Publications by authors named "Bihong T Chen"

41 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

Occult primary white matter impairment in Leber hereditary optic neuropathy.

Eur J Neurol 2021 Jun 24. Epub 2021 Jun 24.

Department of Radiology & Tianjin Key Lab of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China.

Background And Purpose: Leber hereditary optic neuropathy (LHON) is a disease maternally inherited from mitochondria that predominantly impairs the retinal ganglion cells and their axons. To identify whether occult brain white matter (WM) impairment is involved, a voxel-based analysis (VBA) of diffusion metrics was carried out in LHON patients with normal-appearing brain parenchyma.

Methods: Fifty-four symptomatic LHON patients (including 22 acute LHON with vision loss for ≤12 months, and 32 chronic LHON) without any visible brain lesions and 36 healthy controls (HCs) were enrolled in this study. VBA was applied to quantify the WM microstructural changes of LHON patients. Finally, the associations of the severity of WM impairment with disease duration and ophthalmologic deficits were assessed.

Results: Compared with the HCs, the average retinal nerve fiber layer (RNFL) thickness was significantly reduced in patients with chronic LHON, whereas it was increased in patients with acute LHON (p < 0.05, corrected). VBA identified significantly decreased fractional anisotropy widely in WM in both the acute and chronic LHON patients, including the left anterior thalamic radiation and superior longitudinal fasciculus, and bilateral corticospinal tract, dentate nuclei, inferior longitudinal fasciculus, forceps major, and optic radiation (OR; p < 0.05, corrected). The integrity of most WM structures (except for the OR) was correlated with neither disease duration nor RNFL thickness (p > 0.05, corrected).

Conclusions: Occult primary impairment of widespread brain WM is present in LHON patients. The coexisting primary and secondary WM impairment may jointly contribute to the pathological process of LHON.
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http://dx.doi.org/10.1111/ene.14995DOI Listing
June 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

First Multimodal, Three-Dimensional, Image-Guided Total Marrow Irradiation Model for Preclinical Bone Marrow Transplantation Studies.

Int J Radiat Oncol Biol Phys 2021 Jun 11. Epub 2021 Jun 11.

Department of Radiation Oncology, City of Hope Medical Center, Duarte, California; Beckman Research Institute of City of Hope, Duarte, California; Department of Radiation Oncology, University of Minnesota, Minneapolis, Minnesota. Electronic address:

Purpose: Total marrow irradiation (TMI) has significantly advanced radiation conditioning for hematopoietic cell transplantation in hematologic malignancies by reducing conditioning-induced toxicities and improving survival outcomes in relapsed/refractory patients. However, the relapse rate remains high, and the lack of a preclinical TMI model has hindered scientific advancements. To accelerate TMI translation to the clinic, we developed a TMI delivery system in preclinical models.

Methods And Materials: A Precision X-RAD SmART irradiator was used for TMI model development. Images acquired with whole-body contrast-enhanced computed tomography (CT) were used to reconstruct and delineate targets and vital organs for each mouse. Multiple beam and CT-guided Monte Carlo-based plans were performed to optimize doses to the targets and to vary doses to the vital organs. Long-term engraftment and reconstitution potential were evaluated by a congenic bone marrow transplantation (BMT) model and serial secondary BMT, respectively. Donor cell engraftment was measured using noninvasive bioluminescence imaging and flow cytometry.

Results: Multimodal imaging enabled identification of targets (skeleton and spleen) and vital organs (eg, lungs, gut, liver). In contrast to total body irradiation (TBI), TMI treatment allowed variation of radiation dose exposure to organs relative to the target dose. Dose reduction mirrored that in clinical TMI studies. Similar to TBI, mice treated with different TMI regimens showed full long-term donor engraftment in primary BMT and second serial BMT. The TBI-treated mice showed acute gut damage, which was minimized in mice treated with TMI.

Conclusions: A novel multimodal image guided preclinical TMI model is reported here. TMI conditioning maintained long-term engraftment with reconstitution potential and reduced organ damage. Therefore, this TMI model provides a unique opportunity to study the therapeutic benefit of reduced organ damage and BM dose escalation to optimize treatment regimens in BMT and hematologic malignancies.
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http://dx.doi.org/10.1016/j.ijrobp.2021.06.001DOI Listing
June 2021

Phase fMRI defines brain resting-state functional hubs within central and posterior regions.

Brain Struct Funct 2021 Jul 29;226(6):1925-1941. Epub 2021 May 29.

Department of Diagnostic Radiology, City of Hope National Medical Center, Duarte, CA, 91010, USA.

From a brain functional connectivity (FC) matrix, we can identify the hub nodes by a new method of eigencentrality mapping, which not only counts for one node's centrality but also all other nodes' centrality values through correlation connections in an eigenvector of the FC matrix. For the resting-state functional MRI (fMRI) data (complex-valued EPI images in nature), both magnitude and phase images are useful for brain FC analysis. We herein report on brain functional hubness analysis by constructing the FC matrix from phase fMRI data and identifying the hub nodes by eigencentrality mapping. In our study, we collected a cohort of 160 complex-valued fMRI dataset (consisting of magnitude and phase in pairs), and performed independent component analysis (ICA), FC matrix calculation (in size of 50 × 50) and FC matrix eigen decomposition; thereby obtained the 50-node eigencentrality values in the eigenvector associated with the largest eigenvalue. We also compared the hub structures inferred from FC matrices under different thresholding. Alternatively, we obtained the geometric hubs among p value the 50 nodes involved in the FC matrix through the use of harmonic centrality metric. Our results showed that phase fMRI data analysis defines the resting-state brain functional hubs primarily in the central region (subcortex) and the posterior region (parieto-occipital lobes and cerebella). The brain central hubness was supported by the geometric central hubness, which, however, is distinct from the magnitude-inferred hubness in brain superior region (frontal and parietal lobes). Our findings pose a new understanding of (or a debate over) brain functional connectivity architecture.
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http://dx.doi.org/10.1007/s00429-021-02301-zDOI Listing
July 2021

Cognitive Function in Older Adults With Cancer: Assessment, Management, and Research Opportunities.

J Clin Oncol 2021 Jul 27;39(19):2138-2149. Epub 2021 May 27.

Department of Surgery, Cancer Control, University of Rochester Medical Center, Wilmot Cancer Institute, Rochester, NY.

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http://dx.doi.org/10.1200/JCO.21.00239DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8260910PMC
July 2021

Effect of chemotherapy on default mode network connectivity in older women with breast cancer.

