近期人工智能的发展表明放射组学可能代表一种有前途的非侵入性生物标志物，用于预测免疫检查点抑制剂（ICIs）的反应。然而，由于图像采集和重建的差异，独立组中放射组学算法的验证仍然是一个挑战。通过放射组学，我们研究了扫描归一化在更广泛的机器学习框架中的重要性，以实现模型在不同中心中对非小细胞肺癌（NSCLC）患者的ICI反应的外部推广预测。使用已建立的开源PyRadiomics和专有的DeepRadiomics深度学习技术，从642名进展期NSCLC患者的预ICI扫描中提取并比较了放射组学特征。人群被分为两组：一个发现组，包括来自三个学术中心的512名NSCLC患者，以及一个验证组，包括来自第四个中心的130名NSCLC患者。我们对图像进行了和谐化，以解决重建核心、层厚和设备制造商的差异。多变量模型通过交叉验证进行评估，用于估计临床变量、PD-L1表达、PyRadiomics或DeepRadiomics对于6个月进展无病生存（PFS-6）的预测价值。除放射组学特征外，对于PFS-6而言，最佳预后因素是临床 + PD-L1表达的组合（发现组的AUC=0.66，验证组的AUC=0.62）。若无图像和谐化，临床 + PyRadiomics或DeepRadiomics的组合，在发现组中的AUC分别为0.69和0.69，但在验证组中降至0.57和0.52。这种缺乏推广性与CT扫描参数聚类的主成分分析结果一致，随后进行的图像和谐化消除了这些聚类。临床 + DeepRadiomics的组合在发现组和验证组的AUC分别为0.67和0.63。相反，临床 + PyRadiomics的组合未通过推广性验证，AUC分别为0.66和0.59。我们证明了通过CT扫描和机器学习推广方法的协调，临床 + DeepRadiomics的风险预测模型具有推广性。这些结果表现出与常规肿瘤学实践使用的临床 + PD-L1类似的性能。本研究支持放射组学作为未来预测进展期NSCLC的ICI反应的非侵入性策略的巨大潜力。 版权所有 © 2023 Tonneau, Phan, Manem, Low-Kam, Dutil, Kazandjian, Vanderweyen, Panasci, Malo, Coulombe, Gagné, Elkrief, Belkaïd, Di Jorio, Orain, Bouchard, Muanza, Rybicki, Kafi, Huntsman, Joubert, Chandelier and Routy.

Recent developments in artificial intelligence suggest that radiomics may represent a promising non-invasive biomarker to predict response to immune checkpoint inhibitors (ICIs). Nevertheless, validation of radiomics algorithms in independent cohorts remains a challenge due to variations in image acquisition and reconstruction. Using radiomics, we investigated the importance of scan normalization as part of a broader machine learning framework to enable model external generalizability to predict ICI response in non-small cell lung cancer (NSCLC) patients across different centers.Radiomics features were extracted and compared from 642 advanced NSCLC patients on pre-ICI scans using established open-source PyRadiomics and a proprietary DeepRadiomics deep learning technology. The population was separated into two groups: a discovery cohort of 512 NSCLC patients from three academic centers and a validation cohort that included 130 NSCLC patients from a fourth center. We harmonized images to account for variations in reconstruction kernel, slice thicknesses, and device manufacturers. Multivariable models, evaluated using cross-validation, were used to estimate the predictive value of clinical variables, PD-L1 expression, and PyRadiomics or DeepRadiomics for progression-free survival at 6 months (PFS-6).The best prognostic factor for PFS-6, excluding radiomics features, was obtained with the combination of Clinical + PD-L1 expression (AUC = 0.66 in the discovery and 0.62 in the validation cohort). Without image harmonization, combining Clinical + PyRadiomics or DeepRadiomics delivered an AUC = 0.69 and 0.69, respectively, in the discovery cohort, but dropped to 0.57 and 0.52, in the validation cohort. This lack of generalizability was consistent with observations in principal component analysis clustered by CT scan parameters. Subsequently, image harmonization eliminated these clusters. The combination of Clinical + DeepRadiomics reached an AUC = 0.67 and 0.63 in the discovery and validation cohort, respectively. Conversely, the combination of Clinical + PyRadiomics failed generalizability validations, with AUC = 0.66 and 0.59.We demonstrated that a risk prediction model combining Clinical + DeepRadiomics was generalizable following CT scan harmonization and machine learning generalization methods. These results had similar performances to routine oncology practice using Clinical + PD-L1. This study supports the strong potential of radiomics as a future non-invasive strategy to predict ICI response in advanced NSCLC.Copyright © 2023 Tonneau, Phan, Manem, Low-Kam, Dutil, Kazandjian, Vanderweyen, Panasci, Malo, Coulombe, Gagné, Elkrief, Belkaïd, Di Jorio, Orain, Bouchard, Muanza, Rybicki, Kafi, Huntsman, Joubert, Chandelier and Routy.