Department of Radiology, Columbia University Medical Center/New York Presbyterian Hospital, New York, New York.
Gustave Roussy, Université Paris-Saclay, Villejuif, France.
Clin Cancer Res. 2020 May 1;26(9):2151-2162. doi: 10.1158/1078-0432.CCR-19-2942. Epub 2020 Mar 20.
Using standard-of-care CT images obtained from patients with a diagnosis of non-small cell lung cancer (NSCLC), we defined radiomics signatures predicting the sensitivity of tumors to nivolumab, docetaxel, and gefitinib.
Data were collected prospectively and analyzed retrospectively across multicenter clinical trials [nivolumab, = 92, CheckMate017 (NCT01642004), CheckMate063 (NCT01721759); docetaxel, = 50, CheckMate017; gefitinib, = 46, (NCT00588445)]. Patients were randomized to training or validation cohorts using either a 4:1 ratio (nivolumab: 72T:20V) or a 2:1 ratio (docetaxel: 32T:18V; gefitinib: 31T:15V) to ensure an adequate sample size in the validation set. Radiomics signatures were derived from quantitative analysis of early tumor changes from baseline to first on-treatment assessment. For each patient, 1,160 radiomics features were extracted from the largest measurable lung lesion. Tumors were classified as treatment sensitive or insensitive; reference standard was median progression-free survival (NCT01642004, NCT01721759) or surgery (NCT00588445). Machine learning was implemented to select up to four features to develop a radiomics signature in the training datasets and applied to each patient in the validation datasets to classify treatment sensitivity.
The radiomics signatures predicted treatment sensitivity in the validation dataset of each study group with AUC (95 confidence interval): nivolumab, 0.77 (0.55-1.00); docetaxel, 0.67 (0.37-0.96); and gefitinib, 0.82 (0.53-0.97). Using serial radiographic measurements, the magnitude of exponential increase in signature features deciphering tumor volume, invasion of tumor boundaries, or tumor spatial heterogeneity was associated with shorter overall survival.
Radiomics signatures predicted tumor sensitivity to treatment in patients with NSCLC, offering an approach that could enhance clinical decision-making to continue systemic therapies and forecast overall survival.
使用从诊断为非小细胞肺癌（NSCLC）的患者中获得的标准治疗 CT 图像，我们定义了预测肿瘤对 nivolumab、docetaxel 和 gefitinib 敏感性的放射组学特征。
数据是在多中心临床试验中前瞻性收集和回顾性分析的[nivolumab，=92，CheckMate017（NCT01642004），CheckMate063（NCT01721759）；docetaxel，=50，CheckMate017；gefitinib，=46，（NCT00588445）]。使用 4:1 或 2:1 的比例（nivolumab：72T：20V；docetaxel：32T：18V；gefitinib：31T：15V）将患者随机分配到训练或验证队列，以确保验证集中有足够的样本量。放射组学特征是从基线到首次治疗评估的早期肿瘤变化的定量分析中得出的。对于每个患者，从最大可测量的肺病变中提取 1160 个放射组学特征。将肿瘤分类为治疗敏感或不敏感；参考标准是中位无进展生存期（NCT01642004，NCT01721759）或手术（NCT00588445）。实施机器学习从训练数据集中选择最多四个特征来开发放射组学特征，并将其应用于验证数据集中的每个患者以分类治疗敏感性。
放射组学特征在每个研究组的验证数据集预测治疗敏感性，AUC（95%置信区间）：nivolumab，0.77（0.55-1.00）；docetaxel，0.67（0.37-0.96）；gefitinib，0.82（0.53-0.97）。使用连续的影像学测量，特征的指数增长幅度可以破译肿瘤体积、肿瘤边界浸润或肿瘤空间异质性的程度与总生存期较短有关。
放射组学特征预测了 NSCLC 患者对治疗的肿瘤敏感性，提供了一种可以增强临床决策的方法，以继续进行全身治疗并预测总生存期。
