基于人工智能的放射学特征预测广泛期小细胞肺癌的预后和化疗免疫治疗比化疗的额外益处：一项多机构研究

IF 9.1 1区医学 Q1 ONCOLOGY

Cancer letters Pub Date : 2025-06-11 DOI:10.1016/j.canlet.2025.217872

Mohammadhadi Khorrami , Pushkar Mutha , Cristian Barrera , Vidya S. Viswanathan , Fatemeh Ardeshir-Larijani , Prantesh Jain , Kristin Higgins , Anant Madabhushi

{"title":"基于人工智能的放射学特征预测广泛期小细胞肺癌的预后和化疗免疫治疗比化疗的额外益处：一项多机构研究","authors":"Mohammadhadi Khorrami , Pushkar Mutha , Cristian Barrera , Vidya S. Viswanathan , Fatemeh Ardeshir-Larijani , Prantesh Jain , Kristin Higgins , Anant Madabhushi","doi":"10.1016/j.canlet.2025.217872","DOIUrl":null,"url":null,"abstract":"<div><div>Small cell lung cancer (SCLC) is aggressive with poor survival outcomes, and most patients develop resistance to chemotherapy. No predictive biomarkers currently guide therapy. This study evaluates radiomic features to predict PFS and OS in limited-stage SCLC (LS-SCLC) and assesses PFS, OS, and the added benefit of chemoimmunotherapy (CHIO) in extensive-stage SCLC (ES-SCLC). A total of 660 SCLC patients (470 ES-SCLC, 190 LS-SCLC) from three sites were analyzed. LS-SCLC patients received chemotherapy and radiation, while ES-SCLC patients received either chemotherapy alone or CHIO. Radiomic and quantitative vasculature tortuosity features were extracted from CT scans. A LASSO-Cox regression model was used to construct the ES- Risk-Score (ESRS) and LS- Risk-Score (LSRS). ESRS was associated with PFS in training (HR = 1.54, adj. P = .0013) and two independent validation sets (HR = 1.32, adj. P = .0001; HR = 2.4, adj. P = .0073) and with OS in training (HR = 1.37, adj. P = .0054) and validation sets (HR = 1.35, adj. P < .0006; HR = 1.6, adj. P < .0085) in ES-SCLC patients treated with chemotherapy. High-risk patients had improved PFS (HR = 0.68, adj. P < .001) and OS (HR = 0.78, adj. P = .026) with CHIO. LSRS was associated with PFS in training and two independent validation sets (HR = 1.9, adj. P = .007; HR = 1.4, adj. P = .0098; HR = 2.1, adj. P = .028) in LS-SCLC patients receiving chemoradiation. Radiomics is prognostic for PFS and OS and predicts chemoimmunotherapy benefit in high-risk ES-SCLC patients.</div></div>","PeriodicalId":9506,"journal":{"name":"Cancer letters","volume":"628 ","pages":"Article 217872"},"PeriodicalIF":9.1000,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"AI-based radiomic features predict outcomes and the added benefit of chemoimmunotherapy over chemotherapy in extensive stage small cell lung cancer: A multi-institutional study\",\"authors\":\"Mohammadhadi Khorrami , Pushkar Mutha , Cristian Barrera , Vidya S. Viswanathan , Fatemeh Ardeshir-Larijani , Prantesh Jain , Kristin Higgins , Anant Madabhushi\",\"doi\":\"10.1016/j.canlet.2025.217872\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Small cell lung cancer (SCLC) is aggressive with poor survival outcomes, and most patients develop resistance to chemotherapy. No predictive biomarkers currently guide therapy. This study evaluates radiomic features to predict PFS and OS in limited-stage SCLC (LS-SCLC) and assesses PFS, OS, and the added benefit of chemoimmunotherapy (CHIO) in extensive-stage SCLC (ES-SCLC). A total of 660 SCLC patients (470 ES-SCLC, 190 LS-SCLC) from three sites were analyzed. LS-SCLC patients received chemotherapy and radiation, while ES-SCLC patients received either chemotherapy alone or CHIO. Radiomic and quantitative vasculature tortuosity features were extracted from CT scans. A LASSO-Cox regression model was used to construct the ES- Risk-Score (ESRS) and LS- Risk-Score (LSRS). ESRS was associated with PFS in training (HR = 1.54, adj. P = .0013) and two independent validation sets (HR = 1.32, adj. P = .0001; HR = 2.4, adj. P = .0073) and with OS in training (HR = 1.37, adj. P = .0054) and validation sets (HR = 1.35, adj. P < .0006; HR = 1.6, adj. P < .0085) in ES-SCLC patients treated with chemotherapy. High-risk patients had improved PFS (HR = 0.68, adj. P < .001) and OS (HR = 0.78, adj. P = .026) with CHIO. LSRS was associated with PFS in training and two independent validation sets (HR = 1.9, adj. P = .007; HR = 1.4, adj. P = .0098; HR = 2.1, adj. P = .028) in LS-SCLC patients receiving chemoradiation. Radiomics is prognostic for PFS and OS and predicts chemoimmunotherapy benefit in high-risk ES-SCLC patients.</div></div>\",\"PeriodicalId\":9506,\"journal\":{\"name\":\"Cancer letters\",\"volume\":\"628 \",\"pages\":\"Article 217872\"},\"PeriodicalIF\":9.1000,\"publicationDate\":\"2025-06-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Cancer letters\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0304383525004392\",\"RegionNum\":1,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ONCOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cancer letters","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0304383525004392","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ONCOLOGY","Score":null,"Total":0}

