Huihui Xie1, Shuai Ma2, Xiaochao Guo3, Xiaodong Zhang4, Xiaoying Wang5. 1. Department of Radiology, Peking University First Hospital, Beijing, China. Electronic address: xiehuihui1030@163.com. 2. Department of Radiology, Peking University First Hospital, Beijing, China. Electronic address: mashuai316@126.com. 3. Department of Radiology, Peking University First Hospital, Beijing, China. Electronic address: Guoxiaochao1985@163.com. 4. Department of Radiology, Peking University First Hospital, Beijing, China. Electronic address: zhxd2009@gmail.com. 5. Department of Radiology, Peking University First Hospital, Beijing, China. Electronic address: wangxiaoying@bjmu.edu.cn.
Abstract
PURPOSE: To develop a radiomics model in the preoperative differentiation of mucinous cystic neoplasm (MCN) and macrocystic serous cystadenoma (MaSCA) and to compare its diagnostic performance with conventional radiological model. METHODS: 57 Patients (MCN = 31, MaSCA = 26) with preoperative multidetector computed tomography (MDCT) scans were retrospectively included in this study. A radiological model was constructed from radiological features evaluated by radiologists. A radiomics model was constructed with high-dimensional quantitative features extracted from manually segmented volume of interests (VOIs). A combined model was constructed using both radiomics features and radiological features. The diagnostic performance of three models were assessed by the area under the receiver-operating characteristic curve (AUC), sensitivity, specificity, accuracy, and the calibration curves. RESULTS: The radiological model yielded an AUC of 0.775, sensitivity of 74.2 %, specificity of 80.8, and accuracy of 77.2 %. The radiomics model yielded an AUC of 0.989, sensitivity of 93.6 %, specificity of 96.2 %, and accuracy of 94.7 %. The combined model yielded an AUC of 0.994, sensitivity of 96.8 %, specificity of 100 %, and accuracy of 98.2 %. Both combined model and radiomics model showed higher AUC, sensitivity, and accuracy than radiological model (all P < .05). The combined model showed higher AUC than radiomics model, though no significant difference was found (P = .41). The combined model showed better calibration than radiomics model (P = .91 vs. P < .001). CONCLUSIONS: Combined model which contained both radiomics features and radiological features outperformed radiomics model and radiological model in the preoperative differentiation of MCN and MaSCA.
PURPOSE: To develop a radiomics model in the preoperative differentiation of mucinous cystic neoplasm (MCN) and macrocystic serous cystadenoma (MaSCA) and to compare its diagnostic performance with conventional radiological model. METHODS: 57 Patients (MCN = 31, MaSCA = 26) with preoperative multidetector computed tomography (MDCT) scans were retrospectively included in this study. A radiological model was constructed from radiological features evaluated by radiologists. A radiomics model was constructed with high-dimensional quantitative features extracted from manually segmented volume of interests (VOIs). A combined model was constructed using both radiomics features and radiological features. The diagnostic performance of three models were assessed by the area under the receiver-operating characteristic curve (AUC), sensitivity, specificity, accuracy, and the calibration curves. RESULTS: The radiological model yielded an AUC of 0.775, sensitivity of 74.2 %, specificity of 80.8, and accuracy of 77.2 %. The radiomics model yielded an AUC of 0.989, sensitivity of 93.6 %, specificity of 96.2 %, and accuracy of 94.7 %. The combined model yielded an AUC of 0.994, sensitivity of 96.8 %, specificity of 100 %, and accuracy of 98.2 %. Both combined model and radiomics model showed higher AUC, sensitivity, and accuracy than radiological model (all P < .05). The combined model showed higher AUC than radiomics model, though no significant difference was found (P = .41). The combined model showed better calibration than radiomics model (P = .91 vs. P < .001). CONCLUSIONS: Combined model which contained both radiomics features and radiological features outperformed radiomics model and radiological model in the preoperative differentiation of MCN and MaSCA.
Authors: Adam M Awe; Michael M Vanden Heuvel; Tianyuan Yuan; Victoria R Rendell; Mingren Shen; Agrima Kampani; Shanchao Liang; Dane D Morgan; Emily R Winslow; Meghan G Lubner Journal: Abdom Radiol (NY) Date: 2021-10-12