1
|
Wang D, Jasim Taher H, Al-Fatlawi M, Abdullah BA, Khayatovna Ismailova M, Abedi-Firouzjah R. Multi-parametric assessment of cardiac magnetic resonance images to distinguish myocardial infarctions: A tensor-based radiomics feature. J Xray Sci Technol 2024:XST230307. [PMID: 38217635 DOI: 10.3233/xst-230307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/15/2024]
Abstract
AIM This study assessed the myocardial infarction (MI) using a novel fusion approach (multi-flavored or tensor-based) of multi-parametric cardiac magnetic resonance imaging (CMRI) at four sequences; T1-weighted (T1W) in the axial plane, sense-balanced turbo field echo (sBTFE) in the axial plane, late gadolinium enhancement of heart short axis (LGE-SA) in the sagittal plane, and four-chamber views of LGE (LGE-4CH) in the axial plane. METHODS After considering the inclusion and exclusion criteria, 115 patients (83 with MI diagnosis and 32 as healthy control patients), were included in the present study. Radiomic features were extracted from the whole left ventricular myocardium (LVM). Feature selection methods were Least Absolute Shrinkage and Selection Operator (Lasso), Minimum Redundancy Maximum Relevance (MRMR), Chi-Square (Chi2), Analysis of Variance (Anova), Recursive Feature Elimination (RFE), and SelectPersentile. The classification methods were Support Vector Machine (SVM), Logistic Regression (LR), and Random Forest (RF). Different metrics, including receiver operating characteristic curve (AUC), accuracy, F1- score, precision, sensitivity, and specificity were calculated for radiomic features extracted from CMR images using stratified five-fold cross-validation. RESULTS For the MI detection, Lasso (as the feature selection) and RF/LR (as the classifiers) in sBTFE sequences had the best performance (AUC: 0.97). All features and classifiers of T1 + sBTFE sequences with the weighted method (as the fused image), had a good performance (AUC: 0.97). In addition, the results of the evaluated metrics, especially mean AUC and accuracy for all models, determined that the T1 + sBTFE-weighted fused method had strong predictive performance (AUC: 0.93±0.05; accuracy: 0.93±0.04), followed by T1 + sBTFE-PCA fused method (AUC: 0.85±0.06; accuracy: 0.84±0.06). CONCLUSION Our selected CMRI sequences demonstrated that radiomics analysis enables to detection of MI accurately. Among the investigated sequences, the T1 + sBTFE-weighted fused method with the highest AUC and accuracy values was chosen as the best technique for MI detection.
Collapse
Affiliation(s)
- Dehua Wang
- Department of Imaging, The First People's Hospital of Lianyungang, Lianyungang City, Jiangsu Province, China
| | | | - Murtadha Al-Fatlawi
- Department of Radiological Techniques, College of Health and Medical Techniques, Al-Mustaqbal University, Babylon, Iraq
- Shaheed Al-Muhrab Center of Cath & Cardiac Surgery's, Babil Health Directorate, Babylon, Iraq
| | | | | | - Razzagh Abedi-Firouzjah
- Department of Medical Physics Radiobiology and Radiation Protection, School of Medicine, Babol University of Medical Sciences, Babol, Iran
| |
Collapse
|
2
|
Wang C, Chen J, Zheng N, Zheng K, Zhou L, Zhang Q, Zhang W. Predicting the risk of distant metastasis in patients with locally advanced rectal cancer using model based on pre-treatment T2WI-based radiomic features plus postoperative pathological stage. Front Oncol 2023; 13:1109588. [PMID: 37746305 PMCID: PMC10517628 DOI: 10.3389/fonc.2023.1109588] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 08/17/2023] [Indexed: 09/26/2023] Open
Abstract
