Yao-Ren Zhang, Yueh-Hsun Lu, Che-Ming Lin, Jan-Wen Ku
{"title":"预处理CT结构分析预测晚期非小细胞肺癌患者接受免疫治疗的生存结果：一项系统回顾和荟萃分析。","authors":"Yao-Ren Zhang, Yueh-Hsun Lu, Che-Ming Lin, Jan-Wen Ku","doi":"10.1111/1759-7714.70144","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>While established biomarkers predict immunotherapy response in advanced nonsmall cell lung cancer (NSCLC), additional noninvasive imaging biomarkers may enhance treatment selection. Pretreatment computed tomography (CT) texture analysis may provide tumor characterization to predict survival outcomes.</p><p><strong>Methods: </strong>We conducted a systematic review and meta-analysis following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. PubMed and Cochrane Library databases were searched. Study quality was assessed using the quality in prognosis studies (QUIPS) tool. Hazard ratios (HRs) with 95% confidence intervals (CIs) were pooled using random-effects models.</p><p><strong>Results: </strong>Ten retrospective studies involving 2400 patients were included. Patients stratified as low-risk based on CT texture features demonstrated significantly improved survival outcomes compared to high-risk patients. The included studies used diverse radiomic features for risk stratification, including texture features from gray-level co-occurrence matrix (GLCM) such as entropy and dissimilarity, first-order statistical parameters including skewness and kurtosis, gray-level run-length matrix (GLRLM) features, and deep learning-derived features. Meta-analysis of five studies (n = 1102) revealed that patients stratified as low-risk based on these quantitative CT texture signatures had substantially better overall survival (OS) (p < 0.0001) with minimal heterogeneity (I<sup>2</sup> = 0.0%). Similarly, progression-free survival (PFS) analysis of five studies (n = 1799) showed significant benefit for low-risk patients (p < 0.0001), though with moderate heterogeneity (I<sup>2</sup> = 71.7%).</p><p><strong>Conclusions: </strong>Pretreatment quantitative CT texture analysis effectively predicts survival outcomes in advanced NSCLC patients receiving immunotherapy, providing clinically meaningful risk stratification. This noninvasive imaging approach may serve as an additional tool to complement established pathological and molecular biomarkers, including liquid biopsy, for enhanced personalized treatment selection.</p>","PeriodicalId":23338,"journal":{"name":"Thoracic Cancer","volume":"16 15","pages":"e70144"},"PeriodicalIF":2.3000,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12320133/pdf/","citationCount":"0","resultStr":"{\"title\":\"Pretreatment CT Texture Analysis for Predicting Survival Outcomes in Advanced Nonsmall Cell Lung Cancer Patients Receiving Immunotherapy: A Systematic Review and Meta-Analysis.\",\"authors\":\"Yao-Ren Zhang, Yueh-Hsun Lu, Che-Ming Lin, Jan-Wen Ku\",\"doi\":\"10.1111/1759-7714.70144\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>While established biomarkers predict immunotherapy response in advanced nonsmall cell lung cancer (NSCLC), additional noninvasive imaging biomarkers may enhance treatment selection. Pretreatment computed tomography (CT) texture analysis may provide tumor characterization to predict survival outcomes.</p><p><strong>Methods: </strong>We conducted a systematic review and meta-analysis following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. PubMed and Cochrane Library databases were searched. Study quality was assessed using the quality in prognosis studies (QUIPS) tool. Hazard ratios (HRs) with 95% confidence intervals (CIs) were pooled using random-effects models.</p><p><strong>Results: </strong>Ten retrospective studies involving 2400 patients were included. Patients stratified as low-risk based on CT texture features demonstrated significantly improved survival outcomes compared to high-risk patients. The included studies used diverse radiomic features for risk stratification, including texture features from gray-level co-occurrence matrix (GLCM) such as entropy and dissimilarity, first-order statistical parameters including skewness and kurtosis, gray-level run-length matrix (GLRLM) features, and deep learning-derived features. Meta-analysis of five studies (n = 1102) revealed that patients stratified as low-risk based on these quantitative CT texture signatures had substantially better overall survival (OS) (p < 0.0001) with minimal heterogeneity (I<sup>2</sup> = 0.0%). Similarly, progression-free survival (PFS) analysis of five studies (n = 1799) showed significant benefit for low-risk patients (p < 0.0001), though with moderate heterogeneity (I<sup>2</sup> = 71.7%).