使用高级放射线学和深度学习的肺癌患者中免疫疗法反应的个性化预测
Personalized prediction of immunotherapy response in lung cancer patients using advanced radiomics and deep learning
影响因子:3.50000
分区:医学2区 / 肿瘤学2区 核医学2区
发表日期:2024 Sep 30
作者:
Chien-Yi Liao, Yuh-Min Chen, Yu-Te Wu, Heng-Sheng Chao, Hwa-Yen Chiu, Ting-Wei Wang, Jyun-Ru Chen, Tsu-Hui Shiao, Chia-Feng Lu
摘要
肺癌（LC）是癌症相关死亡率的主要原因，免疫疗法（IO）在治疗晚期LC方面表现出了希望。但是，确定可能受益于IO的患者并监测治疗反应仍然具有挑战性。这项研究旨在基于临床特征和高级成像生物标志物在LC患者中开发一个无进展生存率（PFS）的预测模型。对206名接受IO治疗的LC患者进行了回顾性分析。预处理计算机断层扫描图像用于提取先进的成像生物标志物，包括肿瘤内和周围脉血脉络膜放射线学。还收集了临床特征，包括年龄，基因状态，血液学和分期。使用两步特征选择过程（包括单变量COX回归和卡方检验），鉴定出用于预测IO结果的关键放射素和临床特征，然后进行顺序的正向选择。构建了DeepSURV模型，以根据临床和放射线特征来预测PFS。使用时间依赖性接收器操作特征曲线（AUC）和一致性指数（C-INDEX）的区域评估模型性能。将肿瘤内异质性的放射素学和临床特征与临床特征相结合（p <0.001）。拟议的DeepSurv模型表现出预测性能，AUCS范围为0.76至0.80，C索引为0.83。此外，预测的个性化PFS曲线表明，有良好和不利预后的患者之间存在显着差异（P <0.05）。将肿瘤内和周围脉血管脉管系统放射素学的整合具有临床特征，从而使LC患者的PFS开发了PFS的预测模型。提出的模型估计个性化PFS概率并区分预后状况的能力有望促进个性化医学并改善LC中的患者预后。
Abstract
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.