Dooman Arefan1, Ruimei Chai2, Min Sun3, Margarita L Zuley1,4, Shandong Wu5. 1. Department of Radiology, University of Pittsburgh, School of Medicine, 4200 Fifth Ave, Pittsburgh, PA, 15260, USA. 2. Department of Radiology, First Hospital of China Medical University, Shenyang, Liaoning Province, China. 3. UPMC Hillman Cancer Center at St. Margaret, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15215, USA. 4. Magee-Womens Hospital of University of Pittsburgh Medical Center, 300 Halket St, Pittsburgh, PA, 15213, USA. 5. Departments of Radiology of Biomedical Informatics of Bioengineering, University of Pittsburgh, 4200 Fifth Ave, Pittsburgh, PA, 15260, USA.
Abstract
PURPOSE: The purpose of this study was to distinguish axillary lymph node (ALN) status using preoperative breast DCE-MRI radiomics and compare the effects of two-dimensional (2D) and three-dimensional (3D) analysis. METHODS: A retrospective study including 154 breast cancer patients all confirmed by pathology; 80 with ALN metastasis and 74 without. All MRI scans were achieved at a 3.0 Tesla scanner with 7 post-contrast MR phases sequentially acquired with a temporal resolution of 60 s. MRI radiomic features were extracted separately from a 2D single slice (i.e., the representative slice) and the 3D tumor volume. Several machine learning classifiers were built and compared using 2D or 3D analysis to distinguish positive vs negative ALN status. We performed independent test and 10-fold cross validation with multiple repetitions, and used bootstrap test, least absolute shrinkage selection operator, and receiver operating characteristic (ROC) curve analysis as statistical tests. RESULTS: The highest area under the ROC curve (AUC) was 0.81 (95% confidence intervals [CI]: 0.80-0.83) and 0.82 (95% CI: 0.81-0.82) for 2D and 3D analysis, respectively; the corresponding accuracy was 79% and 80%. The linear discriminant analysis (LDA) classifier achieved the highest classification performance. None of the AUC differences between 2D and 3D analysis was statistically significant for the several tested machine learning classifiers (all P> 0.05). CONCLUSIONS: Radiomic features from segmented tumor region in breast MRI were associated with ALN status. The separate radiomic analysis on 3D tumor volume showed a similar effect to the 2D analysis on the single representative slice in the tested machine learning classifiers.
PURPOSE: The purpose of this study was to distinguish axillary lymph node (ALN) status using preoperative breast DCE-MRI radiomics and compare the effects of two-dimensional (2D) and three-dimensional (3D) analysis. METHODS: A retrospective study including 154 breast cancerpatients all confirmed by pathology; 80 with ALN metastasis and 74 without. All MRI scans were achieved at a 3.0 Tesla scanner with 7 post-contrast MR phases sequentially acquired with a temporal resolution of 60 s. MRI radiomic features were extracted separately from a 2D single slice (i.e., the representative slice) and the 3D tumor volume. Several machine learning classifiers were built and compared using 2D or 3D analysis to distinguish positive vs negative ALN status. We performed independent test and 10-fold cross validation with multiple repetitions, and used bootstrap test, least absolute shrinkage selection operator, and receiver operating characteristic (ROC) curve analysis as statistical tests. RESULTS: The highest area under the ROC curve (AUC) was 0.81 (95% confidence intervals [CI]: 0.80-0.83) and 0.82 (95% CI: 0.81-0.82) for 2D and 3D analysis, respectively; the corresponding accuracy was 79% and 80%. The linear discriminant analysis (LDA) classifier achieved the highest classification performance. None of the AUC differences between 2D and 3D analysis was statistically significant for the several tested machine learning classifiers (all P> 0.05). CONCLUSIONS: Radiomic features from segmented tumor region in breast MRI were associated with ALN status. The separate radiomic analysis on 3D tumor volume showed a similar effect to the 2D analysis on the single representative slice in the tested machine learning classifiers.
Authors: Jung Hun Oh; Aditya P Apte; Evangelia Katsoulakis; Nadeem Riaz; Vaios Hatzoglou; Yao Yu; Usman Mahmood; Harini Veeraraghavan; Maryam Pouryahya; Aditi Iyer; Amita Shukla-Dave; Allen Tannenbaum; Nancy Y Lee; Joseph O Deasy Journal: J Med Imaging (Bellingham) Date: 2021-04-30
Authors: Domiziana Santucci; Eliodoro Faiella; Michela Gravina; Ermanno Cordelli; Carlo de Felice; Bruno Beomonte Zobel; Giulio Iannello; Carlo Sansone; Paolo Soda Journal: Cancers (Basel) Date: 2022-09-21 Impact factor: 6.575