Yu Zhang1, Yifeng Zhu1, Xiaomeng Shi2, Juan Tao3, Jingjing Cui4, Yue Dai1, Minting Zheng5, Shaowu Wang6. 1. Department of Radiology, The Second Hospital, Dalian Medical University, NO.467, Zhongshan Road, Shahekou, Dalian, China. 2. Department of Gastrointestinal Surgery, The Second Hospital, Dalian Medical University, Dalian, China. 3. Department of Pathology, The Second Hospital, Dalian Medical University, Dalian, China. 4. Huiying Medical Technology Inc., Beijing, China. 5. Department of Radiology, The First Hospital, Dalian Medical University, Dalian, China. 6. Department of Radiology, The Second Hospital, Dalian Medical University, NO.467, Zhongshan Road, Shahekou, Dalian, China. Electronic address: wsw_2018@163.com.
Abstract
RATIONALE AND OBJECTIVES: The purpose of this study is to develop a radiomics model for predicting the histopathological grades of soft tissue sarcomas preoperatively through magnetic resonance imaging (MRI). MATERIALS AND METHODS: Thirty-five patients who were pathologically diagnosed with soft tissue sarcomas and their histological grades were recruited. All patients had undergone MRI before surgery on a 3.0T MRI scanner. Radiomics features were extracted from fat-suppressed T2-weighted imaging. We used the least absolute shrinkage and selection operator (LASSO) regression method to select features. Then three machine learning classification methods, including random forests, k-nearest neighbor, and support vector machine algorithm were trained using the 5-fold cross validation strategy to separate the soft tissue sarcomas with low- and high-histopathological grades. RESULTS: The radiomics features were significantly associated with the histopathological grades. Quantitative imaging features (n = 1049) were extracted from fat-suppressed T2-weighted imaging, and five features were selected to construct the radiomics model. The model that used support vector machine classification method achieved the best performance among the three methods, with areas under the receiver operating characteristic curves Area Under Curve (AUC) values of 0.92 ± 0.07, accuracy of 0.88. CONCLUSION: Good accuracy and AUC could be obtained using only five radiomic features. Therefore, we proposed that three-dimensional imaging features from fat-suppressed T2-weighted imaging could be used as candidate biomarkers for preoperative prediction of histopathological grades of soft tissue sarcomas noninvasively.
RATIONALE AND OBJECTIVES: The purpose of this study is to develop a radiomics model for predicting the histopathological grades of soft tissue sarcomas preoperatively through magnetic resonance imaging (MRI). MATERIALS AND METHODS: Thirty-five patients who were pathologically diagnosed with soft tissue sarcomas and their histological grades were recruited. All patients had undergone MRI before surgery on a 3.0T MRI scanner. Radiomics features were extracted from fat-suppressed T2-weighted imaging. We used the least absolute shrinkage and selection operator (LASSO) regression method to select features. Then three machine learning classification methods, including random forests, k-nearest neighbor, and support vector machine algorithm were trained using the 5-fold cross validation strategy to separate the soft tissue sarcomas with low- and high-histopathological grades. RESULTS: The radiomics features were significantly associated with the histopathological grades. Quantitative imaging features (n = 1049) were extracted from fat-suppressed T2-weighted imaging, and five features were selected to construct the radiomics model. The model that used support vector machine classification method achieved the best performance among the three methods, with areas under the receiver operating characteristic curves Area Under Curve (AUC) values of 0.92 ± 0.07, accuracy of 0.88. CONCLUSION: Good accuracy and AUC could be obtained using only five radiomic features. Therefore, we proposed that three-dimensional imaging features from fat-suppressed T2-weighted imaging could be used as candidate biomarkers for preoperative prediction of histopathological grades of soft tissue sarcomas noninvasively.
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