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

World Journal of Surgical Oncology https://doi.org/10.1186/s12957-026-04423-2 Article in Press Machine learning-based prognostic model integrating preoperative HALP score and lactate dehydrogenase for predicting postoperative recurrence of prostate cancer Hao Wang, Xuemeng Qiu, Zhen Li, Jiyue Wu, Lijian Gan, Dawei Xie, Yirui Wei, Jianwen Wang & Wei Wang Received: 13 March 2026 Accepted: 18 May 2026 Cite this article as: Wang H., Qiu X., Li Z. et al. Machine learning-based prognostic model integrating preoperative HALP score and lactate dehydrogenase for predicting postoperative recurrence of prostate cancer. World J Surg Onc (2026). https:// doi.org/10.1186/s12957-026-04423-2 A E R P S S We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply. IN If this paper is publishing under a Transparent Peer Review model then Peer Review reports will publish with the final article. I T R E L C © The Author(s) 2026. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/. ARTICLE IN PRESS Machine Learning-Based Prognostic Model Integrating Preoperative HALP Score and Lactate Dehydrogenase for Predicting Postoperative Recurrence of Prostate Cancer Hao Wang1†, Xuemeng Qiu1†, Zhen Li1†, Jiyue Wu1, Lijian Gan1, Dawei Xie1, Yirui Wei1, Jianwen Wang1*, and Wei Wang1* 1 Department of Urology, Beijing Chaoyang Hospital, Capital Medical University, Beijing, 100020, China * Correspondence: Jian-wen Wang, Email: Wei Wang, Email: †: These authors contributed equally to this work and should be considered co-first authors. Abstract Objective: Postoperative biochemical recurrence (BCR) of prostate cancer (PCa) remains a major clinical challenge, and traditional risk assessment systems show suboptimal predictive S S E R P 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. E L C IN Methods: A total of 320 PCa patients (125 recurrences vs. 195 non-recurrences) who underwent I T AR 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 hemoglobin-albumin-lymphocyte-platelet (HALP) surgical score, margin, postoperative preoperative Gleason score, preoperative maximum prostate specific antigen, preoperative lactate dehydrogenase (LDH)] ARTICLE IN PRESS 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. Keywords: prostate cancer; biochemical recurrence; machine learning; HALP score; lactate dehydrogenase; prognostic prediction 1 Introduction S S E R P Prostate cancer (PCa) is one of the most common cancers among middle-aged and elderly IN men worldwide, and represents a major threat to physical health and quality of life[1]. Radical E L C prostatectomy (RP) remains the gold-standard treatment for localized PCa, which can prolong life expectancy and improve prognosis[2]. Despite continuous advances in surgical techniques, I T AR postoperative biochemical recurrence (BCR) continues to be a common and clinically important problem, closely linked to worse long-term survival and poorer quality of life[3]. Traditional risk assessment systems, such as tumor grade, pathological stage, and the D’Amico risk classification, only use limited clinical and pathological variables. Their predictive performance for PCa recurrence remains suboptimal, and is insufficient to support personalized prognostic evaluation and precision clinical decision-making[4]. Artificial intelligence (AI) and big data are changing urological oncology by making diagnosis and outcome assessment more accurate, while also facilitating personalized treatment for prostate and other urological cancers[5]. In recent years, machine learning (ML) has become a common approach for developing prognostic models in oncology[6, 7]. By integrating different types of clinical information, ML methods can de (...truncated)