ObjectiveBased on the latest version of the Database from Colorectal Cancer(DACCA), this study analyzed the long-term effect of neoadjuvant therapy combined with intersphincteric resection (ISR) in patients with rectal cancer. MethodsAccording to the established screening criteria, clinical data of 944 patients with rectal cancer admitted from January 2009 to December 2020 were collected from the DACCA updated on March 21, 2022, to explore the influencing factors for overall survival (OS) and disease specific survival (DSS) of rectal cancer treated with neoadjuvant therapy combined with ISR, by Cox proportional hazard regression model. Results① The 3-year OS and DSS survival rates of neoadjuvant therapy combined with ISR for rectal cancer were 89.2% and 90.4%, respectively, and the 5-year OS and DSS survival rates were 83.9% and 85.4%, respectively. ② For different ISR surgical methods and neoadjuvant therapy plans, there were no significant differences in OS and DSS (P>0.05), but there were significant differences in OS and DSS among different ypTNM stage groups (P<0.001), patients with ypTNM 0–Ⅱ had better OS and DSS. ③ BMI, ypTNM stage and R0 resection were influencing factors for OS and DSS (P<0.05). ④ The overall incidence of postoperative complications was low, including 6.4% (60/944) within 30 days, 7.5% (71/944) within half a year and 3.3% (31/944) over half a year after operation. ConclusionsIn the comprehensive treatment of patients with low/ultra-low rectal cancer, neoadjuvant therapy combined with ISR can achieve relatively stable and good long-term oncological efficacy, and the incidence of short-term postoperative complications is not high, which is one of the options.
ObjectiveTo 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). MethodsA 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 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. ResultsAmong the 199 patients, 155 (22.1%) had good therapeutic outcomes, while 44 (77.9%) had poor outcomes. Univariate logistic regression 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. Logistic regression (LR), support vector machine (SVM), random forest (RF), and XGBoost models were established. In the training set, the AUC values of LR, SVM, RF, XGBoost models 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. ConclusionsThe 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.