Brain Imaging Behav 2021 May 21. Epub 2021 May 21.

Center for Cancer and Aging, City of Hope National Medical Center, Duarte, CA, 91010, USA.

Chemotherapy may impair cognition and contribute to accelerated aging. The purpose of this study was to assess the effects of chemotherapy on the connectivity of the default mode network (DMN) in older women with breast cancer. This prospective longitudinal study enrolled women aged ≥ 60 years with stage I-III breast cancer (CTx group) and matched healthy controls (HC group). Study assessments, consisting of resting-state functional MRI (rs-fMRI) and the Picture Sequence Memory (psm) test for episodic memory from the NIH Toolbox for Cognition, were obtained at baseline and within one month after the completion of chemotherapy for the CTx group and at matched intervals for the HC group. Two-sample t-test and FDR multiple comparison were used for statistical inference. Our analysis of the CTx group (N = 19; 60-82 years of age, mean = 66.6, SD = 5.24) compared to the HC group (N = 14; 60-78 years of age, mean = 68.1, SD = 5.69) revealed weaker DMN subnetwork connectivity in the anterior brain but stronger connectivity in the posterior brain at baseline. After chemotherapy, this pattern was reversed, with stronger anterior connectivity and weaker posterior connectivity. In addition, the meta-level functional network connectivity (FNC) among the DMN subnetworks after chemotherapy was consistently weaker than the baseline FNC as seen in the couplings between anterior cingulate cortex (ACC) and retrosplenial (rSplenia) region, with ΔFNC('ACC','rSplenia')=-0.14, t value=-2.44, 95 %CI=[-0.27,-0.10], p<0.05). The baseline FNC matrices of DMN subnetworks were correlated with psm scores (corr = 0.58, p < 0.05). Our results support DMN alterations as a potential neuroimaging biomarker for cancer-related cognitive impairment and accelerated aging.
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http://dx.doi.org/10.1007/s11682-021-00475-yDOI Listing
May 2021

Predicting Survival Duration With MRI Radiomics of Brain Metastases From Non-small Cell Lung Cancer.

Front Oncol 2021 5;11:621088. Epub 2021 Mar 5.

Department of Medical Oncology and Therapeutics Research, City of Hope Comprehensive Cancer Center and Beckman Research Institute, Duarte, CA, United States.

Brain metastases are associated with poor survival. Molecular genetic testing informs on targeted therapy and survival. The purpose of this study was to perform a MR imaging-based radiomic analysis of brain metastases from non-small cell lung cancer (NSCLC) to identify radiomic features that were important for predicting survival duration. We retrospectively identified our study cohort via an institutional database search for patients with brain metastases from EGFR, ALK, and/or KRAS mutation-positive NSCLC. We segmented the brain metastatic tumors on the brain MR images, extracted radiomic features, constructed radiomic scores from significant radiomic features based on multivariate Cox regression analysis ( < 0.05), and built predictive models for survival duration. Of the 110 patients in the cohort (mean age 57.51 ± 12.32 years; range: 22-85 years, M:F = 37:73), 75, 26, and 15 had NSCLC with EGFR, ALK, and KRAS mutations, respectively. Predictive modeling of survival duration using both clinical and radiomic features yielded areas under the receiver operative characteristic curve of 0.977, 0.905, and 0.947 for the EGFR, ALK, and KRAS mutation-positive groups, respectively. Radiomic scores enabled the separation of each mutation-positive group into two subgroups with significantly different survival durations, i.e., shorter vs. longer duration when comparing to the median survival duration of the group. Our data supports the use of radiomic scores, based on MR imaging of brain metastases from NSCLC, as non-invasive biomarkers for survival duration. Future research with a larger sample size and external cohorts is needed to validate our results.
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http://dx.doi.org/10.3389/fonc.2021.621088DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7973105PMC
March 2021

Computed Tomography Radiomics for Predicting Pathological Grade of Renal Cell Carcinoma.

Front Oncol 2020 27;10:570396. Epub 2021 Jan 27.

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

Background: Clear cell renal cell carcinoma (ccRCC) is the most common renal cancer and it has the worst prognosis among all renal cancers. However, traditional radiological characteristics on computed tomography (CT) scans of ccRCC have been insufficient to predict the pathological grade of ccRCC before surgery.

Methods: Patients with ccRCC were retrospectively enrolled into this study and were separated into two groups according to the World Health Organization (WHO)/International Society of Urological Pathology (ISUP) grading system, i.e., low-grade (Grade I and II) group and high-grade (Grade III and IV) group. Traditional CT radiological characteristics such as tumor size, pre- and post-enhancing CT densities were assessed. In addition, radiomic texture analysis based on the CT imaging of the ccRCC were also performed. A CT-based machine learning method combining the traditional radiological characteristics and radiomic features was used in the predictive modeling for differentiating the low-grade from the high-grade ccRCC. Model performance was evaluated with the receiver operating characteristic curve (ROC) analysis.

Results: A total of 264 patients with pathologically confirmed ccRCC were included in this study. In this cohort, 206 patients had the low-grade tumors and 58 had the high-grade tumors. The model built with traditional radiological characteristics achieved an area under the curve (AUC) of 0.9175 (95% CI: 0.8765-0.9585) and 0.8088 (95% CI: 0.7064-0.9113) in differentiating the low-grade from the high-grade ccRCC for the training cohort and the validation cohort respectively. The model built with the radiomic textural features yielded an AUC value of 0.8170 (95% CI: 0.7353-0.8987) and 0.8017 (95% CI: 0.6878-0.9157) for the training cohort and the validation cohort, respectively. The combined model integrating both the traditional radiological characteristics and the radiomic textural features achieved the highest efficacy, with an AUC of 0.9235 (95% CI: 0.8646-0.9824) and an AUC of 0.9099 (95% CI: 0.8324-0.9873) for the training cohort and validation cohort, respectively.

Conclusion: We developed a machine learning radiomic model achieving a satisfying performance in differentiating the low-grade from the high-grade ccRCC. Our study presented a potentially useful non-invasive imaging-focused method to predict the pathological grade of renal cancers prior to surgery.
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http://dx.doi.org/10.3389/fonc.2020.570396DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7873602PMC
January 2021

Spinal Manipulative Therapy Alters Brain Activity in Patients With Chronic Low Back Pain: A Longitudinal Brain fMRI Study.

Front Integr Neurosci 2020 19;14:534595. Epub 2020 Nov 19.

