The D-Lab: Decision Support for Precision Medicine, GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center+, Universiteitssingel 40, 6229 ER, Maastricht, The Netherlands.
Department of Radiation Oncology (MAASTRO), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center+, Doctor Tanslaan 12, 6229 ET, Maastricht, The Netherlands.
Lung Cancer. 2018 Oct;124:6-11. doi: 10.1016/j.lungcan.2018.07.023. Epub 2018 Jul 20.
Recently it has been shown that radiomic features of computed tomography (CT) have prognostic information in stage I-III non-small cell lung cancer (NSCLC) patients. We aim to validate this prognostic radiomic signature in stage IV adenocarcinoma patients undergoing chemotherapy.
Two datasets of chemo-naive stage IV adenocarcinoma patients were investigated, dataset 1: 285 patients with CTs performed in a single center; dataset 2: 223 patients included in a multicenter clinical trial. The main exclusion criteria were EGFR mutation or unknown mutation status and non-delineated primary tumor. Radiomic features were calculated for the primary tumor. The c-index of cox regression was calculated and compared to the signature performance for overall survival (OS).
In total CT scans from 195 patients were eligible for analysis. Patients having a prognostic index (PI) lower than the signature median (n = 92) had a significantly better OS than patients with a PI higher than the median (n = 103, HR 1.445, 95% CI 1.07-1.95, p = 0.02, c-index 0.576, 95% CI 0.527-0.624).
The radiomic signature, derived from daily practice CT scans, has prognostic value for stage IV NSCLC, however the signature performs less than previously described for stage I-III NSCLC stages. In the future, machine learning techniques can potentially lead to a better prognostic imaging based model for stage IV NSCLC.
最近有研究表明，计算机断层扫描（CT）的放射组学特征与 I-III 期非小细胞肺癌（NSCLC）患者的预后有关。本研究旨在验证该放射组学特征在接受化疗的 IV 期腺癌患者中的预后价值。
本研究共纳入两个未经化疗的 IV 期腺癌患者数据集，数据集 1：285 例患者在单一中心进行 CT 检查；数据集 2：223 例患者纳入一项多中心临床试验。主要排除标准为 EGFR 突变或未知突变状态以及原发性肿瘤未明确。对原发性肿瘤进行放射组学特征分析。计算 COX 回归的 C 指数，并与总生存期（OS）的特征表现进行比较。
共对 195 例患者的 CT 扫描进行了分析。预后指数（PI）低于特征中位数的患者（n=92）比 PI 高于中位数的患者（n=103）的 OS 显著更好（HR 1.445，95%CI 1.07-1.95，p=0.02，C 指数 0.576，95%CI 0.527-0.624）。
该源自日常实践 CT 扫描的放射组学特征对 IV 期 NSCLC 具有预后价值，但对 I-III 期 NSCLC 阶段的预测性能不如之前描述的那么好。未来，机器学习技术可能会为 IV 期 NSCLC 带来更好的基于影像的预后模型。
