Niu Ye, Xie Han-Bing, Jia Hao-Bo, Zhao Lin, Liu Le, Liu Ping-Ping, Li Xue-Meng, Wang Rui-Tao, Li Yuan-Zhou
Department of Internal Medicine, Harbin Medical University Cancer Hospital, Harbin Medical University, Harbin, Heilongjiang, China.
The School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China.
Cancer Med. 2025 Aug;14(15):e71077. doi: 10.1002/cam4.71077.
Prognostic stratification in non-small cell lung cancer (NSCLC) presents considerable challenges due to tumor heterogeneity. Emerging evidence has proposed that adipose tissue may play a prognostic role in oncological outcomes. This study investigates the integration of deep learning (DL)-derived computed tomography (CT) imaging biomarkers with mediastinal adiposity metrics to develop a multimodal prognostic model for postoperative survival prediction in NSCLC patients.
A retrospective cohort of 702 surgically resected NSCLC patients was analyzed. Tumor radiomic features were extracted using a DenseNet121 convolutional neural network architecture, while mediastinal fat area (MFA) was quantified through semiautomated segmentation using ImageJ software. A multimodal survival prediction model was developed through feature-level fusion of DL-extracted tumor characteristics and MFA measurements. Model performance was evaluated using Harrell's concordance index (C-index) and receiver operating characteristic (ROC) analysis. Risk stratification was performed using an optimal threshold derived from training data, with subsequent Kaplan-Meier survival curve comparison between high- and low-risk cohorts.
The DL-based tumor model achieved C-indices of 0.787 (95% CI: 0.742-0.832) for disease-free survival (DFS) and 0.810 (95% CI: 0.768-0.852) for overall survival (OS) in internal validation. Integration of MFA with DL-derived tumor features yielded a multimodal model demonstrating enhanced predictive performance, with C-indices of 0.823 (OS) and 0.803 (DFS). Kaplan-Meier analysis revealed significant survival divergence between risk-stratified groups (log-rank p < 0.05).
The multimodal fusion of DL-extracted tumor radiomics and mediastinal adiposity metrics represents a significant advancement in postoperative survival prediction for NSCLC patients, demonstrating superior prognostic capability compared to unimodal approaches.
由于肿瘤异质性，非小细胞肺癌（NSCLC）的预后分层面临巨大挑战。新出现的证据表明，脂肪组织可能在肿瘤学结局中发挥预后作用。本研究探讨将深度学习（DL）衍生的计算机断层扫描（CT）成像生物标志物与纵隔脂肪度量指标相结合，以开发一种用于预测NSCLC患者术后生存的多模态预后模型。
对702例接受手术切除的NSCLC患者的回顾性队列进行分析。使用DenseNet121卷积神经网络架构提取肿瘤放射组学特征，同时使用ImageJ软件通过半自动分割对纵隔脂肪面积（MFA）进行量化。通过对DL提取的肿瘤特征和MFA测量值进行特征级融合，开发了一种多模态生存预测模型。使用Harrell一致性指数（C指数）和受试者操作特征（ROC）分析评估模型性能。使用从训练数据得出的最佳阈值进行风险分层，随后比较高风险和低风险队列之间的Kaplan-Meier生存曲线。
基于DL的肿瘤模型在内部验证中，无病生存期（DFS）的C指数为0.787（95%CI：0.742-0.832），总生存期（OS）的C指数为0.810（95%CI：0.768-0.852）。MFA与DL衍生的肿瘤特征相结合产生了一个多模态模型，其预测性能增强，OS的C指数为0.823，DFS的C指数为0.803。Kaplan-Meier分析显示，风险分层组之间存在显著的生存差异（对数秩p<0.05）。
DL提取的肿瘤放射组学与纵隔脂肪度量指标的多模态融合代表了NSCLC患者术后生存预测的重大进展，与单模态方法相比，具有卓越的预后能力。