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

Spinal manipulative therapy (SMT) helps to reduce chronic low back pain (cLBP). However, the underlying mechanism of pain relief and the neurological response to SMT remains unclear. We utilized brain functional magnetic resonance imaging (fMRI) upon the application of a real-time spot pressure mechanical stimulus to assess the effects of SMT on patients with cLBP. Patients with cLBP (Group 1, = 14) and age-matched healthy controls without cLBP (Group 2, = 20) were prospectively enrolled. Brain fMRI was performed for Group 1 at three time points: before SMT (TP1), after the first SMT session (TP2), and after the sixth SMT session (TP3). The healthy controls (Group 2) did not receive SMT and underwent only one fMRI scan. During fMRI scanning, a real-time spot pressure mechanical stimulus was applied to the low back area of all participants. Participants in Group 1 completed clinical questionnaires assessing pain and quality of life using a visual analog scale (VAS) and the Chinese Short Form Oswestry Disability Index (C-SFODI), respectively. Before SMT (TP1), there were no significant differences in brain activity between Group 1 and Group 2. After the first SMT session (TP2), Group 1 showed significantly greater brain activity in the right parahippocampal gyrus, right dorsolateral prefrontal cortex, and left precuneus compared to Group 2 ( < 0.05). After the sixth SMT session (TP3), Group 1 showed significantly greater brain activity in the posterior cingulate gyrus and right inferior frontal gyrus compared to Group 2 ( < 0.05). After both the first and sixth SMT sessions (TP2 and TP3), Group 1 had significantly lower VAS pain scores and C-SFODI scores than at TP1 ( < 0.001). We observed alterations in brain activity in regions of the default mode network in patients with cLBP after SMT. These findings suggest the potential utility of the default mode network as a neuroimaging biomarker for pain management in patients with cLBP. Chinese Clinical Trial Registry, identifier ChiCTR1800015620.
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http://dx.doi.org/10.3389/fnint.2020.534595DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7710896PMC
November 2020

Incorporating SULF1 polymorphisms in a pretreatment CT-based radiomic model for predicting platinum resistance in ovarian cancer treatment.

Biomed Pharmacother 2021 Jan 20;133:111013. Epub 2020 Nov 20.

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

Objective: Early detection of platinum resistance for ovarian cancer treatment remains challenging. This study aims to develop a machine learning model incorporating genomic data such as Single-Nucleotide Polymorphisms (SNPs) of Human Sulfatase 1 (SULF1) with a CT radiomic model based on pre-treatment CT images, to predict platinum resistance for ovarian cancer (OC) treatment.

Methods: A cohort of 102 patients with pathologically confirmed OC was retrospectively enrolled into this study from January 2006 to February 2018. All patients had platinum-based chemotherapy after maximal cyto-reductive surgery. This cohort was separated into two groups according to treatment response, i.e., the group with platinum-resistant disease (PR group) and the group with platinum-sensitive disease (PS group). We genotyped 12 SNPs of SULF1 for all OC patients using Mass Array Method. Radiomic features, SNP data and clinicopathological data of the 102 patients were used to build the differentiation models. The study participants were divided into two cohorts: the training cohort (n = 71) and the validation cohort (n = 31). Feature selection and predictive modeling were performed using least absolute shrinkage and selection operator (LASSO), Random Forest Classifier and Support Vector Machine methods. Model performance for predicting platinum resistance was assessed with respect to its calibration, discrimination, and clinical application.

Results: For prediction of platinum resistance, the approach combining the radiomics, clinicopathological data and SNP data demonstrated higher classification efficiency, with an AUC value of 0.993 (95 % CI: 0.83 to 0.98) in the training cohort and 0.967 (95 % CI: 0.83 to 0.98) in validation cohort, than the performance with only the SNPs of SULF1 model (AUC: training, 0.843 [95 %CI: 0.738-0.948]; validation, 0.815 [0.601-1.000]), or with only the radiomic model (AUC: training, 0.874 [95 %CI: 0.789-0.960]; validation, 0.832 [95 %CI: 0.687-0.976]). This integrated approach also showed good calibration and favorable clinical utility.

Conclusions: A predictive model combining pretreatment CT radiomics with genomic data such as SNPs of SULF1 could potentially help to predict platinum resistance in ovarian cancer treatment.
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http://dx.doi.org/10.1016/j.biopha.2020.111013DOI Listing
January 2021

Differentiating Peripherally-Located Small Cell Lung Cancer From Non-small Cell Lung Cancer Using a CT Radiomic Approach.

Front Oncol 2020 22;10:593. Epub 2020 Apr 22.

Department of Medical Oncology and Therapeutics Research, City of Hope Comprehensive Cancer Center and Beckman Research Institute, Duarte, CA, United States.

Lung cancer can be classified into two main categories: small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC), which are different in treatment strategy and survival probability. The lung CT images of SCLC and NSCLC are similar such that their subtle differences are hardly visually discernible by the human eye through conventional imaging evaluation. We hypothesize that SCLC/NSCLC differentiation could be achieved via computerized image feature analysis and classification in feature space, as termed a radiomic model. The purpose of this study was to use CT radiomics to differentiate SCLC from NSCLC adenocarcinoma. Patients with primary lung cancer, either SCLC or NSCLC adenocarcinoma, were retrospectively identified. The post-diagnosis pre-treatment lung CT images were used to segment the lung cancers. Radiomic features were extracted from histogram-based statistics, textural analysis of tumor images and their wavelet transforms. A minimal-redundancy-maximal-relevance method was used for feature selection. The predictive model was constructed with a multilayer artificial neural network. The performance of the SCLC/NSCLC adenocarcinoma classifier was evaluated by the area under the receiver operating characteristic curve (AUC). Our study cohort consisted of 69 primary lung cancer patients with SCLC ( = 35; age mean ± SD = 66.91± 9.75 years), and NSCLC adenocarcinoma ( = 34; age mean ± SD = 58.55 ± 11.94 years). The SCLC group had more male patients and smokers than the NSCLC group ( < 0.05). Our SCLC/NSCLC classifier achieved an overall performance of AUC of 0.93 (95% confidence interval = [0.85, 0.97]), sensitivity = 0.85, and specificity = 0.85). Adding clinical data such as smoking history could improve the performance slightly. The top ranking radiomic features were mostly textural features. Our results showed that CT radiomics could quantitatively represent tumor heterogeneity and therefore could be used to differentiate primary lung cancer subtypes with satisfying results. CT image processing with the wavelet transformation technique enhanced the radiomic features for SCLC/NSCLC classification. Our pilot study should motivate further investigation of radiomics as a non-invasive approach for early diagnosis and treatment of lung cancer.
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http://dx.doi.org/10.3389/fonc.2020.00593DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7188953PMC
April 2020

Radiomic prediction of mutation status based on MR imaging of lung cancer brain metastases.