Objective To assess the prognostic value of a model based on pre-treatment T2WI-based radiomic features and postoperative pathological staging in patients with locally advanced rectal cancer who have undergone neoadjuvant chemoradiotherapy. Methods Radiomic features were derived from T2WI, and a radiomic signature (RS) was established and validated for the prediction of distant metastases (DM). Subsequently, we designed and validated a nomogram model that combined the radiomic signature and postoperative pathological staging for enhanced DM prediction. Performance measures such as the concordance index (C-index) and area under the curve (AUC) were computed to assess the predictive accuracy of the models. Results A total of 260 patients participated in this study, of whom 197 (75.8%) were male, and the mean age was 57.2 years with a standard deviation of 11.2 years. 15 radiomic features were selected to define the radiomic signature. Patients with a high-risk radiomic signature demonstrated significantly shorter distant metastasis-free survival (DMFS) in both the development and validation cohorts. A nomogram, incorporating the radiomic signature, pathological T stage, and N stage, achieved an area under the curve (AUC) value of 0.72 (95% CI, 0.60-0.83) in the development cohort and 0.83 (95% CI, 0.73-0.92) in the validation cohort. Conclusion A radiomic signature derived from T2WI-based radiomic features can effectively distinguish patients with varying risks of DM. Furthermore, a nomogram integrating the radiomic signature and postoperative pathological stage proves to be a robust predictor of DMFS.
Collapse
Affiliation(s)
- Chen Wang
- Department of Colorectal Surgery, Changhai Hospital, Naval Medical University, Shanghai, China
- Hereditary Colorectal Cancer Center and Genetic Block Center of Familial Cancer, Changhai Hospital, Shanghai, China
| | - Jingjing Chen
- Graduate School of Naval Medical University, Shanghai, China
| | - Nanxin Zheng
- Department of Colorectal Surgery, Changhai Hospital, Naval Medical University, Shanghai, China
- Hereditary Colorectal Cancer Center and Genetic Block Center of Familial Cancer, Changhai Hospital, Shanghai, China
| | - Kuo Zheng
- Department of Colorectal Surgery, Changhai Hospital, Naval Medical University, Shanghai, China
- Hereditary Colorectal Cancer Center and Genetic Block Center of Familial Cancer, Changhai Hospital, Shanghai, China
| | - Lu Zhou
- Department of Colorectal Surgery, Changhai Hospital, Naval Medical University, Shanghai, China
- Hereditary Colorectal Cancer Center and Genetic Block Center of Familial Cancer, Changhai Hospital, Shanghai, China
| | - Qianwen Zhang
- Department of Radiology, Changhai Hospital, Naval Medical University, Shanghai, China
| | - Wei Zhang
- Department of Colorectal Surgery, Changhai Hospital, Naval Medical University, Shanghai, China
- Hereditary Colorectal Cancer Center and Genetic Block Center of Familial Cancer, Changhai Hospital, Shanghai, China
| |
Collapse
|
3
|
Lin K, Sarnari R, Carr JC, Markl M. Cine MRI-Derived Radiomics Features of the Cardiac Blood Pool: Periodicity, Specificity, and Reproducibility. J Magn Reson Imaging 2023; 58:807-814. [PMID: 36533630 PMCID: PMC10277313 DOI: 10.1002/jmri.28572] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 12/05/2022] [Accepted: 12/06/2022] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Although radiomics features of the left ventricular wall have been used to assess cardiac diseases, radiomics features of the cardiac blood pool have been relatively ignored. PURPOSE To test the hypothesis that cine MRI-derived radiomics features of the cardiac blood pool are associated with cardiac function and motion. STUDY TYPE Retrospective. POPULATION A total of 26 healthy volunteers (51.2 ± 15.6 years; 17 males). FIELD STRENGTH/SEQUENCE A 1.5 T/balanced steady-state free precession (bSSFP). ASSESSMENT The radiomics features (107 features in seven classes) of the blood pool of the left/right ventricle/atrium (LV/RV/LA/RA) were extracted on four-chamber cine images (25 phases). Conventional cardiac function parameters (volumes, ejection fraction [EF] and longitudinal strain) were assessed in each cardiac chamber. Intraobserver- and interobserver agreements of radiomics features of all chambers acquired at all phases were assessed, as well as scan-rescan agreement in a subset of 13 volunteers. STATISTICAL TESTS Pearson correlation coefficients (r) were used to assess the associations between peak values of radiomics features and end-diastolic (or maximal) volume, end-systolic (or minimal) volume, EF, and longitudinal strain of corresponding chambers. Good intraobserver, interobserver, and scan-rescan