</p><p><strong>Conclusions: </strong>Pretreatment quantitative CT texture analysis effectively predicts survival outcomes in advanced NSCLC patients receiving immunotherapy, providing clinically meaningful risk stratification. This noninvasive imaging approach may serve as an additional tool to complement established pathological and molecular biomarkers, including liquid biopsy, for enhanced personalized treatment selection.</p>\",\"PeriodicalId\":23338,\"journal\":{\"name\":\"Thoracic Cancer\",\"volume\":\"16 15\",\"pages\":\"e70144\"},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2025-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12320133/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Thoracic Cancer\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1111/1759-7714.70144\",\"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":"Thoracic Cancer","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1111/1759-7714.70144","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ONCOLOGY","Score":null,"Total":0}
Pretreatment CT Texture Analysis for Predicting Survival Outcomes in Advanced Nonsmall Cell Lung Cancer Patients Receiving Immunotherapy: A Systematic Review and Meta-Analysis.
Background: While established biomarkers predict immunotherapy response in advanced nonsmall cell lung cancer (NSCLC), additional noninvasive imaging biomarkers may enhance treatment selection. Pretreatment computed tomography (CT) texture analysis may provide tumor characterization to predict survival outcomes.
Methods: We conducted a systematic review and meta-analysis following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. PubMed and Cochrane Library databases were searched. Study quality was assessed using the quality in prognosis studies (QUIPS) tool. Hazard ratios (HRs) with 95% confidence intervals (CIs) were pooled using random-effects models.
Results: Ten retrospective studies involving 2400 patients were included. Patients stratified as low-risk based on CT texture features demonstrated significantly improved survival outcomes compared to high-risk patients. The included studies used diverse radiomic features for risk stratification, including texture features from gray-level co-occurrence matrix (GLCM) such as entropy and dissimilarity, first-order statistical parameters including skewness and kurtosis, gray-level run-length matrix (GLRLM) features, and deep learning-derived features. Meta-analysis of five studies (n = 1102) revealed that patients stratified as low-risk based on these quantitative CT texture signatures had substantially better overall survival (OS) (p < 0.0001) with minimal heterogeneity (I2 = 0.0%). Similarly, progression-free survival (PFS) analysis of five studies (n = 1799) showed significant benefit for low-risk patients (p < 0.0001), though with moderate heterogeneity (I2 = 71.7%).
Conclusions: Pretreatment quantitative CT texture analysis effectively predicts survival outcomes in advanced NSCLC patients receiving immunotherapy, providing clinically meaningful risk stratification. This noninvasive imaging approach may serve as an additional tool to complement established pathological and molecular biomarkers, including liquid biopsy, for enhanced personalized treatment selection.
期刊介绍：
Thoracic Cancer aims to facilitate international collaboration and exchange of comprehensive and cutting-edge information on basic, translational, and applied clinical research in lung cancer, esophageal cancer, mediastinal cancer, breast cancer and other thoracic malignancies. Prevention, treatment and research relevant to Asia-Pacific is a focus area, but submissions from all regions are welcomed. The editors encourage contributions relevant to prevention, general thoracic surgery, medical oncology, radiology, radiation medicine, pathology, basic cancer research, as well as epidemiological and translational studies in thoracic cancer. Thoracic Cancer is the official publication of the Chinese Society of Lung Cancer, International Chinese Society of Thoracic Surgery and is endorsed by the Korean Association for the Study of Lung Cancer and the Hong Kong Cancer Therapy Society.
The Journal publishes a range of article types including: Editorials, Invited Reviews, Mini Reviews, Original Articles, Clinical Guidelines, Technological Notes, Imaging in thoracic cancer, Meeting Reports, Case Reports, Letters to the Editor, Commentaries, and Brief Reports.
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