Qiaoling Chen1, Yanfen Cui2, Hongmei Gu3, Feng Feng4, Ting Xue1, Hui Peng1, Manman Li1, Xinghua Zhu5, Shaofeng Duan6. 1. Nantong University, Nantong, 226001, Jiangsu Province, China. 2. Department of Radiology, Shanxi Tumor Hospital, Shanxi, 030013, Shanxi Province, China. 3. Department of Radiology, Affiliated Hospital of Nantong University, Nantong, 226001, Jiangsu Province, China. guhongmei71@163.com. 4. Department of Radiology, Affiliated Tumor Hospital of Nantong University, Nantong, 226001, Jiangsu Province, China. fengfeng@ntu.edu.cn. 5. Department of Pathology, Affiliated Tumor Hospital of Nantong University, Nantong, 226001, Jiangsu Province, China. 6. GE Healthcare China, Shanghai City, 210000, China.
Abstract
PURPOSE: To develop and validate a computed tomography (CT) radiomics nomogram from multicentre datasets for preoperative prediction of perineural invasion (PNI) in colorectal cancer. METHODS: A total of 299 patients with histologically confirmed colorectal cancer from three hospitals were enrolled in this retrospective study. Radiomic features were extracted from the whole tumour volume. The least absolute shrinkage and selection operator logistic regression was applied for feature selection and radiomics signature construction. Finally, a radiomics nomogram combining the radiomics score and clinical predictors was established. The receiver operating characteristic curve and decision curve analysis (DCA) were used to evaluate the predictive performance of the radiomics nomogram in the training cohort, internal validation and external validation cohorts. RESULTS: Twelve radiomics features extracted from the whole tumour volume were used to construct the radiomics model. The area under the curve (AUC) values of the radiomics model in the training cohort, internal validation cohort, external validation cohort 1, and external validation cohort 2 were 0.82 (0.75-0.90), 0.77 (0.62-0.92), 0.71 (0.56-0.85), and 0.73 (0.60-0.85), respectively. The nomogram, which combined the radiomics score with T category and N category by CT, yielded better performance in the training cohort (AUC = 0.88), internal validation cohort (AUC = 0.80), external validation cohort 1 (AUC = 0.75), and external validation cohort 2 (AUC = 0.76). DCA confirmed the clinical utility of the nomogram. CONCLUSIONS: The CT-based radiomics nomogram has the potential to accurately predict PNI in patients with colorectal cancer.
PURPOSE: To develop and validate a computed tomography (CT) radiomics nomogram from multicentre datasets for preoperative prediction of perineural invasion (PNI) in colorectal cancer. METHODS: A total of 299 patients with histologically confirmed colorectal cancer from three hospitals were enrolled in this retrospective study. Radiomic features were extracted from the whole tumour volume. The least absolute shrinkage and selection operator logistic regression was applied for feature selection and radiomics signature construction. Finally, a radiomics nomogram combining the radiomics score and clinical predictors was established. The receiver operating characteristic curve and decision curve analysis (DCA) were used to evaluate the predictive performance of the radiomics nomogram in the training cohort, internal validation and external validation cohorts. RESULTS: Twelve radiomics features extracted from the whole tumour volume were used to construct the radiomics model. The area under the curve (AUC) values of the radiomics model in the training cohort, internal validation cohort, external validation cohort 1, and external validation cohort 2 were 0.82 (0.75-0.90), 0.77 (0.62-0.92), 0.71 (0.56-0.85), and 0.73 (0.60-0.85), respectively. The nomogram, which combined the radiomics score with T category and N category by CT, yielded better performance in the training cohort (AUC = 0.88), internal validation cohort (AUC = 0.80), external validation cohort 1 (AUC = 0.75), and external validation cohort 2 (AUC = 0.76). DCA confirmed the clinical utility of the nomogram. CONCLUSIONS: The CT-based radiomics nomogram has the potential to accurately predict PNI in patients with colorectal cancer.
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