Establishment and application of three predictive models of anastomotic leakage after rectal cancer sphincter-preserving surgery

Interpretable Prognostic Modeling for Postoperative Pancreatic Cancer Using Multi-machine Learning and Habitat Radiomics: A Multi-center Study

RATIONALE AND OBJECTIVES

Accurate risk stratification is critical for guiding personalized treatment in resectable pancreatic cancer (RPC). This retrospective study assessed the utility of habitat radiomics for predicting recurrence-free survival (RFS) in RPC patients.

MATERIALS AND METHODS

A total of 455 RPC patients were divided into training and external test sets from January 2018 to July 2024. Tumors were segmented into subregions using habitat radiomics to capture localized heterogeneity. Seven machine learning models, including random survival forest (RSF), were compared using Harrell's C-index. The optimal model underwent further validation through time-dependent ROC and Kaplan-Meier (KM) analyses. Shapley additive explanations (SHAP) and survival local interpretable model-agnostic explanations (SurvLIME) were applied to enhance model interpretability.

RESULTS

The RSF model based on habitat radiomics achieved a C-index of 0.828 in the training cohort and 0.702, 0.680 in external test sets, outperforming whole-tumor radiomics (p<0.05). Time-dependent ROC analysis showed AUCs of 0.71, 0.83, and 0.79 at 0.5, 1, and 2 years in the first test set, and 0.65, 0.79, and 0.75 in the second test set. KM analysis revealed that the predicted low-risk groups had significantly longer RFS compared to the predicted high-risk groups in both external test sets (all p<0.05). Interpretability analysis identified key variables, including Feature 1, Feature 5, Feature 2, and Feature 4 from Habitat Subregion 1, and Feature 3 from Habitat Subregion 3.

CONCLUSION

The habitat radiomics RSF machine learning model improves prognostic accuracy and interpretability for postoperative RPC, providing a promising tool for personalized management.

Multi-Modal Machine Learning for Evaluating the Predictive Value of Pelvimetric Measurements (Pelvimetry) for Anastomotic Leakage After Restorative Low Anterior Resection

Background/Objectives: Anastomotic leakage (AL) remains a major complication after restorative rectal cancer surgery, with accurate preoperative risk stratification posing a significant challenge. Pelvic measurements derived from magnetic resonance imaging (MRI) have been proposed as potential predictors of AL, but their clinical utility remains uncertain. Methods: This retrospective, multicenter cohort study analyzed rectal cancer patients undergoing restorative surgery between 2013 and 2021. Pelvic dimensions were assessed using MRI-based pelvimetry. Univariate and multivariate regression analyses identified independent risk factors for AL. Subsequently, machine Learning (ML) models-logistic regression, random forest classifier, and XGBoost-were developed to predict AL using preoperative clinical data alone and in combination with pelvimetry. Model performance was evaluated using F1 scores, with the area under the receiver operating characteristic (ROC-AUC) and precision-recall curves (AUC-PR) as primary metrics. Results: Among 487 patients, the overall AL rate was 14%. Multivariate regression analysis identified distance to the anorectal junction, pelvic inlet width, and interspinous distance as independent risk factors for AL (p < 0.05). The logistic regression model incorporating pelvimetry achieved the highest predictive performance, with a mean ROC-AUC of 0.70 ± 0.09 and AUC-PR of 0.32 ± 0.10. Although predictive models that included pelvic measurements demonstrated higher ROC-AUCs compared to those without pelvimetry, the improvement was not statistically significant. Conclusions: Pelvic dimensions, specifically pelvic inlet and interspinous distance, were independently associated with an increased risk of AL. While ML models incorporating pelvimetry showed only moderate predictive performance, these measurements should be considered in developing clinical prediction tools for AL to enhance preoperative risk stratification.

Serum nutritional predictive biomarkers and risk assessment for anastomotic leakage after laparoscopic surgery in rectal cancer patients

BACKGROUND

Anastomotic leakage (AL) is one of the severest complications after laparoscopic surgery for middle/low rectal cancer, significantly impacting patient outcomes. Identifying reliable predictive factors for AL remains a clinical challenge. Serum nutritional biomarkers have been implicated in surgical outcomes but are underexplored as predictive tools for AL in this setting. Our study hypothesizes that preoperative serum levels of prealbumin (PA), albumin (ALB), and transferrin (TRF), along with surgical factors, can accurately predict AL risk.

AIM

To determine the predictive value of preoperative serum nutritional biomarkers for rectal cancer AL following laparoscopic surgery.

METHODS

In the retrospective cohort study carried out at a tertiary cancer center, we examined 560 individuals who underwent laparoscopic procedures for rectal cancer from 2018 to 2022. Preoperative serum levels of PA, ALB, and TRF were measured. We employed multivariate logistic regression to determine the independent risk factors for AL, and a predictive model was constructed and evaluated using receiver operating characteristic curve analysis.

RESULTS

AL occurred in 11.96% of cases, affecting 67 out of 560 patients. Multivariate analysis identified PA, ALB, and TRF as the independent risk factor, each with an odds ratio of 2.621 [95% confidence interval (CI): 1.582-3.812, P = 0.012], 3.982 (95%CI: 1.927-4.887, P = 0.024), and 2.109 (95%CI: 1.162-2.981, P = 0.031), respectively. Tumor location (< 7 cm from anal verge) and intraoperative bleeding ≥ 300 mL also increased AL risk. The predictive model demonstrated an excellent accuracy, achieving an area under the receiver operating characteristic curve of 0.942, a sensitivity of 0.844, and a specificity of 0.922, demonstrating an excellent ability to discriminate.

CONCLUSION

Preoperative serum nutritional biomarkers, combined with surgical factors, reliably predict anastomotic leakage risk after rectal cancer surgery, highlighting their importance in preoperative assessment.