agreements for radiomics features acquired were defined as intraclass correlation coefficient (ICC) > 0.7 or coefficient of variation (CoV) < 20%. RESULTS Most radiomics features of the blood pool varied periodically throughout the cardiac cycle. Peak values of chamber-specific blood pool radiomics features were correlated with traditional cardiac function and motion indices of corresponding chambers (r: 0.4-0.87). Ninety-three (87%), 86 (80%), and 73 (68%) radiomics features demonstrated good intraobserver, interobserver, and scan-rescan reproducibility, respectively. CONCLUSION Cine MRI-derived radiomics features within LV/RV/LA/RA are associated with traditional cardiac function and motion indices of corresponding chambers and may have the potential to become novel quantitative imaging biomarkers in cardiovascular medicine. EVIDENCE LEVEL 3. TECHNICAL EFFICACY 1.
Collapse
Affiliation(s)
- Kai Lin
- Department of Radiology, Northwestern University, Chicago, Illinois, USA
| | - Roberto Sarnari
- Department of Radiology, Northwestern University, Chicago, Illinois, USA
| | - James C Carr
- Department of Radiology, Northwestern University, Chicago, Illinois, USA
| | - Michael Markl
- Department of Radiology, Northwestern University, Chicago, Illinois, USA
| |
Collapse
|
4
|
Chiu FY, Yen Y. Efficient Radiomics-Based Classification of Multi-Parametric MR Images to Identify Volumetric Habitats and Signatures in Glioblastoma: A Machine Learning Approach. Cancers (Basel) 2022; 14:1475. [PMID: 35326626 DOI: 10.3390/cancers14061475] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Accepted: 03/11/2022] [Indexed: 02/01/2023] Open
Abstract
Simple Summary Glioblastomas carry a poor prognosis and usually presents with heterogeneous regions in the brain tumor. Multi-parametric MR images can show morphological characteristics. Radiomics features refer to the extraction of a large number of quantitative measurements that describe the geometry, intensity, and texture which were extracted from contrast-enhanced T1-weighted images from anatomical MRI and metabolic features from PET. It also provides a qualitative image interpretation as well as cellular, molecular, and tumor properties. Thus, it derives additional information about the entire tumor volume which is generally of irregular shape and size from routinely evaluated “non-invasive” imaging biomarkers techniques. We demonstrated volumetric habitats and signatures in necrosis, solid tumor, peritumoral tissue, and edema with key biological processes and phenotype features. This provides physicians with key information on how the disease is progressing in the brain and can also give an indication of how well treatment is working. Abstract Glioblastoma (GBM) is a fast-growing and aggressive brain tumor of the central nervous system. It encroaches on brain tissue with heterogeneous regions of a necrotic core, solid part, peritumoral tissue, and edema. This study provided qualitative image interpretation in GBM subregions and radiomics features in quantitative usage of image analysis, as well as ratios of these tumor components. The aim of this study was to assess the potential of multi-parametric MR fingerprinting with volumetric tumor phenotype and radiomic features to underlie biological process and prognostic status of patients with cerebral gliomas. Based on efficiently classified and retrieved cerebral multi-parametric MRI, all data were analyzed to derive volume-based data of the entire tumor from local cohorts and The Cancer Imaging Archive (TCIA) cohorts with GBM. Edema was mainly enriched for homeostasis whereas necrosis was associated with texture features. The proportional volume size of the edema was about 1.5 times larger than the size of the solid part tumor. The volume size of the solid part was approximately 0.7 times in the necrosis area. Therefore, the multi-parametric MRI-based radiomics model reveals efficiently classified tumor subregions of GBM and suggests that prognostic radiomic features from routine MRI examination may also be significantly associated with key biological processes as a practical imaging biomarker.
