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Purpose <p>PPOI is one of the common complications of intraperitoneal hyperthermic chemotherapy during laparoscopic radical resection of rectal cancer, which seriously affects the prognosis of patients. The purpose of this study is to establish a prediction system for PPOI secondary to laparoscopic radical resection of rectal cancer combined with intraperitoneal hyperthermic chemotherapy with lobaplatin.</p> Materials and methods <p>Retrospectively analyzed the clinical data of 800 patients who received laparoscopic radical rectal cancer combined with lobaplatin hyperthermia and intraperitoneal chemotherapy in three Level 3 Grade A hospitals from June 1, 2014, to June 1, 2024, and determined the predictive factors through univariate, multivariate, and Lasso regression analysis. We employ eight ML algorithms, Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Extreme gradient boosting (XGB), Support Vector Machine (SVM), Multilayer Perceptron (MLP), K-Nearest Neighbor (KNN), Gaussian Naive Bayes (GNB), to train and develop ML models using a 10x cross-validation method. The performance of the model was evaluated by a variety of indicators, including the area under the receiver operating characteristic curve (ROC), calibration curve, decision curve, PR curve, and confusion matrix. In addition, model interpretation is performed through Shapley Additive Interpretation (SHAP) analysis to clarify the importance of each feature of the model and its basis for decision-making.</p> Results <p>We identified six key predictors, including Surgical bleeding, Duration of surgery, HB, WBC, ALB and adhesiolysis, and built a prediction model based on these factors. The sensitivity, specificity, positive predictive value, and negative predictive value of different models were compared. All eight models showed good predictive performance and stability, with the RF model being the optimal model. Finally, we developed a web-based calculator based on the optimal model.</p> Conclusions <p>These predictors and models were able to assess the potential for PPOI following laparoscopic radical curative rectal cancer combined with lobaplatin hyperthermic intraperitoneal chemotherapy. Early alerts can be provided in a clinical setting, helping medical professionals make informed judgments and select the most appropriate treatment strategy (https://zw17786325639.shinyapps.io/ppoi/).</p>

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Predicting PPOI secondary to laparoscopic radical resection of rectal cancer combined with lobaplatin hyperthermic intraperitoneal chemotherapy: a multi-center study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月17日

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  • Wei Zhang,
  • Baitong Che,
  • Nan Yang,
  • Feifei Cheng,
  • Qun Xu,
  • Yaxun Li,
  • Jinkun Xie,
  • Chongshi Gao,
  • Zhiyong Yang,
  • Donglin Ma,
  • Tianxiao Zhang,
  • Zewen Ba,
  • Weibin Zhang

摘要

Purpose

PPOI is one of the common complications of intraperitoneal hyperthermic chemotherapy during laparoscopic radical resection of rectal cancer, which seriously affects the prognosis of patients. The purpose of this study is to establish a prediction system for PPOI secondary to laparoscopic radical resection of rectal cancer combined with intraperitoneal hyperthermic chemotherapy with lobaplatin.

Materials and methods

Retrospectively analyzed the clinical data of 800 patients who received laparoscopic radical rectal cancer combined with lobaplatin hyperthermia and intraperitoneal chemotherapy in three Level 3 Grade A hospitals from June 1, 2014, to June 1, 2024, and determined the predictive factors through univariate, multivariate, and Lasso regression analysis. We employ eight ML algorithms, Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Extreme gradient boosting (XGB), Support Vector Machine (SVM), Multilayer Perceptron (MLP), K-Nearest Neighbor (KNN), Gaussian Naive Bayes (GNB), to train and develop ML models using a 10x cross-validation method. The performance of the model was evaluated by a variety of indicators, including the area under the receiver operating characteristic curve (ROC), calibration curve, decision curve, PR curve, and confusion matrix. In addition, model interpretation is performed through Shapley Additive Interpretation (SHAP) analysis to clarify the importance of each feature of the model and its basis for decision-making.

Results

We identified six key predictors, including Surgical bleeding, Duration of surgery, HB, WBC, ALB and adhesiolysis, and built a prediction model based on these factors. The sensitivity, specificity, positive predictive value, and negative predictive value of different models were compared. All eight models showed good predictive performance and stability, with the RF model being the optimal model. Finally, we developed a web-based calculator based on the optimal model.

Conclusions

These predictors and models were able to assess the potential for PPOI following laparoscopic radical curative rectal cancer combined with lobaplatin hyperthermic intraperitoneal chemotherapy. Early alerts can be provided in a clinical setting, helping medical professionals make informed judgments and select the most appropriate treatment strategy (https://zw17786325639.shinyapps.io/ppoi/).