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Objective <p>Postoperative biochemical recurrence (BCR) of prostate cancer (PCa) remains a major clinical challenge, and traditional risk assessment systems show suboptimal predictive performance for PCa recurrence. This study aimed to develop and validate interpretable machine learning (ML) models for predicting PCa postoperative recurrence by integrating multi-dimensional clinical features, and to construct a simplified and practical prognostic model for individualized risk stratification.</p> Methods <p>A total of 320 PCa patients (125 recurrences vs. 195 non-recurrences) who underwent laparoscopic radical prostatectomy (LRP) at the primary center were retrospectively enrolled as the internal cohort, and 144 patients (50 recurrences vs. 94 non-recurrences) from another campus were included as the external validation cohort. Ten ML algorithms were used to construct prediction models with clinicopathological, preoperative hematological and nutrition-inflammation features. Stratified sampling and ten-fold cross-validation were used for model training and validation, and SHAP analysis was adopted for feature importance evaluation and model interpretability. Recursive feature inclusion was performed to optimize the model, and clinical cutoffs of key indicators were determined.</p> Results <p>The gradient boosting machine (GBM) model achieved the best predictive performance in the internal cohort with an AUC of 0.891, which was significantly superior to the UCSF-CAPRA score (AUC = 0.703) and the D’Amico classification (AUC = 0.610). A simplified 5-feature GBM model [positive surgical margin, preoperative hemoglobin-albumin-lymphocyte-platelet (HALP) score, postoperative Gleason score, preoperative maximum prostate specific antigen, preoperative lactate dehydrogenase (LDH)] achieved an AUC of 0.912 in the internal cohort and 0.895 in the external cohort, with excellent calibration and higher net clinical benefit. The optimal cutoffs were 41.31 for preoperative HALP score and 182.61 U/L for preoperative LDH. Low HALP was associated with shorter recurrence-free survival (HR = 0.30, <i>P</i> &lt; 0.0001), and high LDH indicated increased recurrence risk (HR = 1.37, <i>P</i> = 0.083). A three-tier risk stratification system was established based on the cutoff values to predict postoperative recurrence risk.</p> Conclusion <p>The ML model integrating preoperative HALP score, LDH and core clinicopathological features has high accuracy and good clinical applicability for predicting PCa postoperative recurrence. Validated successfully in both internal and external cohorts, the 5-feature simplified model can serve as a practical tool for individualized recurrence risk assessment, facilitating optimized clinical management of PCa patients.</p>

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Machine learning-based prognostic model integrating preoperative HALP score and lactate dehydrogenase for predicting postoperative recurrence of prostate cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月27日

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  • Hao Wang,
  • Xuemeng Qiu,
  • Zhen Li,
  • Jiyue Wu,
  • Lijian Gan,
  • Dawei Xie,
  • Yirui Wei,
  • Jianwen Wang,
  • Wei Wang

摘要

Objective

Postoperative biochemical recurrence (BCR) of prostate cancer (PCa) remains a major clinical challenge, and traditional risk assessment systems show suboptimal predictive performance for PCa recurrence. This study aimed to develop and validate interpretable machine learning (ML) models for predicting PCa postoperative recurrence by integrating multi-dimensional clinical features, and to construct a simplified and practical prognostic model for individualized risk stratification.

Methods

A total of 320 PCa patients (125 recurrences vs. 195 non-recurrences) who underwent laparoscopic radical prostatectomy (LRP) at the primary center were retrospectively enrolled as the internal cohort, and 144 patients (50 recurrences vs. 94 non-recurrences) from another campus were included as the external validation cohort. Ten ML algorithms were used to construct prediction models with clinicopathological, preoperative hematological and nutrition-inflammation features. Stratified sampling and ten-fold cross-validation were used for model training and validation, and SHAP analysis was adopted for feature importance evaluation and model interpretability. Recursive feature inclusion was performed to optimize the model, and clinical cutoffs of key indicators were determined.

Results

The gradient boosting machine (GBM) model achieved the best predictive performance in the internal cohort with an AUC of 0.891, which was significantly superior to the UCSF-CAPRA score (AUC = 0.703) and the D’Amico classification (AUC = 0.610). A simplified 5-feature GBM model [positive surgical margin, preoperative hemoglobin-albumin-lymphocyte-platelet (HALP) score, postoperative Gleason score, preoperative maximum prostate specific antigen, preoperative lactate dehydrogenase (LDH)] achieved an AUC of 0.912 in the internal cohort and 0.895 in the external cohort, with excellent calibration and higher net clinical benefit. The optimal cutoffs were 41.31 for preoperative HALP score and 182.61 U/L for preoperative LDH. Low HALP was associated with shorter recurrence-free survival (HR = 0.30, P < 0.0001), and high LDH indicated increased recurrence risk (HR = 1.37, P = 0.083). A three-tier risk stratification system was established based on the cutoff values to predict postoperative recurrence risk.

Conclusion

The ML model integrating preoperative HALP score, LDH and core clinicopathological features has high accuracy and good clinical applicability for predicting PCa postoperative recurrence. Validated successfully in both internal and external cohorts, the 5-feature simplified model can serve as a practical tool for individualized recurrence risk assessment, facilitating optimized clinical management of PCa patients.