Collapse
|
5
|
Yao X, Huang X, Yang C, Hu A, Zhou G, Lei J, Shu J. A Novel Approach to Assessing Differentiation Degree and Lymph Node Metastasis of Extrahepatic Cholangiocarcinoma: Prediction Using a Radiomics-Based Particle Swarm Optimization and Support Vector Machine Model. JMIR Med Inform 2020; 8:e23578. [PMID: 33016889 PMCID: PMC7573697 DOI: 10.2196/23578] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2020] [Accepted: 09/18/2020] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Radiomics can improve the accuracy of traditional image diagnosis to evaluate extrahepatic cholangiocarcinoma (ECC); however, this is limited by variations across radiologists, subjective evaluation, and restricted data. A radiomics-based particle swarm optimization and support vector machine (PSO-SVM) model may provide a more accurate auxiliary diagnosis for assessing differentiation degree (DD) and lymph node metastasis (LNM) of ECC. OBJECTIVE The objective of our study is to develop a PSO-SVM radiomics model for predicting DD and LNM of ECC. METHODS For this retrospective study, the magnetic resonance imaging (MRI) data of 110 patients with ECC who were diagnosed from January 2011 to October 2019 were used to construct a radiomics prediction model. Radiomics features were extracted from T1-precontrast weighted imaging (T1WI), T2-weighted imaging (T2WI), and diffusion-weighted imaging (DWI) using MaZda software (version 4.6; Institute of Electronics, Technical University of Lodz). We performed dimension reduction to obtain 30 optimal features of each sequence, respectively. A PSO-SVM radiomics model was developed to predict DD and LNM of ECC by incorporating radiomics features and apparent diffusion coefficient (ADC) values. We randomly divided the 110 cases into a training group (88/110, 80%) and a testing group (22/110, 20%). The performance of the model was evaluated by analyzing the area under the receiver operating characteristic curve (AUC). RESULTS A radiomics model based on PSO-SVM was developed by using 110 patients with ECC. This model produced average AUCs of 0.8905 and 0.8461, respectively, for DD in the training and testing groups of patients with ECC. The average AUCs of the LNM in the training and testing groups of patients with ECC were 0.9036 and 0.8889, respectively. For the 110 patients, this model has high predictive performance. The average accuracy values of the training group and testing group for DD of ECC were 82.6% and 80.9%, respectively; the average accuracy values of the training group and testing group for LNM of ECC were 83.6% and 81.2%, respectively. CONCLUSIONS The MRI-based PSO-SVM radiomics model might be useful for auxiliary clinical diagnosis and decision-making, which has a good potential for clinical application for DD and LNM of ECC.
