Na Chang1, Lingling Cui2, Yahong Luo3, Zhihui Chang1, Bing Yu1, Zhaoyu Liu1. 1. Department of Radiology, Shengjing Hospital of China Medical University, Shenyang 110004, China. 2. Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang 110001, China. 3. Department of Radiology, Liaoning Cancer Institute and Hospital, Shenyang 110000, China.
Abstract
BACKGROUND: The histological grade of pancreatic cancer is an important independent predictor of outcome. However, we lack a method for safely and accurately obtaining the pathological grade before surgery. Radiomics has been used to discriminate between histological grades in tumors. We aimed to develop and validate a radiomics signature for the preoperative prediction of histological grades of pancreatic ductal adenocarcinoma (PDAC) that was based on contrast-enhanced computed tomography (CE-CT). METHODS: This study comprised 301 patients with pathologically confirmed PDAC who were randomly divided into a training (n=151) and test group (n=150). Radiomics features were selected by a support vector machine (SVM) model, and a radiomics signature was generated by the least absolute shrinkage and selection operator (LASSO) model. An additional 100 patients from 2 other medical centers were used for external validation. Receiver operating characteristic (ROC) curve analysis was used to assess the model and to identify the optimal cutoff value. RESULTS: The radiomics signatures between high-grade and low-grade PDACs in the training and test groups were significantly different (P<0.05). The areas under the curve (AUCs) of the training and test datasets were 0.961 and 0.910, respectively. The optimal cutoff value of the radiomics score was 0.426. In the external validation dataset, the difference between the radiomics signatures of high-grade versus low-grade PDACs was also significant (P<0.05). The radiomics signature for the external validation data had an AUC of 0.770. CONCLUSIONS: The CE-CT-based radiomics signature showed moderate predictive accuracy for differentiating low-grade from high-grade PDAC and should become a new noninvasive method for the preoperative prediction of histological grades of PDAC. 2020 Quantitative Imaging in Medicine and Surgery. All rights reserved.
BACKGROUND: The histological grade of pancreatic cancer is an important independent predictor of outcome. However, we lack a method for safely and accurately obtaining the pathological grade before surgery. Radiomics has been used to discriminate between histological grades in tumors. We aimed to develop and validate a radiomics signature for the preoperative prediction of histological grades of pancreatic ductal adenocarcinoma (PDAC) that was based on contrast-enhanced computed tomography (CE-CT). METHODS: This study comprised 301 patients with pathologically confirmed PDAC who were randomly divided into a training (n=151) and test group (n=150). Radiomics features were selected by a support vector machine (SVM) model, and a radiomics signature was generated by the least absolute shrinkage and selection operator (LASSO) model. An additional 100 patients from 2 other medical centers were used for external validation. Receiver operating characteristic (ROC) curve analysis was used to assess the model and to identify the optimal cutoff value. RESULTS: The radiomics signatures between high-grade and low-grade PDACs in the training and test groups were significantly different (P<0.05). The areas under the curve (AUCs) of the training and test datasets were 0.961 and 0.910, respectively. The optimal cutoff value of the radiomics score was 0.426. In the external validation dataset, the difference between the radiomics signatures of high-grade versus low-grade PDACs was also significant (P<0.05). The radiomics signature for the external validation data had an AUC of 0.770. CONCLUSIONS: The CE-CT-based radiomics signature showed moderate predictive accuracy for differentiating low-grade from high-grade PDAC and should become a new noninvasive method for the preoperative prediction of histological grades of PDAC. 2020 Quantitative Imaging in Medicine and Surgery. All rights reserved.
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