Towards a survival risk prediction model for metastatic NSCLC patients on durvalumab using whole-lung CT radiomics

无线电技术 医学 杜瓦卢马布 一致性 比例危险模型 肿瘤科 内科学 总体生存率 列线图 队列 百分位 生存分析 放射科 免疫疗法 癌症 无容量 统计 数学
作者
Kedar A. Patwardhan,Harish RaviPrakash,Νικόλαος Νικολάου,Ignacio González‐García,José Domingo Salazar,Paul Metcalfe,Joachim Reischl
出处
期刊:Frontiers in Immunology [Frontiers Media SA]
卷期号:15
标识
DOI:10.3389/fimmu.2024.1383644
摘要

Background Existing criteria for predicting patient survival from immunotherapy are primarily centered on the PD-L1 status of patients. We tested the hypothesis that noninvasively captured baseline whole-lung radiomics features from CT images, baseline clinical parameters, combined with advanced machine learning approaches, can help to build models of patient survival that compare favorably with PD-L1 status for predicting ‘less-than-median-survival risk’ in the metastatic NSCLC setting for patients on durvalumab. With a total of 1062 patients, inclusive of model training and validation, this is the largest such study yet. Methods To ensure a sufficient sample size, we combined data from treatment arms of three metastatic NSCLC studies. About 80% of this data was used for model training, and the remainder was held-out for validation. We first trained two independent models; Model-C trained to predict survival using clinical data; and Model-R trained to predict survival using whole-lung radiomics features. Finally, we created Model-C+R which leveraged both clinical and radiomics features. Results The classification accuracy (for median survival) of Model-C, Model-R, and Model-C+R was 63%, 55%, and 68% respectively. Sensitivity analysis of survival prediction across different training and validation cohorts showed concordance indices ([95 percentile]) of 0.64 ([0.63, 0.65]), 0.60 ([0.59, 0.60]), and 0.66 ([0.65,0.67]), respectively. We additionally evaluated generalization of these models on a comparable cohort of 144 patients from an independent study, demonstrating classification accuracies of 65%, 62%, and 72% respectively. Conclusion Machine Learning models combining baseline whole-lung CT radiomic and clinical features may be a useful tool for patient selection in immunotherapy. Further validation through prospective studies is needed.
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