A systematic review and meta-analysis of lung cancer risk prediction models.

IF 2.7 3区医学 Q3 ONCOLOGY

Acta Oncologica Pub Date : 2025-05-12 DOI:10.2340/1651-226X.2025.42529

Ghida Khalife, Matilda Nilsson, Lotta Peltola, Juho Waris, Antti Jekunen, Riikka-Leena Leskelä, Heidi Andersén, Mikko Nuutinen, Eija Heikkilä, Susanna Nurmi-Rantala, Paulus Torkki

{"title":"A systematic review and meta-analysis of lung cancer risk prediction models.","authors":"Ghida Khalife, Matilda Nilsson, Lotta Peltola, Juho Waris, Antti Jekunen, Riikka-Leena Leskelä, Heidi Andersén, Mikko Nuutinen, Eija Heikkilä, Susanna Nurmi-Rantala, Paulus Torkki","doi":"10.2340/1651-226X.2025.42529","DOIUrl":null,"url":null,"abstract":"Background: Lung cancer (LC) remains the leading cause of cancer-related mortality worldwide. Early detection through targeted screening significantly improves patient outcomes. However, identifying high-risk individuals remains a critical challenge.Purpose: This systematic review evaluates externally validated LC risk prediction models to assess their performance and potential applicability in screening strategies.Methods: Of the 11,805 initial studies, 66 met inclusion criteria and 38 published mainly between 2020 and 2024 were included in the final analysis. Model methodologies, validation approaches, and performance metrics were extracted and compared.Results: The review identified 18 models utilising conventional machine learning, six employing neural networks, and 14 comparing different predictive frameworks. The Prostate Lung Colorectal and Ovarian Cancer Screening Trial (PLCOm2012) demonstrated superior sensitivity across diverse populations, while newer models, such as Optimized Early Warning model for Lung cancer risk (OWL) and CanPredict, showed promising results. However, differences in population demographics and healthcare systems may limit the generalisability of these models.Interpretation: While LC risk prediction models have advanced, their applicability to specific healthcare systems, such as Finland's, requires further adaptation and validation. Future research should focus on optimising these models for local contexts to improve clinical impact and cost-effectiveness in targeted screening programmes.Systematic review registration: PROSPERO CRD42022321391.","PeriodicalId":7110,"journal":{"name":"Acta Oncologica","volume":"64 ","pages":"661-671"},"PeriodicalIF":2.7000,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12086449/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Acta Oncologica","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.2340/1651-226X.2025.42529","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ONCOLOGY","Score":null,"Total":0}

引用次数: 0

Abstract

Background: Lung cancer (LC) remains the leading cause of cancer-related mortality worldwide. Early detection through targeted screening significantly improves patient outcomes. However, identifying high-risk individuals remains a critical challenge.

Purpose: This systematic review evaluates externally validated LC risk prediction models to assess their performance and potential applicability in screening strategies.

Methods: Of the 11,805 initial studies, 66 met inclusion criteria and 38 published mainly between 2020 and 2024 were included in the final analysis. Model methodologies, validation approaches, and performance metrics were extracted and compared.

Results: The review identified 18 models utilising conventional machine learning, six employing neural networks, and 14 comparing different predictive frameworks. The Prostate Lung Colorectal and Ovarian Cancer Screening Trial (PLCOm2012) demonstrated superior sensitivity across diverse populations, while newer models, such as Optimized Early Warning model for Lung cancer risk (OWL) and CanPredict, showed promising results. However, differences in population demographics and healthcare systems may limit the generalisability of these models.

Interpretation: While LC risk prediction models have advanced, their applicability to specific healthcare systems, such as Finland's, requires further adaptation and validation. Future research should focus on optimising these models for local contexts to improve clinical impact and cost-effectiveness in targeted screening programmes.

Systematic review registration: PROSPERO CRD42022321391.

查看原文本刊更多论文

肺癌风险预测模型的系统回顾和荟萃分析。

背景：肺癌（LC）仍然是世界范围内癌症相关死亡的主要原因。通过有针对性的筛查早期发现可显著改善患者预后。然而，识别高危人群仍然是一项重大挑战。目的：本系统综述评估外部验证的LC风险预测模型，以评估其在筛选策略中的性能和潜在适用性。方法：在11805项初始研究中，66项符合纳入标准，38项主要发表于2020 - 2024年的研究纳入最终分析。提取并比较了模型方法、验证方法和性能指标。结果：该综述确定了18个使用传统机器学习的模型，6个使用神经网络，14个比较不同的预测框架。前列腺肺结直肠癌和卵巢癌筛查试验（PLCOm2012）在不同人群中显示出优越的敏感性，而较新的模型，如肺癌风险优化早期预警模型（OWL）和CanPredict，显示出令人鼓舞的结果。然而，人口统计和医疗保健系统的差异可能会限制这些模型的普遍性。解释：虽然LC风险预测模型已经取得了进步，但其对特定医疗保健系统（如芬兰的医疗保健系统）的适用性需要进一步调整和验证。未来的研究应该集中于优化这些模型以适应当地情况，从而提高针对性筛查项目的临床影响和成本效益。系统评价注册：PROSPERO CRD42022321391。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Acta Oncologica 医学-肿瘤学

CiteScore

4.30

自引率

3.20%

发文量

301

审稿时长

3 months

期刊介绍： Acta Oncologica is a journal for the clinical oncologist and accepts articles within all fields of clinical cancer research. Articles on tumour pathology, experimental oncology, radiobiology, cancer epidemiology and medical radio physics are also welcome, especially if they have a clinical aim or interest. Scientific articles on cancer nursing and psychological or social aspects of cancer are also welcomed. Extensive material may be published as Supplements, for which special conditions apply.