Magn Reson Imaging 2020 06 13;69:49-56. Epub 2020 Mar 13.

Department of Medical Oncology and Therapeutics Research, City of Hope Comprehensive Cancer Center and Beckman Research Institute, Duarte 91010, CA, United States.

Lung cancer metastases comprise most of all brain metastases in adults and most brain metastases are diagnosed by magnetic resonance (MR) scans. The purpose of this study was to conduct an MR imaging-based radiomic analysis of brain metastatic lesions from patients with primary lung cancer to classify mutational status of the metastatic disease. We retrospectively identified lung cancer patients with brain metastases treated at our institution between 2009 and 2017 who underwent genotype testing of their primary lung cancer. Brain MR Images were used for segmentation of enhancing tumors and peritumoral edema, and for radiomic feature extraction. The most relevant radiomic features were identified and used with clinical data to train random forest classifiers to classify the mutation status. Of 110 patients in the study cohort (mean age 57.51 ± 12.32 years; M: F = 37:73), 75 had an EGFR mutation, 21 had an ALK translocation, and 15 had a KRAS mutation. One patient had both ALK translocation and EGFR mutation. Majority of radiomic features most relevant for mutation classification were textural. Model building using both radiomic features and clinical data yielded more accurate classifications than using either alone. For classification of EGFR, ALK, and KRAS mutation status, the model built with both radiomic features and clinical data resulted in area-under-the-curve (AUC) values based on cross-validation of 0.912, 0.915, and 0.985, respectively. Our study demonstrated that MR imaging-based radiomic analysis of brain metastases in patients with primary lung cancer may be used to classify mutation status. This approach may be useful for devising treatment strategies and informing prognosis.
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http://dx.doi.org/10.1016/j.mri.2020.03.002DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7237274PMC
June 2020

Machine learning-based multiparametric MRI radiomics for predicting the aggressiveness of papillary thyroid carcinoma.

Eur J Radiol 2020 Jan 20;122:108755. Epub 2019 Nov 20.

Department of Diagnostic Radiology, City of Hope National Medical Center, Duarte, CA, United States. Electronic address:

Purpose: To investigate the predictive capability of machine learning-based multiparametric magnetic resonance (MR) imaging radiomics for evaluating the aggressiveness of papillary thyroid carcinoma (PTC) preoperatively.

Methods: This prospective study enrolled consecutive patients who underwent neck MR scans and subsequent thyroidectomy during the study interval. The diagnosis and aggressiveness of PTC were determined by pathological evaluation of thyroidectomy specimens. Thyroid nodules were segmented manually on the MR images, and radiomic features were then extracted. Predictive machine learning modelling was used to evaluate the prediction of PTC aggressiveness. Area under the receiver operating characteristic curve (AUC) values for the model performance were obtained for radiomic features, clinical characteristics, and combinations of radiomic features and clinical characteristics.

Results: The study cohort included 120 patients with pathology-confirmed PTC (training cohort: n = 96; testing cohort: n = 24). A total of 1393 features were extracted from T2-weighted, apparent diffusion coefficient (ADC) and contrast-enhanced T1-weighted MR images for each patient. The combination of Least Absolute Shrinkage and Selection Operator for radiomic feature selection and Gradient Boosting Classifier for classifying PTC aggressiveness achieving the AUC of 0.92. In contrast, clinical characteristics alone poorly predicted PTC aggressiveness, with an AUC of 0.56.

Conclusions: Our study showed that machine learning-based multiparametric MR imaging radiomics could accurately distinguish aggressive from non-aggressive PTC preoperatively. This approach may be helpful for informing treatment strategies and prognosis of patients with aggressive PTC.
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http://dx.doi.org/10.1016/j.ejrad.2019.108755DOI Listing
January 2020

Effects of chemotherapy on aging white matter microstructure: A longitudinal diffusion tensor imaging study.

J Geriatr Oncol 2020 03 2;11(2):290-296. Epub 2019 Nov 2.

Center for Cancer and Aging, City of Hope National Medical Center, Duarte, CA 91010, United States; Department of Supportive Care Medicine, City of Hope National Medical Center, Duarte, CA 91010, United States. Electronic address:

Objective: We aimed to use diffusion tensor imaging (DTI) to detect alterations in white matter microstructure in older patients with breast cancer receiving chemotherapy.

Methods: We recruited women age ≥60 years with stage I-III breast cancer (chemotherapy [CT] group; n = 19) to undergo two study assessments: at baseline and within one month after chemotherapy. Each assessment consisted of a brain magnetic resonance imaging scan with DTI and neuropsychological (NP) testing using the National Institutes of Health (NIH) Toolbox Cognition Battery. An age- and sex-matched group of healthy controls (HC, n = 14) underwent the same assessments at matched intervals. Four DTI parameters (fractional anisotropy [FA], mean diffusivity [MD], axial diffusivity [AD], and radial diffusivity [RD]) were calculated and correlated with NP testing scores.

Results: For CT group but not HCs, we detected statistically significant increases in MD and RD in the genu of the corpus callosum from time point 1 to time point 2 at p < 0.01, effect size:0.3655 and 0.3173, and 95% confidence interval: from 0.1490 to 0.5821, and from 0.1554 to 0.4792, for MD and RD respectively. AD values increased for the CT group and decreased for the HC group over time, resulting in significant between-group differences (p = 0.0056, effect size:1.0215, 95% confidence interval: from 0.2773 to 1.7657). There were no significant correlations between DTI parameters and NP scores (p > 0.05).

Conclusions: We identified alterations in white matter microstructures in older women with breast cancer undergoing chemotherapy. These findings may potentially serve as neuroimaging biomarkers for identifying cognitive impairment in older adults with cancer.
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http://dx.doi.org/10.1016/j.jgo.2019.09.016DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7054164PMC
March 2020

Sensorimotor and pain-related alterations of the gray matter and white matter in Type 2 diabetic patients with peripheral neuropathy.

Hum Brain Mapp 2020 02 29;41(3):710-725. Epub 2019 Oct 29.

Department of Radiology, Xiangya Hospital, Central South University, Changsha, China.

