利用先进影像组学和深度学习进行肺癌免疫治疗反应的个性化预测

Personalized prediction of immunotherapy response in lung cancer patients using advanced radiomics and deep learning

CANCER IMAGING

影响因子:3.5

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

DOI: 10.1186/s40644-024-00779-4

摘要

肺癌（LC）是癌症相关死亡的主要原因之一，免疫治疗（IO）在晚期肺癌的治疗中显示出良好的前景。然而，识别可能受益于IO的患者以及监测治疗反应仍然具有挑战性。本研究旨在建立一个基于临床特征和先进影像生物标志物的肺癌患者免疫治疗无进展生存期（PFS）预测模型。回顾性分析了206例接受免疫治疗的肺癌患者的资料，使用术前CT影像提取肿瘤内及肿瘤周围血管的放射组学特征，并收集年龄、基因状态、血液学参数和分期等临床信息。通过单变量Cox回归和卡方检验的两步特征筛选，确定关键的影像组学和临床特征，并采用顺序前向选择建立DeepSurv模型，以预测PFS。模型性能通过时间依赖的ROC曲线下面积（AUC）和一致性指数（C-index）进行评估。结果显示，结合肿瘤内部异质性和肿瘤周围血管的影像组学与临床特征，显著提升了免疫治疗反应的预测能力（p<0.001）。所提出的DeepSurv模型表现出AUC范围为0.76至0.80、C指数为0.83的优异性能。此外，个性化的PFS预测曲线在预后良好的患者与预后不佳的患者之间存在显著差异（p<0.05）。该模型将肿瘤内外血管的影像组学与临床特征结合，成功构建了肺癌免疫治疗PFS的预测模型，为实现个性化医疗和改善患者预后提供了潜在工具。

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.