HU Yiguang 1,2 , LIU Biyao 1,2 , ZHANG Wendi 1,2 , PU Ruifang 2 , KONG Huizhen 2 , TIAN Junqi 3 , WANG Yong 1 , WANG Xiaodong 1
  • 1. Division of Gastrointestinal Surgery, Department of General Surgery, West China Hospital, Sichuan University, Chengdu 610041, P. R. China;
  • 2. West China School of Medicine, Sichuan University, Chengdu 610041, P. R. China;
  • 3. School of Computer Science, Sichuan University, Chengdu 610065, P. R. China;
WANG Yong, Email: wy642192587@163.com; WANG Xiaodong, Email: lockwan@163.com
rectal cancer; neoadjuvant chemoradiotherapy; machine learning; radiomics; tumor regression grade
10.7507/1007-9424.202411009
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Objective To construct a multimodal imaging radiomics model based on enhanced CT features to predict tumor regression grade (TRG) in patients with locally advanced rectal cancer (LARC) following neoadjuvant chemoradiotherapy (NCRT). Methods A retrospective analysis was conducted on the Database from Colorectal Cancer (DACCA) at West China Hospital of Sichuan University, including 199 LARC patients treated from October 2016 to October 2023. All patients underwent total mesorectal excision after NCRT. Clinical pathological information was collected, and radiomics features were extracted from CT images prior to NCRT. Python 3.13.0 was used for feature dimension reduction, and univariate logistic regression (LR) along with Lasso regression with 5-fold cross-validation were applied to select radiomics features. Patients were randomly divided into training and testing sets at a ratio of 7∶3 for machine learning and joint model construction. The model’s performance was evaluated using accuracy, sensitivity, specificity, and the area under the curve (AUC). Receiver operating characteristic curve (ROC), confusion matrices, and clinical decision curves (DCA) were plotted to assess the model’s performance. Results Among the 199 patients, 155 (77.89%) had poor therapeutic outcomes, while 44 (22.11%) had good outcomes. Univariate LR and Lasso regression identified 8 clinical pathological features and 5 radiomic features, including 1 shape feature, 2 first-order statistical features, and 2 texture features. LR, support vector machine (SVM), random forest (RF), and eXtreme gradient boosting (XGBoost) models were established. In the training set, the AUC values of LR, SVM, RF, XGBoost models were 0.99, 0.98, 1.00, and 1.00, respectively, with accuracy rates of 0.94, 0.93, 1.00, and 1.00, sensitivity rates of 0.98, 1.00, 1.00, and 1.00, and specificity rates of 0.80, 0.67, 1.00, and 1.00, respectively. In the testing set, the AUC values of 4 models were 0.97, 0.92, 0.96, and 0.95, with accuracy rates of 0.87, 0.87, 0.88, and 0.90, sensitivity rates of 1.00, 1.00, 1.00, and 0.95, and specificity rates of 0.50, 0.50, 0.56, and 0.75. Among the models, the XGBoost model had the best performance, with the highest accuracy and specificity rates. DCA indicated clinical benefits for all 4 models. Conclusions The multimodal imaging radiomics model based on enhanced CT has good clinical application value in predicting the efficacy of NCRT in LARC. It can accurately predict good and poor therapeutic outcomes, providing personalized clinical surgical interventions.

Citation： HU Yiguang, LIU Biyao, ZHANG Wendi, PU Ruifang, KONG Huizhen, TIAN Junqi, WANG Yong, WANG Xiaodong. A study on predictive models for the efficacy of neoadjuvant chemoradiotherapy in locally advanced rectal cancer based on CT radiomics. CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY, 2025, 32(2): 205-212. doi: 10.7507/1007-9424.202411009 Copy

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