Although diabetic peripheral neuropathy (DPN) has long been considered a disease of the peripheral nervous system, recent neuroimaging studies have shown that alterations in the central nervous system may play a crucial role in its pathogenesis. Here, we used surface-based morphometry (SBM) and tract-based spatial statistics (TBSS) to investigate gray matter (GM) and white matter (WM) differences between patients with DPN (n = 67, 44 painless and 23 painful) and healthy controls (HCs; n = 88). Compared with HCs, patients with DPN exhibited GM abnormalities in the pre- and postcentral gyrus and in several deep GM nuclei (caudate, putamen, medial pallidum, thalamus, and ventral nuclear). They also exhibited altered WM tracts (corticospinal tract, spinothalamic tract, and thalamocortical projecting fibers). These findings suggest impaired motor and somatosensory pathways in DPN. Further, patients with DPN (particularly painful DPN) exhibited morphological differences in the cingulate, insula, prefrontal cortex, and thalamus, as well as impaired WM integrity in periaqueductal WM and internal and external capsules. This suggests pain-perception/modulation pathways are altered in painful DPN. Intermodal correlation analyses found that the morphological indices of the brain regions identified by the SBM analysis were significantly correlated with the fractional anisotropy of brain regions identified by the TBSS analysis, suggesting that the GM and WM alterations were tightly coupled. Overall, our study showed sensorimotor and pain-related GM and WM alterations in patients with DPN, which might be involved in the development of DPN.
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http://dx.doi.org/10.1002/hbm.24834DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7268085PMC
February 2020

Evaluation of skeletal muscle perfusion in a canine hind limb ischemia model using CT perfusion imaging.

Diagn Interv Radiol 2020 Jan;26(1):28-33

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

Purpose: To evaluate skeletal muscle perfusion in a canine hind limb ischemia model using CT perfusion imaging (CTPI).

Methods: Twelve beagles underwent embolization at the branch of the left deep femoral artery. The right hind limbs were used as controls. CTPI was performed immediately after embolization. The perfusion parameters of the regions of interest (ROI), including blood volume (BV), blood flow (BF), mean transit time (MTT) and permeability (PMB), were obtained in both the lateral and posterior hind limb muscle groups.

Results: After embolization, the BV, BF and PMB values in the lateral muscles of the left hind limbs were significantly lower than those in the right hind limbs (P > 0.05), and the MTT was significantly prolonged (P > 0.05). The values for BV, BF, MTT and PMB in the posterior muscles of the left hind limbs were not significantly different from those in the right hind limbs (P > 0.05). The values for BV, BF and PMB in the lateral muscles of the left hind limbs were significantly lower than those in the posterior muscles of the left hind limbs (P > 0.05).

Conclusion: CTPI could be used to evaluate skeletal muscle perfusion in a canine model, which may have clinical relevance in lower limb ischemia and vascular reconstruction.
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http://dx.doi.org/10.5152/dir.2019.18478DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7075588PMC
January 2020

Barriers to clinical trial enrollment of older adults with cancer: A qualitative study of the perceptions of community and academic oncologists.

J Geriatr Oncol 2020 03 31;11(2):327-334. Epub 2019 Jul 31.

Department of Supportive Care Medicine, City of Hope, Duarte, CA, United States of America.

Objectives: Oncologists can be one of the major barriers to older adult's participation in research. Multiple studies have described academic clinicians' concerns for not enrolling older adults onto trials. Although the majority of older adults receive their cancer care in the community, few studies have examined the unique challenges that community oncologists face and how they differ from oncologist-related barriers in academia.

Methods: Semi-structured interviews were conducted by telephone or face-to-face with 44 medical oncologists (24 academic-based and 20 community-based) at City of Hope from March to June 2018. Interviews explored oncologists' perceptions of barriers to clinical trial enrollment of older adults with cancer. Data were analyzed using qualitative content analysis.

Results: Of the 44 participants, 36% were women and 68% were in practice for >10 years. Among the entire sample, stringent eligibility criteria (n = 20) and oncologist concerns for treatment toxicities (n = 15) were the most commonly cited barriers. Compared to academic oncologists, community oncologists more often cited patient attitudes, beliefs, and understanding (n = 9 vs. n = 2) and caregiver burden (n = 6 vs. n = 0). In contrast, compared to community oncologists, academic oncologists more often cited oncologist bias (n = 10 vs. n = 3) and insufficient time/support (n = 4 vs. n = 1).

Conclusions: Differences in perceptions among academic and community oncologists about trials suggest that barriers are multifaceted, complex, and vary by practice setting. Interventions to increase trial accrual among older adults with cancer may benefit from being tailored to address the unique barriers of different practice settings.
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http://dx.doi.org/10.1016/j.jgo.2019.07.017DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6989372PMC
March 2020

Experimental supporting data on evaluation of skeletal muscle perfusion in canine hind limb ischemia model using color-coded digital subtraction angiography.

Data Brief 2019 Aug 7;25:103737. Epub 2019 Mar 7.

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

In this article, we presented the detailed measurements and comparisons of skeletal muscle perfusion parameters in a canine hind limb ischemia model. Data presented here is related to and supportive to the research article "Evaluation of skeletal muscle perfusion in canine hind limb ischemia model using color-coded digital subtraction angiography" [1], where interpretation of the research data presented here is available.
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http://dx.doi.org/10.1016/j.dib.2019.103737DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6603800PMC
August 2019

MRI-Based Radiomics Predicts Tumor Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer.

Front Oncol 2019 26;9:552. Epub 2019 Jun 26.

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

Conventional methods for predicting treatment response to neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC) are limited. This study retrospectively recruited 134 LARC patients who underwent standard nCRT followed by total mesorectal excision surgery in our institution. Based on pre-operative axial T2-weighted images, machine learning radiomics was performed. A receiver operating characteristic (ROC) curve was performed to test the efficiencies of the predictive model. Among the 134 patients, 32 (23.9%) achieved pathological complete response (pCR), 69 (51.5%) achieved a good response, and 91 (67.9%) achieved down-staging. For prediction of pCR, good-response, and down-staging, the predictive model demonstrated high classification efficiencies, with an AUC value of 0.91 (95% CI: 0.83-0.98), 0.90 (95% CI: 0.83-0.97), and 0.93 (95% CI: 0.87-0.98), respectively. Our machine learning radiomics model showed promise for predicting response to nCRT in patients with LARC. Our predictive model based on the commonly used T2-weighted images on pelvic Magnetic Resonance Imaging (MRI) scans has the potential to be adapted in clinical practice. Methods for predicting the response of the locally advanced rectal cancer (LARC, T3-4, or N+) to neoadjuvant chemoradiotherapy (nCRT) is lacking. In the present study, we developed a new machine learning radiomics method based on T2-weighted images. As a non-invasive tool, this method facilitates prediction performance effectively. It achieves a satisfactory overall diagnostic accuracy for predicting of pCR, good response, and down-staging show an AUC of 0.908, 0.902, and 0.930 in LARC patients, respectively.
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http://dx.doi.org/10.3389/fonc.2019.00552DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6606732PMC
June 2019

Assessing Cerebral White Matter Microstructure in Children With Congenital Sensorineural Hearing Loss: A Tract-Based Spatial Statistics Study.

