Yan-Song Yang1,2, Feng Feng2, Yong-Juan Qiu2, Gui-Hua Zheng3, Ya-Qiong Ge4, Yue-Tao Wang5. 1. Department of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, No.185, Juqian Street, Changzhou, 213003, Jiangsu, China. 2. Department of Radiology, Affiliated Cancer Hospital of Nantong University, Nantong, 226001, Jiangsu, China. 3. Department of Pathology, Affiliated Cancer Hospital of Nantong University, Nantong, 226001, Jiangsu, China. 4. GE Healthcare, Shanghai, 210000, China. 5. Department of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, No.185, Juqian Street, Changzhou, 213003, Jiangsu, China. yuetao-w@163.com.
Abstract
PURPOSE: To establish and validate two predictive radiomics models for preoperative prediction of lymph node metastases (LNMs) and tumor deposits (TDs) respectively in rectal cancer (RC) patients. METHODS: A total of 139 RC patients (98 in the training cohort and 41 in the validation cohort) were enrolled in the present study. High-resolution magnetic resonance images (HRMRI) were retrieved for tumor segmentation and feature extraction. HRMRI findings of RC were assessed by three experienced radiologists. Two radiomics nomograms were established by integrating the clinical risk factors, HRMRI findings and radiomics signature. RESULTS: The predictive nomogram of LNMs showed good predictive performance (area under the curve [AUC], 0.90; 95% confidence interval [CI] 0.83-0.96) which was better than clinico-radiological (AUC, 0.83; 95% CI 0.74-0.93; Delong test, p = 0.017) or radiomics signature-only model (AUC, 0.77; 95% CI 0.67-0.86; Delong test, p = 0.003) in training cohort. Application of the nomogram in the validation cohort still exhibited good performance (AUC, 0.87; 95% CI 0.76-0.98). The accuracy, sensitivity and specificity of the combined model in predicting LNMs was 0.86,0.79 and 0.91 in training cohort and 0.83,0.85 and 0.82 in validation cohort. As for TDs, the predictive efficacy of the nomogram (AUC, 0.82; 95% CI 0.71-0.93) was not significantly higher than radiomics signature-only model (AUC, 0.80; 95% CI 0.69-0.92; Delong test, p = 0.71). Radiomics signature-only model was adopted to predict TDs with accuracy=0.76, sensitivity=0.72 and specificity=0.94 in training cohort and 0.68, 0.62 and 0.97 in validation cohort. CONCLUSION: HRMRI-based radiomics models could be helpful for the prediction of LNMs and TDs preoperatively in RC patients.
PURPOSE: To establish and validate two predictive radiomics models for preoperative prediction of lymph node metastases (LNMs) and tumor deposits (TDs) respectively in rectal cancer (RC) patients. METHODS: A total of 139 RC patients (98 in the training cohort and 41 in the validation cohort) were enrolled in the present study. High-resolution magnetic resonance images (HRMRI) were retrieved for tumor segmentation and feature extraction. HRMRI findings of RC were assessed by three experienced radiologists. Two radiomics nomograms were established by integrating the clinical risk factors, HRMRI findings and radiomics signature. RESULTS: The predictive nomogram of LNMs showed good predictive performance (area under the curve [AUC], 0.90; 95% confidence interval [CI] 0.83-0.96) which was better than clinico-radiological (AUC, 0.83; 95% CI 0.74-0.93; Delong test, p = 0.017) or radiomics signature-only model (AUC, 0.77; 95% CI 0.67-0.86; Delong test, p = 0.003) in training cohort. Application of the nomogram in the validation cohort still exhibited good performance (AUC, 0.87; 95% CI 0.76-0.98). The accuracy, sensitivity and specificity of the combined model in predicting LNMs was 0.86,0.79 and 0.91 in training cohort and 0.83,0.85 and 0.82 in validation cohort. As for TDs, the predictive efficacy of the nomogram (AUC, 0.82; 95% CI 0.71-0.93) was not significantly higher than radiomics signature-only model (AUC, 0.80; 95% CI 0.69-0.92; Delong test, p = 0.71). Radiomics signature-only model was adopted to predict TDs with accuracy=0.76, sensitivity=0.72 and specificity=0.94 in training cohort and 0.68, 0.62 and 0.97 in validation cohort. CONCLUSION: HRMRI-based radiomics models could be helpful for the prediction of LNMs and TDs preoperatively in RC patients.
Entities:
Keywords:
Lymph nodes; Magnetic resonance imaging; Nomogram; Radiomics; Rectal cancer
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