Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, No. 155, Sec. 2, Linong Street, Beitou District, Taipei, 112, Taiwan.
Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA.
Cancer Imaging. 2024 Sep 30;24(1):129. doi: 10.1186/s40644-024-00779-4.
Lung cancer (LC) is a leading cause of cancer-related mortality, and immunotherapy (IO) has shown promise in treating advanced-stage LC. However, identifying patients likely to benefit from IO and monitoring treatment response remains challenging. This study aims to develop a predictive model for progression-free survival (PFS) in LC patients with IO based on clinical features and advanced imaging biomarkers.
A retrospective analysis was conducted on a cohort of 206 LC patients receiving IO treatment. Pre-treatment computed tomography images were used to extract advanced imaging biomarkers, including intratumoral and peritumoral-vasculature radiomics. Clinical features, including age, gene status, hematology, and staging, were also collected. Key radiomic and clinical features for predicting IO outcomes were identified using a two-step feature selection process, including univariate Cox regression and chi-squared test, followed by sequential forward selection. The DeepSurv model was constructed to predict PFS based on clinical and radiomic features. Model performance was evaluated using the area under the time-dependent receiver operating characteristic curve (AUC) and concordance index (C-index).
Combining radiomics of intratumoral heterogeneity and peritumoral-vasculature with clinical features demonstrated a significant enhancement (p < 0.001) in predicting IO response. The proposed DeepSurv model exhibited a prediction performance with AUCs ranging from 0.76 to 0.80 and a C-index of 0.83. Furthermore, the predicted personalized PFS curves revealed a significant difference (p < 0.05) between patients with favorable and unfavorable prognoses.
Integrating intratumoral and peritumoral-vasculature radiomics with clinical features enabled the development of a predictive model for PFS in LC patients with IO. The proposed model's capability to estimate individualized PFS probability and differentiate the prognosis status held promise to facilitate personalized medicine and improve patient outcomes in LC.
肺癌（LC）是癌症相关死亡的主要原因，免疫疗法（IO）已显示出治疗晚期 LC 的潜力。然而，确定可能从 IO 中受益的患者并监测治疗反应仍然具有挑战性。本研究旨在基于临床特征和先进的影像学生物标志物，为接受 IO 治疗的 LC 患者建立无进展生存期（PFS）的预测模型。
对接受 IO 治疗的 206 例 LC 患者的队列进行了回顾性分析。使用预处理 CT 图像提取了包括肿瘤内和肿瘤周围血管的放射组学等先进的影像学生物标志物。还收集了包括年龄、基因状态、血液学和分期在内的临床特征。使用两步特征选择过程，包括单变量 Cox 回归和卡方检验，以及顺序向前选择，确定了预测 IO 结果的关键放射组学和临床特征。使用基于临床和放射组学特征的 DeepSurv 模型来预测 PFS。使用时间依赖性接受者操作特征曲线（AUC）和一致性指数（C-index）评估模型性能。
将肿瘤内异质性和肿瘤周围血管的放射组学与临床特征相结合，可显著提高预测 IO 反应的能力（p<0.001）。所提出的 DeepSurv 模型的预测性能 AUC 范围为 0.76 至 0.80，C-index 为 0.83。此外，预测的个性化 PFS 曲线显示了预后良好和预后不良患者之间的显著差异（p<0.05）。
将肿瘤内和肿瘤周围血管的放射组学与临床特征相结合，为接受 IO 治疗的 LC 患者的 PFS 建立了预测模型。该模型能够估计个体 PFS 概率并区分预后状态，有望促进个性化医疗并改善 LC 患者的预后。