Front Neurosci 2019 21;13:597. Epub 2019 Jun 21.

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

Objectives: To assess the microstructural properties of cerebral white matter in children with congenital sensorineural hearing loss (CSNHL).

Methods: Children (>4 years of age) with profound CSNHL and healthy controls with normal hearing (the control group) were enrolled and underwent brain magnetic resonance imaging (MRI) scans with diffusion tensor imaging (DTI). DTI parameters including fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity were obtained from a whole-brain tract-based spatial statistics analysis and were compared between the two groups. In addition, a region of interest (ROI) approach focusing on auditory cortex, i.e., Heschl's gyrus, using visual cortex, i.e., forceps major as an internal control, was performed. Correlations between mean DTI values and age were obtained with the ROI method.

Results: The study cohort consisted of 23 children with CSHNL (11 boys and 12 girls; mean age ± SD: 7.21 ± 2.67 years; range: 4.1-13.5 years) and 18 children in the control group (11 boys and 7 girls; mean age ± SD: 10.86 ± 3.56 years; range: 4.5-15.3 years). We found the axial diffusivity values being significantly greater in the left anterior thalamic radiation, right corticospinal tract, and corpus callosum in the CSHNL group than in the control group ( < 0.05). Significantly higher radial diffusivity values in the white matter tracts were noted in the CSHNL group as compared to the control group ( < 0.05). The fractional anisotropy values in the Heschl's gyrus in the CSNHL group were lower compared to the control group ( = 0.0015). There was significant negative correlation between the mean fractional anisotropy values in Heschl's gyrus and age in the CSNHL group < 7 years of age ( = -0.59, = 0.004).

Conclusion: Our study showed higher axial and radial diffusivities in the children affected by CNHNL as compared to the hearing children. We also found lower fractional anisotropy values in the Heschl's gyrus in the CSNHL group. Furthermore, we identified negative correlation between the fractional anisotropy values and age up to 7 years in the children born deaf. Our study findings suggest that myelination and axonal structure may be affected due to acoustic deprivation. This information may help to monitor hearing rehabilitation in the deaf children.
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http://dx.doi.org/10.3389/fnins.2019.00597DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6598398PMC
June 2019

Intrinsic brain activity changes associated with adjuvant chemotherapy in older women with breast cancer: a pilot longitudinal study.

Breast Cancer Res Treat 2019 Jul 13;176(1):181-189. Epub 2019 Apr 13.

Center for Cancer and Aging, City of Hope National Medical Center, Duarte, CA, USA.

Purpose: Older cancer patients are at increased risk of cancer-related cognitive impairment. The purpose of this study was to assess the alterations in intrinsic brain activity associated with adjuvant chemotherapy in older women with breast cancer.

Methods: Chemotherapy treatment (CT) group included sixteen women aged ≥ 60 years (range 60-82 years) with stage I-III breast cancers, who underwent both resting-state functional magnetic resonance imaging (rs-fMRI) and neuropsychological testing with NIH Toolbox for Cognition before adjuvant chemotherapy, at time point 1 (TP1), and again within 1 month after completing chemotherapy, at time point 2 (TP2). Fourteen age- and sex-matched healthy controls (HC) underwent the same assessments at matched intervals. Three voxel-wise rs-fMRI parameters: amplitude of low-frequency fluctuation (ALFF), fractional ALFF (fALFF), and regional homogeneity, were computed at each time point. The changes in rs-fMRI parameters from TP1 to TP2 for each group, the group differences in changes (the CT group vs. the HC group), and the group difference in the baseline rs-fMRI parameters were assessed. In addition, correlative analysis between the rs-fMRI parameters and neuropsychological testing scores was also performed.

Results: In the CT group, one brain region, which included parts of the bilateral subcallosal gyri and right anterior cingulate gyrus, displayed increased ALFF from TP1 to TP2 (cluster p-corrected = 0.024); another brain region in the left precuneus displayed decreased fALFF from TP1 to TP2 (cluster level p-corrected = 0.025). No significant changes in the rs-fMRI parameters from TP1 to TP2 were observed in the HC group. Although ALFF and fALFF alterations were observed only in the CT group, none of the between-group differences in rs-fMRI parameter changes reached statistical significance.

Conclusions: Our study results of ALFF and fALFF alterations in the chemotherapy-treated women suggest that adjuvant chemotherapy may affect intrinsic brain activity in older women with breast cancer.
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http://dx.doi.org/10.1007/s10549-019-05230-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6551279PMC
July 2019

Transcatheter Thrombolysis with Percutaneous Transluminal Angioplasty Using a Trans-Brachial Approach to Treat Thrombosed Arteriovenous Fistulas.

Med Sci Monit 2019 Apr 13;25:2727-2734. Epub 2019 Apr 13.

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

BACKGROUND Arteriovenous fistulas (AVFs) are used to provide vascular access for hemodialysis in patients with end-stage renal failure. However, stenosis and thrombosis can compromise long-term AVF patency. The objective of this study was to evaluate catheter thrombolysis with percutaneous transluminal angioplasty (PTA), using a trans-brachial approach, for acutely thrombosed AVFs. MATERIAL AND METHODS This retrospective study examined 30 cases of AVF thrombosis treated between January 1, 2015 and January 1, 2017. All patients received transcatheter thrombolysis with PTA using a trans-brachial approach. AVF patency was assessed after 6 months. RESULTS Thrombolysis with PTA was performed at 2 to 72 h after diagnosis of AVF occlusion due to acute thrombosis, and AVF patency was restored in all patients. After 6 months, the primary and secondary patency rates were 76.7% and 93.3%, respectively. For type I stenosis, primary patency was achieved in 10 of 16 patients (62.5%) and secondary patency was achieved in 14 of 16 patients (87.5%). For type II stenosis, primary patency was achieved in 13 of 14 patients (92.9%) and secondary patency was achieved in 14 of 14 patients (100%). Comparing type I and II stenosis, a significant difference was detected in the rates of primary patency (odds ratio=0.909, 95% confidence interval 0.754-1.096, P=0.049), but not secondary patency (P=0.178), after 6 months. CONCLUSIONS Our study provides preliminary evidence that catheter-directed thrombolysis with PTA using a trans-brachial approach can achieve high patency rates when used to treat acutely thrombosed AVFs.
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http://dx.doi.org/10.12659/MSM.915755DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6476234PMC
April 2019

A Predictive Scoring Model for Short-Term Local Recurrent Nasopharyngeal Carcinoma Based on Magnetic Resonance Imaging.