Collapse
Affiliation(s)
- Xiaopeng Yao
- School of Medical Information and Engineering, Southwest Medical University, Luzhou, China.,Central Nervous System Drug Key Laboratory of Sichuan Province, Southwest Medical University, Luzhou, China
| | - Xinqiao Huang
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Chunmei Yang
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Anbin Hu
- School of Medical Information and Engineering, Southwest Medical University, Luzhou, China.,Central Nervous System Drug Key Laboratory of Sichuan Province, Southwest Medical University, Luzhou, China
| | - Guangjin Zhou
- Department of Radiology, Peking University Third Hospital, Beijing, China
| | - Jianbo Lei
- School of Medical Information and Engineering, Southwest Medical University, Luzhou, China.,Center for Medical Informatics/Institute of Medical Technology, Peking University, Beijing, China
| | - Jian Shu
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| |
Collapse
|
6
|
He B, Ji T, Zhang H, Zhu Y, Shu R, Zhao W, Wang K. MRI-based radiomics signature for tumor grading of rectal carcinoma using random forest model. J Cell Physiol 2019; 234:20501-20509. [PMID: 31074022 DOI: 10.1002/jcp.28650] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2018] [Revised: 03/10/2019] [Accepted: 03/19/2019] [Indexed: 12/27/2022]
Abstract
The present study aimed to construct prospective models for tumor grading of rectal carcinoma by using magnetic resonance (MR)-based radiomics features. A set of 118 patients with rectal carcinoma was analyzed. After imbalance-adjustments of the data using Synthetic Minority Oversampling Technique (SMOTE), the final data set was randomized into the training set and validation set at the ratio of 3:1. The radiomics features were captured from manually segmented lesion of magnetic resonance imaging (MRI). The most related radiomics features were selected using the random forest model by calculating the Gini importance of initial extracted characteristics. A random forest classifier model was constructed using the top important features. The classifier model performance was evaluated via receive operator characteristic curve and area under the curve (AUC). A total of 1,131 radiomics features were extracted from segmented lesion. The top 50 most important features were selected to construct a random forest classifier model. The AUC values of grade 1, 2, 3, and 4 for training set were 0.918, 0.822, 0.775, and 1.000, respectively, and the corresponding AUC values for testing set were 0.717, 0.683, 0.690, and 0.827 separately. The developed feature selection method and machine learning-based prediction models using radiomics features of MRI show a relatively acceptable performance in tumor grading of rectal carcinoma and could distinguish the tumor subjects from the healthy ones, which is important for the prognosis of cancer patients.
Collapse
Affiliation(s)
- Bo He
- Key Laboratory of Drug Addiction and Rehabilitation, National Health Commission of the Peoples' Republic of China, Kunming, Yunnan, China.,Department of Medical Imaging, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - Tao Ji
- Yunnan Institute of Digestive Disease, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - Hong Zhang
- Department of Medical Imaging, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - Yun Zhu
- Department of Medical Imaging, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - Ruo Shu
- Yunnan Institute of Digestive Disease, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - Wei Zhao
- Department of Medical Imaging, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - Kunhua Wang
- Key Laboratory of Drug Addiction and Rehabilitation, National Health Commission of the Peoples' Republic of China, Kunming, Yunnan, China.,Yunnan Institute of Digestive Disease, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| |
Collapse
|
7
|
Bagher-Ebadian H, Siddiqui F, Liu C, Movsas B, Chetty IJ. On the impact of smoothing and noise on robustness of CT and CBCT radiomics features for patients with head and neck cancers. Med Phys 2017; 44:1755-1770. [PMID: 28261818 DOI: 10.1002/mp.12188] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2016] [Revised: 02/13/2017] [Accepted: 02/17/2017] [Indexed: 01/09/2023] Open
