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":"肺癌风险预测模型的系统回顾和荟萃分析。","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":"<p><strong>Background: </strong>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.</p><p><strong>Purpose: </strong>This systematic review evaluates externally validated LC risk prediction models to assess their performance and potential applicability in screening strategies.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Interpretation: </strong>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.</p><p><strong>Systematic review registration: </strong>PROSPERO CRD42022321391.</p>","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":"{\"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\":\"<p><strong>Background: </strong>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.</p><p><strong>Purpose: </strong>This systematic review evaluates externally validated LC risk prediction models to assess their performance and potential applicability in screening strategies.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Interpretation: </strong>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.</p><p><strong>Systematic review registration: </strong>PROSPERO CRD42022321391.</p>\",\"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}","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}
A systematic review and meta-analysis of lung cancer risk prediction models.
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.
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.
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