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ORIGINAL RESEARCH article

Front. Oncol.

Sec. Genitourinary Oncology

Volume 15 - 2025 | doi: 10.3389/fonc.2025.1603993

This article is part of the Research TopicEnhancing Prostate Cancer Diagnosis: Biomarkers and Imaging for Improved Patient OutcomesView all 15 articles

Prognostic value and predictive model construction for patients undergoing laparoscopic radical prostatectomy based on the preoperative NPL-IRS score and prognostic nutritional index

Provisionally accepted
Hao  WangHao WangPu-Shen  YangPu-Shen YangWei  YiruiWei YiruiDawei  XieDawei XieWANG  SiqiWANG SiqiWei-Feng  HeWei-Feng HeWei  WangWei Wang*Jian-Wen  WangJian-Wen Wang*
  • Beijing Chaoyang Hospital, Capital Medical University, Beijing, China

The final, formatted version of the article will be published soon.

Objective: To explore the prognostic value of preoperative hematological indicators for prostate cancer (PCa) patients with laparoscopic radical prostatectomy (LRP) and construct a nomogram prediction model based on hematological indicators and clinicopathological characteristics. Method: PCa patients who underwent LRP in Beijing Chaoyang Hospital from January 2017 to December 2022 were retrospectively analyzed. Clinicopathological data and blood indicators, including the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), red blood cell distribution width (RDW), prognosis nutritional index were compared between non-recurrence and recurrence groups. The NPL-IRS score was inflammatory response system score based on the cut-off values NLR, PLR, LMR. Kaplan-Meier analysis was used to calculate the prognostic survival curve. Univariable and multivariable Cox regression risk models were used to identify independent risk factors. A nomogram prediction model was developed, and its accuracy was evaluated and validated through receiver operating characteristic (ROC) curve, C-index, and calibration curve. Internal validation was conducted using Bootstrap method, and the model was also evaluated through external validation. Results: The number of PCa patients in the training set and external validation set was 210 and 110, respectively. A higher NLR, PLR, RDW, and NPL-IRS score but lower LMR and prognosis nutritional index levels were related to a poor recurrence-free survival (RFS). In training set, the area under the curve (AUC) of the NLR, PLR, LMR, NPL-IRS score, prognosis nutritional index, and RDW were 0.735, 0.710, 0.719, 0.768, 0.728, and 0.599, respectively. Prostate specific antigen density (PSAD), prognosis nutritional index, NPL-IRS score, Gleason score (GS), and positive surgical margin (PSM) were independent risk factors. A new nomogram model was constructed based on these parameters to predict one-year, three-year, and five-year RFS with the AUC of 0.828, 0.867, and 0.892, which could provide an additional clinical net benefit. In external validation set, the AUCs were 0.847, 0.894, and 0.906, respectively. Conclusions: Higher preoperative NLR, PLR, and RDW or lower LMR and prognosis nutritional index are associated with poorer RFS. The nomogram prediction model based on preoperative PSAD, prognosis nutritional index, NPL-IRS score, GS, and PSM provides important postoperative treatment guidance.

Keywords: prostate cancer, laparoscopic radical prostatectomy, NPL-IRS score, Prognostic nutritional index, prognosis

Received: 01 Apr 2025; Accepted: 31 Jul 2025.

Copyright: © 2025 Wang, Yang, Yirui, Xie, Siqi, He, Wang and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence:
Wei Wang, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China
Jian-Wen Wang, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China

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