Prognostic model for log odds of negative lymph node in locally advanced rectal cancer via interpretable machine learning - Ye Wang; Zhen Pan; Huajun Cai; Shoufeng Li; Ying Huang; Jinfu Zhuang; Xing Liu; Guoxian Guan (Scientific Reports)
Abstract No studies have examined the prognostic value of the log odds of negative lymph nodes/T stage (LONT) in locally advanced rectal cancer (LARC) treated with neoadjuvant chemoradiotherapy (nCRT). We aimed to assess the prognostic value of LONT and develop a machine learning model to predict overall survival (OS) and disease-free survival (DFS) in LARC patients treated with nCRT. The study included 820 LARC patients who received nCRT between September 2010 and October 2017. Univariate and multivariate Cox regression analyses identified prognostic factors, which were then used to develop risk assessment models with 9 machine learning algorithms. Model hyperparameters were optimized using random search and 10-fold cross-validation. The models were evaluated using metrics such as the area under the receiver operating characteristic curves (AUC), decision curve analysis, calibration curves, and precision and accuracy for predicting OS and DFS. Shapley’s additive explanations (SHAP) was also used for model interpretation. The study included 820 patients, identifying LONT as a significant independent prognostic factor for both OS and DFS. Nine machine learning algorithms were used to create predictive models based on these factors. The extreme gradient boosting (XGB) model showed the best performance, with a mean AUC of 0.89 for OS and 0.83 for DFS in 10-fold cross-validation. Additionally, the predictions generated by the XGB model were analyzed using SHAP. Finally, we developed an online web-based calculator utilizing the XGB model to enhance the model’s generalizability and to provide improved support for physicians in their decision-making processes. The study developed an XGB model utilizing LONT to predict OS and DFS in patients with LARC undergoing nCRT. Furthermore, an online web calculator was constructed using the XGB model to facilitate the model’s generalization and to enhance physician decision-making.