Development and validation of a machine learning-based risk model for metastatic disease in NmCRPC patients: a tumour marker prognostic study.

IF 12.5 2区 医学 Q1 SURGERY
Xudong Ni, Ziyun Wang, Xiaomeng Li, Jixinnan Sui, Weiwei Ma, Jian Pan, Dingwei Ye, Yao Zhu
{"title":"Development and validation of a machine learning-based risk model for metastatic disease in NmCRPC patients: a tumour marker prognostic study.","authors":"Xudong Ni, Ziyun Wang, Xiaomeng Li, Jixinnan Sui, Weiwei Ma, Jian Pan, Dingwei Ye, Yao Zhu","doi":"10.1097/JS9.0000000000002321","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Nonmetastatic castration-resistant prostate cancer (nmCRPC) is a clinical challenge due to the high progression rate to metastasis and mortality. To date, no prognostic model has been developed to predict the metastatic probability for nmCRPC patients. In this study, we developed and externally validated a machine-learning model capable of calculating risk scores and predicting the likelihood of metastasis in nmCRPC patients.</p><p><strong>Patients and methods: </strong>A total of 2,716 nmCRPC patients were included in this study. The training and testing datasets were derived from Clinical Trial A (The clinical trial's name and NCT number are concealed by the double-blind review policy) and Clinical Trial B, respectively. Regarding metastasis-free survival (MFS) as the endpoint, we subjected 13 clinical features to 10 machine-learning models and their combinations to predict metastasis. Model performance was assessed through accuracy (AUC), calibration (slope and intercept), and clinical utility (DCA). The risk score calculated by the model and risk factors based on eight identified variates were used for metastatic risk stratification.</p><p><strong>Results: </strong>The final prognostic model included eight prognostic factors, including novel hormone therapy (NHT) application, Gleason score, previous treatments received (both surgery and radiotherapy, or neither), Race (White), PSA doubling time (PSADT), hemoglobin (HGB), and lgPSA. The prognostic model resulted in a C-index of 0.724 (95% CI 0.700-0.747) in internal validation and relatively good performance through tAUC (>0.70 at 3-month intervals between 6 and 39 months) in external validation. In the risk score stratifying strategy, compared with the low-risk group, the metastasis HRs for medium- and high-risk groups were 1.72 (95% CI 1.39-2.12) and 4.43 (95% CI 3.66-5.38); as for risk factor count, the HRs are 1.98 (95% CI 1.50-2.61) and 4.17 (95% CI 3.16-5.52), respectively.</p><p><strong>Conclusions: </strong>In this study, we developed and validated a machine learning prognostic model to predict the risk of metastasis in nmCRPC patients. This model can assist in the risk stratification of nmCRPC patients, guide follow-up strategies, and aid in selecting personalized treatment intensities.</p><p><strong>Key words: </strong>Nonmetastatic castration-resistant prostate cancer; Prostate Cancer; Machine learning; Prognostic model; Metastasis-Free Survival.</p>","PeriodicalId":14401,"journal":{"name":"International journal of surgery","volume":" ","pages":""},"PeriodicalIF":12.5000,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International journal of surgery","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1097/JS9.0000000000002321","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"SURGERY","Score":null,"Total":0}
引用次数: 0

Abstract

Background: Nonmetastatic castration-resistant prostate cancer (nmCRPC) is a clinical challenge due to the high progression rate to metastasis and mortality. To date, no prognostic model has been developed to predict the metastatic probability for nmCRPC patients. In this study, we developed and externally validated a machine-learning model capable of calculating risk scores and predicting the likelihood of metastasis in nmCRPC patients.

Patients and methods: A total of 2,716 nmCRPC patients were included in this study. The training and testing datasets were derived from Clinical Trial A (The clinical trial's name and NCT number are concealed by the double-blind review policy) and Clinical Trial B, respectively. Regarding metastasis-free survival (MFS) as the endpoint, we subjected 13 clinical features to 10 machine-learning models and their combinations to predict metastasis. Model performance was assessed through accuracy (AUC), calibration (slope and intercept), and clinical utility (DCA). The risk score calculated by the model and risk factors based on eight identified variates were used for metastatic risk stratification.

Results: The final prognostic model included eight prognostic factors, including novel hormone therapy (NHT) application, Gleason score, previous treatments received (both surgery and radiotherapy, or neither), Race (White), PSA doubling time (PSADT), hemoglobin (HGB), and lgPSA. The prognostic model resulted in a C-index of 0.724 (95% CI 0.700-0.747) in internal validation and relatively good performance through tAUC (>0.70 at 3-month intervals between 6 and 39 months) in external validation. In the risk score stratifying strategy, compared with the low-risk group, the metastasis HRs for medium- and high-risk groups were 1.72 (95% CI 1.39-2.12) and 4.43 (95% CI 3.66-5.38); as for risk factor count, the HRs are 1.98 (95% CI 1.50-2.61) and 4.17 (95% CI 3.16-5.52), respectively.

Conclusions: In this study, we developed and validated a machine learning prognostic model to predict the risk of metastasis in nmCRPC patients. This model can assist in the risk stratification of nmCRPC patients, guide follow-up strategies, and aid in selecting personalized treatment intensities.

Key words: Nonmetastatic castration-resistant prostate cancer; Prostate Cancer; Machine learning; Prognostic model; Metastasis-Free Survival.

求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
17.70
自引率
3.30%
发文量
0
审稿时长
6-12 weeks
期刊介绍: The International Journal of Surgery (IJS) has a broad scope, encompassing all surgical specialties. Its primary objective is to facilitate the exchange of crucial ideas and lines of thought between and across these specialties.By doing so, the journal aims to counter the growing trend of increasing sub-specialization, which can result in "tunnel-vision" and the isolation of significant surgical advancements within specific specialties.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信