Abstract
PURPOSE We investigated the characteristics of radiomics features extracted from planning CT (pCT) and cone beam CT (CBCT) image datasets acquired for 18 oropharyngeal cancer patients treated with fractionated radiation therapy. Images were subjected to smoothing, sharpening, and noise to evaluate changes in features relative to baseline datasets. METHODS Textural features were extracted from tumor volumes, contoured on pCT and CBCT images, according to the following eight different classes: intensity based histogram features (IBHF), gray level run length (GLRL), law's textural information (LAWS), discrete orthonormal stockwell transform (DOST), local binary pattern (LBP), two-dimensional wavelet transform (2DWT), Two dimensional Gabor filter (2DGF), and gray level co-occurrence matrix (GLCM). A total of 165 radiomics features were extracted. Images were post-processed prior to feature extraction using a Gaussian noise model with different signal-to-noise-ratios (SNR = 5, 10, 15, 20, 25, 35, 50, 75, 100, and 150). Gaussian filters with different cut off frequencies (varied discreetly from 0.0458 to 0.7321 cycles-mm-1 ) were applied to image datasets. Effect of noise and smoothing on each extracted feature was quantified using mean absolute percent change (MAPC) between the respective values on post-processed and baseline images. The Fisher method for combining Welch P-values was used for tests of significance. Three comparisons were investigated: (a) Baseline pCT versus modified pCT (with given filter applied); (b) Baseline CBCT versus modified CBCT, and (c) Baseline and modified pCT versus baseline and modified CBCT. RESULTS Features extracted from CT and CBCT image datasets were robust to low-pass filtering (MAPC = 17.5%, pvalFisher¯ = 0.93 for CBCT and MAPC = 7.5%, pvalFisher¯ = 0.98 for pCT) and noise (MAPC = 27.1%, pvalFisher¯ = 0.89 for CBCT, and MAPC = 34.6%, pvalFisher¯ = 0.61 for pCT). Extracted features were significantly impacted (MAPC=187.7%, pvalFisher¯ < 0.0001 for CBCT, and MAPC = 180.6%, pvalFisher¯ < 0.01 for pCT) by LOG which is classified as a high-pass filter. Features most impacted by low pass filtering were LAWS (MAPC = 11.2%, pvalFisher¯ = 0.44), GLRL (MAPC = 9.7%, pvalFisher¯ = 0.70) and IBHF (MAPC = 21.7%, pvalFisher¯ = 0.83), for the pCT datasets, and LAWS (MAPC = 20.2%, pvalFisher¯ = 0.24), GLRL (MAPC = 14.5%, pvalFisher¯ = 0.44), and 2DGF (MAPC=16.3%, pvalFisher¯ = 0.52), for CBCT image datasets. For pCT datasets, features most impacted by noise were GLRL (MAPC = 29.7%, pvalFisher¯ = 0.06), LAWS (MAPC = 96.6%, pvalFisher¯ = 0.42), and GLCM (MAPC = 36.2%, pvalFisher¯ = 0.48), while the LBPF (MAPC = 5.2%, pvalFisher¯ = 0.99) was found to be relatively insensitive to noise. For CBCT datasets, GLRL (MAPC = 8.9%, pvalFisher¯ = 0.80) and LAWS (MAPC = 89.3%, pvalFisher¯ = 0.81) features were impacted by noise, while the LBPF (MAPC = 2.2%, pvalFisher¯ = 0.99) and DOST (MAPC = 13.7%, pvalFisher¯ = 0.98) features were noise insensitive. Apart from 15 features, no significant differences were observed for the remaining 150 textural features extracted from baseline pCT and CBCT image datasets (MAPC = 90.1%, pvalFisher¯ = 0.26). CONCLUSIONS Radiomics features extracted from planning CT and daily CBCT image datasets for head/neck cancer patients were robust to low-power Gaussian noise and low-pass filtering, but were impacted by high-pass filtering. Textural features extracted from CBCT and pCT image datasets were similar, suggesting interchangeability of pCT and CBCT for investigating radiomics features as possible biomarkers for outcome.
Collapse
Affiliation(s)
- Hassan Bagher-Ebadian
- Department of Radiation Oncology, Henry Ford Hospital, Detroit, MI, USA.,Department of Physics, Oakland University, Rochester, MI, USA
| | - Farzan Siddiqui
- Department of Radiation Oncology, Henry Ford Hospital, Detroit, MI, USA
| | - Chang Liu
- Department of Radiation Oncology, Henry Ford Hospital, Detroit, MI, USA
| | - Benjamin Movsas
- Department of Radiation Oncology, Henry Ford Hospital, Detroit, MI, USA
| | - Indrin J Chetty
- Department of Radiation Oncology, Henry Ford Hospital, Detroit, MI, USA
| |
Collapse
|