR mammography (MRM).

Methods: 146 patients were retrospectively evaluated, including 91 high-risk patients (50 BRCA patients, 41 patients with elevated lifetime risk). 56 screening patients were matched to the high-risk cases on the basis of age. The correlation of BPE with factors such as fibroglandular tissue (FGT), age, menopausal status, breast cancer, high-risk precondition as well as motion were investigated using linear regression.

Results: BPE positively correlated with FGT (P<.001) and negatively correlated with menopausal status (P<.001). Cancer did not show an effect on BPE (P>.05). A high-risk precondition showed a significant impact on the formation of BPE (P<.05). However, when corrected for motion, the correlation between BPE and a high-risk precondition became weak and insignificant, and a highly significant association between BPE and motion was revealed (P<.01).

Conclusion: BPE positively correlated with FGT and negatively correlated with age. Cancer did not have an effect on BPE. A high-risk precondition appears to have a negative effect on BPE. However, when corrected for motion, high-risk preconditions became insignificant. Technical as well as physiological influences seem to play an important role in the formation of BPE.
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http://dx.doi.org/10.1016/j.tranon.2017.12.006DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5884181PMC
April 2018

Decreased background parenchymal enhancement of the contralateral breast after two cycles of neoadjuvant chemotherapy is associated with tumor response in HER2-positive breast cancer.

Acta Radiol 2018 Jul 24;59(7):806-812. Epub 2017 Oct 24.

1 Department of Radiology, Fudan University Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, PR China.

Background Several recent studies have focused on the association between background parenchymal enhancement (BPE) and tumor response to neoadjuvant chemotherapy (NAC), but early prediction of tumor response based on BPE has yet not been investigated. Purpose To retrospectively investigate whether changes in the BPE of the contralateral breast following NAC could help predict tumor response in early stage HER2-positive breast cancer. Material and Methods Data from 71 patients who were diagnosed with unilateral HER2 positive breast cancer and then underwent NAC with trastuzumab before surgery were analyzed retrospectively. Two experienced radiologists independently categorized the patients' levels of BPE of the contralateral breast into four categories (1 = minimal, 2 = mild, 3 = moderate, 4 = marked) at baseline and after the second cycle of NAC. After undergoing surgery, 34 patients achieved pathologic complete response (pCR) and 37 patients had residual disease (non-pCR). The association between BPE and histopathologic tumor response was analyzed. Result The level of BPE was higher in premenopausal than post-menopausal women both at baseline and after the second cycle of NAC ( P < 0.005). A significant reduction in BPE ( P < 0.001) was observed after the second NAC cycle; however, a more obvious decrease in BPE was identified in premenopausal relative to post-menopausal women ( P = 0.041). No significant association was identified between pCR and baseline BPE ( P = 0.287). However, after the second NAC cycle, decreased BPE was significantly associated with pCR ( P = 0.003). Conclusion For HER2-positive patients, changes in BPE may serve as an additional imaging biomarker of treatment response at an early stage.
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http://dx.doi.org/10.1177/0284185117738560DOI Listing
July 2018

Association Between Background Parenchymal Enhancement and Pathologic Complete Remission Throughout the Neoadjuvant Chemotherapy in Breast Cancer Patients.

Transl Oncol 2017 Oct 12;10(5):786-792. Epub 2017 Aug 12.

Department of Radiology, Fudan University Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, PR China. Electronic address:

Purpose: To retrospectively investigate the quantitative background parenchymal enhancement (BPE) of the contralateral normal breast in patients with unilateral invasive breast cancer throughout multiple monitoring points of neoadjuvant chemotherapy (NAC) and to further determine whether BPE is associated with tumor response, especially at the early stage of NAC.

Materials And Methods: A total of 90 patients with unilateral breast cancer who then received six or eight cycles of NAC before surgery were analyzed retrospectively. BPE was measured in dynamic contrast-enhanced MRI at baseline and after 2nd, 4th, and 6th NAC, respectively. Correlation between BPE and tumor size was analyzed, and the association between pathologic complete remission (pCR) and BPE was also analyzed.

Results: The BPE of contralateral normal breast showed a constant reduction throughout NAC therapy regardless of the menopausal status (P<.001 in all). Both the BPEs and the changes of BPE in each of the three monitoring points were significantly correlated with those in tumor size (P<.05 in all), and the reduction of BPE after 2nd NAC had the largest diagnostic value for pCR (AUC=0.726, P<.001), particularly in hormonal receptor (HR)-negative patients (OR=0.243, 95%CI=0.083 to 0.706, P=.009).

Conclusion: The BPE of contralateral normal breast had a constant decreased tendency similar to the change of tumor size in NAC. Reduction of BPE at the early stage of NAC was positively associated with pCR, especially in HR-negative status.
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http://dx.doi.org/10.1016/j.tranon.2017.07.005DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5554960PMC
October 2017