引用次数: 0

摘要

小细胞肺癌（SCLC）具有侵袭性，生存预后差，大多数患者对化疗产生耐药性。目前尚无预测性生物标志物指导治疗。本研究评估放射学特征以预测有限期SCLC （LS-SCLC）的PFS和OS，并评估广泛期SCLC （ES-SCLC）的PFS、OS和化疗免疫治疗（CHIO）的附加益处。共分析了660例SCLC患者（470例ES-SCLC， 190例LS-SCLC）。LS-SCLC患者接受化疗和放疗，ES-SCLC患者接受单独化疗或CHIO。从CT扫描中提取放射学和定量血管扭曲特征。采用LASSO-Cox回归模型构建ES- Risk-Score （ESRS）和LS- Risk-Score （LSRS）。ESRS与训练中的PFS相关（HR = 1.54, adj. P = 0.0013），两个独立验证集(HR = 1.32, adj. P = 0.0001；HR = 2.4, adj. P = 0.0073)，训练中OS （HR = 1.37, adj. P = 0.0054）和验证集(HR = 1.35, adj. P <；考虑;HR = 1.6, P <；.0085)在接受化疗的ES-SCLC患者中。高危患者PFS改善(HR = 0.68, P <；.001)和OS （HR = 0.78, adj. P = 0.026）。在训练和两个独立验证集中，LSRS与PFS相关(HR = 1.9, adj. P = .007；HR = 1.4, adj. P = 0.0098；接受放化疗的LS-SCLC患者HR = 2.1, adj. P = 0.028)。放射组学可以预测PFS和OS，并预测高危ES-SCLC患者的化疗免疫治疗效果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

AI-based radiomic features predict outcomes and the added benefit of chemoimmunotherapy over chemotherapy in extensive stage small cell lung cancer: A multi-institutional study

Small cell lung cancer (SCLC) is aggressive with poor survival outcomes, and most patients develop resistance to chemotherapy. No predictive biomarkers currently guide therapy. This study evaluates radiomic features to predict PFS and OS in limited-stage SCLC (LS-SCLC) and assesses PFS, OS, and the added benefit of chemoimmunotherapy (CHIO) in extensive-stage SCLC (ES-SCLC). A total of 660 SCLC patients (470 ES-SCLC, 190 LS-SCLC) from three sites were analyzed. LS-SCLC patients received chemotherapy and radiation, while ES-SCLC patients received either chemotherapy alone or CHIO. Radiomic and quantitative vasculature tortuosity features were extracted from CT scans. A LASSO-Cox regression model was used to construct the ES- Risk-Score (ESRS) and LS- Risk-Score (LSRS). ESRS was associated with PFS in training (HR = 1.54, adj. P = .0013) and two independent validation sets (HR = 1.32, adj. P = .0001; HR = 2.4, adj. P = .0073) and with OS in training (HR = 1.37, adj. P = .0054) and validation sets (HR = 1.35, adj. P < .0006; HR = 1.6, adj. P < .0085) in ES-SCLC patients treated with chemotherapy. High-risk patients had improved PFS (HR = 0.68, adj. P < .001) and OS (HR = 0.78, adj. P = .026) with CHIO. LSRS was associated with PFS in training and two independent validation sets (HR = 1.9, adj. P = .007; HR = 1.4, adj. P = .0098; HR = 2.1, adj. P = .028) in LS-SCLC patients receiving chemoradiation. Radiomics is prognostic for PFS and OS and predicts chemoimmunotherapy benefit in high-risk ES-SCLC patients.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Cancer letters 医学-肿瘤学

CiteScore

17.70

自引率

2.10%

发文量

427

审稿时长

15 days

期刊介绍： Cancer Letters is a reputable international journal that serves as a platform for significant and original contributions in cancer research. The journal welcomes both full-length articles and Mini Reviews in the wide-ranging field of basic and translational oncology. Furthermore, it frequently presents Special Issues that shed light on current and topical areas in cancer research. Cancer Letters is highly interested in various fundamental aspects that can cater to a diverse readership. These areas include the molecular genetics and cell biology of cancer, radiation biology, molecular pathology, hormones and cancer, viral oncology, metastasis, and chemoprevention. The journal actively focuses on experimental therapeutics, particularly the advancement of targeted therapies for personalized cancer medicine, such as metronomic chemotherapy. By publishing groundbreaking research and promoting advancements in cancer treatments, Cancer Letters aims to actively contribute to the fight against cancer and the improvement of patient outcomes.