Meng Liang1, Zhengting Cai2, Hongmei Zhang1, Chencui Huang2, Yankai Meng3, Li Zhao1, Dengfeng Li1, Xiaohong Ma4, Xinming Zhao5. 1. Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, People's Republic of China. 2. Huiying Medical Technology Co., Ltd., HaiDian District, Beijing City, 100192, People's Republic of China. 3. Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, People's Republic of China; Department of Radiology, the Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu Province, People's Republic of China. 4. Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, People's Republic of China. Electronic address: dr_maxh_cams@sina.com. 5. Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, People's Republic of China. Electronic address: zhaoxm2018@outlook.com.
Abstract
RATIONALE AND OBJECTIVES: To use machine learning-based magnetic resonance imaging radiomics to predict metachronous liver metastases (MLM) in patients with rectal cancer. MATERIALS AND METHODS: This study retrospectively analyzed 108 patients with rectal cancer (54 in MLM group and 54 in nonmetastases group). Feature selection were performed in the radiomic feature sets extracted from images of T2-weighted image (T2WI) and venous phase (VP) sequence respectively, and the combining feature set with 2058 radiomic features incorporating two sequences with the least absolute shrinkage and selection operator method. Five-fold cross-validation and two machine learning algorithms (support vector machine [SVM]; logistic regression [LR]) were utilized for predictive model constructing. The diagnostic performance of the models was evaluated by receiver operating characteristic curves with indicators of accuracy, sensitivity, specificity and area under the curve, and compared by DeLong test. RESULTS: Five, 8, and 22 optimal features were selected from 1029 T2WI, 1029 VP, and 2058 combining features, respectively. Four-group models were constructed using the five T2WI features (ModelT2), the 8 VP features (ModelVP), the combined 13 optimal features (Modelcombined), and the 22 optimal features selected from 2058 features (Modeloptimal). In ModelVP, the LR was superior to the SVM algorithm (P = 0.0303). The Modeloptimal using LR algorithm showed the best prediction performance (P = 0.0019-0.0081) with accuracy, sensitivity, specificity, and area under the curve of 0.80, 0.83, 0.76, and 0.87, respectively. CONCLUSION: Radiomics models based on baseline rectal magnetic resonance imaging has high potential for MLM prediction, especially the Modeloptimal using LR algorithm. Moreover, except for ModelVP, the LR was not superior to the SVM algorithm for model construction.
RATIONALE AND OBJECTIVES: To use machine learning-based magnetic resonance imaging radiomics to predict metachronous liver metastases (MLM) in patients with rectal cancer. MATERIALS AND METHODS: This study retrospectively analyzed 108 patients with rectal cancer (54 in MLM group and 54 in nonmetastases group). Feature selection were performed in the radiomic feature sets extracted from images of T2-weighted image (T2WI) and venous phase (VP) sequence respectively, and the combining feature set with 2058 radiomic features incorporating two sequences with the least absolute shrinkage and selection operator method. Five-fold cross-validation and two machine learning algorithms (support vector machine [SVM]; logistic regression [LR]) were utilized for predictive model constructing. The diagnostic performance of the models was evaluated by receiver operating characteristic curves with indicators of accuracy, sensitivity, specificity and area under the curve, and compared by DeLong test. RESULTS: Five, 8, and 22 optimal features were selected from 1029 T2WI, 1029 VP, and 2058 combining features, respectively. Four-group models were constructed using the five T2WI features (ModelT2), the 8 VP features (ModelVP), the combined 13 optimal features (Modelcombined), and the 22 optimal features selected from 2058 features (Modeloptimal). In ModelVP, the LR was superior to the SVM algorithm (P = 0.0303). The Modeloptimal using LR algorithm showed the best prediction performance (P = 0.0019-0.0081) with accuracy, sensitivity, specificity, and area under the curve of 0.80, 0.83, 0.76, and 0.87, respectively. CONCLUSION: Radiomics models based on baseline rectal magnetic resonance imaging has high potential for MLM prediction, especially the Modeloptimal using LR algorithm. Moreover, except for ModelVP, the LR was not superior to the SVM algorithm for model construction.
Authors: Mustafa Bektaş; Jurriaan B Tuynman; Jaime Costa Pereira; George L Burchell; Donald L van der Peet Journal: World J Surg Date: 2022-09-15 Impact factor: 3.282
Authors: Jose M Celaya-Padilla; Karen E Villagrana-Bañuelos; Juan José Oropeza-Valdez; Joel Monárrez-Espino; Julio E Castañeda-Delgado; Ana Sofía Herrera-Van Oostdam; Julio César Fernández-Ruiz; Fátima Ochoa-González; Juan Carlos Borrego; Jose Antonio Enciso-Moreno; Jesús Adrián López; Yamilé López-Hernández; Carlos E Galván-Tejada Journal: Diagnostics (Basel) Date: 2021-11-25