■预测患者从免疫疗法生存的现有标准主要集中在患者的PD-L1状态。我们检验了从CT图像非侵入性捕获基线全肺影像组学特征的假设,基线临床参数,结合先进的机器学习方法,可以帮助建立与PD-L1状态相比有利的患者生存模型,以预测接受Durvalumab治疗的转移性NSCLC患者的“低于中位生存风险”。共有1062名患者,包括模型训练和验证,这是迄今为止规模最大的此类研究。
■为了确保足够的样本量,我们合并了三项转移性NSCLC研究的治疗组数据.大约80%的数据用于模型训练,其余的等待验证。我们首先训练了两个独立的模型;模型-C训练以使用临床数据预测生存;模型-R训练以使用全肺放射组学特征预测生存。最后,我们创建了利用临床和影像组学功能的Model-C+R.
■模型C的分类精度(中位生存期),Model-R,Model-C+R为63%,55%,分别为68%。不同训练和验证队列生存预测的敏感性分析显示一致性指数([95百分位数])为0.64([0.63,0.65]),0.60([0.59,0.60]),和0.66([0.65,0.67]),分别。我们还评估了来自独立研究的144名患者的可比较队列中这些模型的推广。显示65%的分类准确率,62%,分别为72%。
■结合基线全肺CT影像和临床特征的机器学习模型可能是免疫治疗患者选择的有用工具。需要通过前瞻性研究进一步验证。
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.