Cancer Biother Radiopharm 2019 Mar 26;34(2):76-84. Epub 2018 Dec 26.

8 Department of Diagnostic Radiology, City of Hope National Medical Center, Duarte, California.

Objective: To predict the early identification of recurrence based on magnetic resonance imaging (MRI) in nasopharyngeal cancer (NPC) patients.

Methods: The clinical and MRI data of 215 patients with local recurrent NPC were retrospectively reviewed. Logistic regression analysis was performed to distinguish the independent risk factors for the short-term (less than 24 months) local recurrence of NPC. The predictive score model was based on the regression coefficients of significant independent variables.

Results: Residual disease in the nasopharyngeal cavity (NC), masticator space invasion (MSI), skull base bone erosion (SBBE), and MRI-detected cranial nerve invasion (MDCNI) were all significant independent risk factors for the short-term recurrence of NPC (p < 0.05). The receiver operating characteristic curve showed that the total score had a maximal AUC (area under the curve) value of 0.897, with a cutoff point of 10.50. The sensitivity and specificity were 79.4% and 80.5%, respectively.

Conclusion: Residual lesions in NC, MSI, SBBE, and MDCNI are independent risk factors in predicting the short-term recurrence of NPC. The authors' findings suggest that patients with a score of more than 10.50 points should be hypervigilant regarding the possibility of short-term recurrence.
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http://dx.doi.org/10.1089/cbr.2018.2531DOI Listing
March 2019

Evaluation of skeletal muscle perfusion in canine hind limb ischemia model using color-coded digital subtraction angiography.

Microvasc Res 2019 05 18;123:81-85. Epub 2018 Dec 18.

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

Objective: To evaluate perfusion alterations in skeletal muscle in a canine hind limb ischemia model using color-coded digital subtraction angiography (CC-DSA).

Methods: Twelve beagles underwent embolization at the branch of their left deep femoral artery. Right hind limbs were used as the control group. Angiography was performed before and immediately after embolization. Upon CC-DSA analysis, time to peak (TTP) was measured before embolization in both sides of the beagles' hind limbs at the middle iliac artery, and the distant, middle and proximal femoral artery. Regions of interest (ROI) peak and ROI peak time were symmetrically computed in proximal and distal thigh muscles before and immediately after embolization. The data were analyzed and compared using the Wilcoxon signed rank test.

Results: Before embolization, ROI peak in the proximal thigh was lower than in the ipsilateral distal thigh, whereas ROI peak time in the proximal thigh was longer than in the distal thigh. In the iliac femoral artery, there was no significant difference in ROI peak, ROI peak time, or TTP between right and left sides. After embolization, ROI peaks in proximal and distal skeletal muscles of the left hind limb were significantly lower than on the contralateral side. ROI peak time was significantly longer in the left proximal and left distal thigh compared to the contralateral side. There were no significant changes in ROI peak or ROI peak time in the right proximal and right distal thigh compared to pre-embolization values. Changes in ROI peak and ROI peak time were larger in the left proximal than in the left distal thigh.

Conclusion: CC-DSA provided real-time measurement of changes in vascular hemodynamics and skeletal muscle perfusion without increasing X-ray usage or contrast agent dose.
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http://dx.doi.org/10.1016/j.mvr.2018.12.003DOI Listing
May 2019

Radiomics improves efficiency for differentiating subclinical pheochromocytoma from lipid-poor adenoma: a predictive, preventive and personalized medical approach in adrenal incidentalomas.

EPMA J 2018 Dec 21;9(4):421-429. Epub 2018 Sep 21.

7Department of Radiology, Keck Medical Center of USC, Los Angeles, CA USA.

Objectives: This study aims to define a radiomic signature for pre-operative differentiation between subclinical pheochromocytoma (sPHEO) and lipid-poor adrenal adenoma (LPA) in adrenal incidentaloma. The goal was to apply a predictive, preventive, and personalized medical approach to the management of adrenal tumors.

Patients And Methods: This retrospective study consisted of 265 consecutive patients (training cohort, 212 (LPA, 145; sPHEO, 67); validation cohort, 53 (LPA, 36; sPHEO, 17)). Computed tomography (CT) imaging features were evaluated, including long diameter (LD), short diameter (SD), pre-enhanced CT value (CT), enhanced CT value (CT), shape, homogeneity, necrosis or cystic degeneration (N/C). Radiomic features were extracted and then were used to construct a radiomic signature (Rad-score) and radiomic nomogram. The area under the receiver operating characteristic curve (AUC) was used to evaluate their performance.

Results: Sixteen of three hundred forty candidate features were used to build a radiomic signature. The signature was significantly different between the sPHEO and LPA groups (AUC: training, 0.907; validation, 0.902). The radiomic nomogram based on enhanced CT features (M1) consisted of Rad-score, LD, SD, CT, shape, homogeneity and N/C (AUC: training, 0.957; validation, 0.967). The pre-enhanced CT features based radiomic nomogram (M2) included Rad-score, LD, SD, CT, shape, and homogeneity (AUC: training, 0.955; validation, 0.958).

Conclusions: Our radiomic nomograms based on pre-enhanced and enhanced CT images distinguished sPHEO from LPA. In addition, the promising result using pre-enhanced CT images for predictive diagnostics is important because patients could avoid the additional radiation and risk associated with enhanced CT.
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http://dx.doi.org/10.1007/s13167-018-0149-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6261906PMC
December 2018

Adrenal incidentaloma: machine learning-based quantitative texture analysis of unenhanced CT can effectively differentiate sPHEO from lipid-poor adrenal adenoma.

J Cancer 2018 8;9(19):3577-3582. Epub 2018 Sep 8.

Department of Radiology, Keck Medical Center of USC, Los Angeles, CA.

To evaluate the feasibility and accuracy of machine learning based texture analysis of unenhanced CT images in differentiating subclinical pheochromocytoma (sPHEO) from lipid-poor adenoma (LPA) in adrenal incidentaloma (AI). Seventy-nine patients with 80 LPA and 29 patients with 30 sPHEO were included in the study. Texture parameters were derived using imaging software (MaZda). Thirty texture features were selected and LPA was performed for the features selected. The number of positive features was used to predict results. Logistic multiple regression analysis was performed on the 30 texture features, and a predictive equation was created based on the coefficients obtained. LPA yielded a misclassification rate of 19.39% in differentiating sPHEO from LPA. Our predictive model had an accuracy rate of 94.4% (102/108), with a sensitivity of 86.2% (25/29) and a specificity of 97.5% (77/79) for differentiation. When the number of positive features was greater than 8, the accuracy of prediction was 85.2% (92/108), with a sensitivity of 96.6% (28/29) and a specificity of 81% (64/79). Machine learning-based quantitative texture analysis of unenhanced CT may be a reliable quantitative method in differentiating sPHEO from LPA when AI is present.
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http://dx.doi.org/10.7150/jca.26356DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6171020PMC
September 2018

Catheter-Based Computed Tomography Angiography in Anterolateral Thigh Perforator Mapping of Chinese Patients.

J Reconstr Microsurg 2019 Mar 2;35(3):221-228. Epub 2018 Oct 2.

Department of Diagnostic Radiology, City of Hope National Medical Center, Duarte, California, United States.

Background:  During reconstructive surgery, anterolateral thigh (ALT) flap harvest is challenging due to variation and uncertainty in perforator distribution. We performed a pilot study to identify the predictive value of catheter-based computed tomography angiography (C-CTA) and traditional CTA (T-CTA) in ALT perforator mapping for patients whose ALT perforators were difficult to identify.

Methods:  Thirty-four consecutive T-CTA/C-CTA-mapped ALT flaps were evaluated for extremity reconstruction. The perforator location, origin, and course were compared between T-CTA/C-CTA imaging and intraoperative findings. The mapping efficiency of T-CTA and C-CTA was compared thoroughly.

Results:  Among the 34 ALT thigh flaps, 117 (36) of the 130 perforators identified intraoperatively were visible on C-CTA (T-CTA) in a subgroup of Chinese limb trauma patients with limited activity. C-CTA showed a satisfactory efficiency in perforator mapping, which was much better than the efficiency of T-CTA. C-CTA also showed a much better sensitivity (90.00 vs. 27.69%), specificity (94.74 vs. 66.67%), and accuracy (91.07 vs. 36.69%), and a much lower false-positive (1.68 vs. 26.53%), and false-negative rate (10.00 vs. 72.31%). Moreover, C-CTA could accurately predict the origin and septocutaneous or intramuscular course in all identified perforators. All flaps were elevated successfully and survived.

Conclusion:  C-CTA outperforms T-CTA in the preoperative perforator mapping of ALT flaps in a subgroup of Chinese limb trauma patients. C-CTA should be the method of choice for perforator mapping in patients whose ALT flaps are intended for extremity reconstruction.
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http://dx.doi.org/10.1055/s-0038-1672129DOI Listing
March 2019

Renal solitary fibrous tumor/hemangiopericytoma: computed tomography findings and clinicopathologic features.

Abdom Radiol (NY) 2019 02;44(2):642-651

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

Purpose: To retrospectively characterize the clinical, pathological, and computed tomography (CT) findings of renal solitary fibrous tumor/hemangiopericytoma (rSFT/HPC).

Methods: Twelve patients with rSFT/HPCs were enrolled. The CT findings and clinicopathological features were retrospectively reviewed.

Results: This study included six male and six female patients (median age: 47; age range: 20-82 years). Eight benign (grade I) and four malignant (grade III) rSFT/HPCs were identified. Of the 12 lesions, 10 were in the renal sinus near the renal pelvis, while two replaced the whole kidney. Five lesions were well-defined, five were partially ill-defined, and two were ill-defined. Mild (5/12) and intermediate (1/12) hydronephrosis was observed. On the unenhanced CT images, ten tumors showed slightly higher density when compared to the normal renal parenchyma, and two masses were isodense to hypodense. After intravenous contrast medium injection, three enhancement patterns were observed, including "prolonged enhancement" (PE) (6/12), "gradual enhancement" (4/12), and "early washout" (2/12). A central fibrous scar was found in five patients. Compared to the grade I lesions, the grade III rSFT/HPC lesions tended to be larger (maximal diameter > 10 cm) and more heterogeneous with a higher incidence of the PE pattern.

Conclusions: We have shown that rSFT/HPCs usually arise from the renal sinus, and present as lobulated, slightly hyperdense, gradually enhancing soft tissue masses. CT findings, including large size, heterogeneity, and the PE pattern, may assist in the pre-operative identification of malignant grade III rSFT/HPCs.
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http://dx.doi.org/10.1007/s00261-018-1777-8DOI Listing
February 2019

Cortical Surface Area Rather Than Cortical Thickness Potentially Differentiates Radiation Encephalopathy at Early Stage in Patients With Nasopharyngeal Carcinoma.

Front Neurosci 2018 27;12:599. Epub 2018 Aug 27.

Key Laboratory for NeuroInformation of Ministry of Education, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China.

Radiation encephalopathy (RE) is one of the most severe complications in nasopharyngeal carcinoma (NPC) patients after radiotherapy (RT). However, the morphological alteration of early RE is insufficiently investigated. We aimed to investigate the cortical thickness and surface area alterations in NPC patients with or without RE in the follow-up. A total of 168 NPC patients each underwent a single scan and analysis at various times either Pre-RT ( = 56) or Post-RT ( = 112). We further divided the Post-RT NPC patients into three groups based on the time of the analysis following RT (Post-RT and Post-RT) or whether RE signs were detected in the analysis (Post-RT). We confined the vertex-wise analyses of the cortical thickness and surface area to the bilateral temporal lobes. Interestingly, we revealed a gradual increase in the cortical surface area of the temporal lobe with increasing time after RT within the Post-RT group, consistent with the between-group findings, which showed a significant increase in cortical surface area in the Post-RT group relative to the Pre-RT group and the Post-RT group. By contrast, such a trend was not observed in the cortical thickness findings. We concluded that the cortical surface area, rather than cortical thickness, may serve as a potential biomarker for early diagnosis of RE.
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http://dx.doi.org/10.3389/fnins.2018.00599DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6120047PMC
August 2018
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