用多模态生成模型预测肺癌放疗中肿瘤演变，提升治疗监测精度。
Longitudinal NSCLC Treatment Progression via Multimodal Generative Models
- 基于剂量和临床变量，生成放疗后的模拟CT图像。
- 扩散模型比GAN更稳定，生成的肿瘤变化轨迹更符合解剖规律。
- 适用于放疗方案优化与个体化治疗研究，尤其适合临床医生和放射科专家。
放疗期间肿瘤演化预测是临床关键挑战，尤其当变化受解剖结构和治疗共同驱动时。本文提出虚拟治疗（VT）框架，将非小细胞肺癌（NSCLC）进展建模为剂量感知的多模态条件图像到图像转换问题。给定基线CT、临床变量及辐射剂量增量，VT旨在合成反映治疗诱导解剖变化的随访CT图像。在包含222例Ⅲ期NSCLC患者、共895次放疗期间采集的不规则时间点CT扫描的数据集上评估该框架。生成过程结合已交付剂量增量以及人口学和肿瘤相关临床变量。对比了基于GAN和扩散模型的2D与2.5D配置。定量与定性结果显示，扩散模型在多模态、剂量感知条件下表现更一致，生成结果更稳定且解剖合理性更高，支持VT作为非侵入式治疗监测与自适应放疗研究的潜力工具。
原文摘要 · Abstract (English)
Predicting tumor evolution during radiotherapy is a clinically critical challenge, particularly when longitudinal changes are driven by both anatomy and treatment. In this work, we introduce a Virtual Treatment (VT) framework that formulates non-small cell lung cancer (NSCLC) progression as a dose-aware multimodal conditional image-to-image translation problem. Given a CT scan, baseline clinical variables, and a specified radiation dose increment, VT aims to synthesize plausible follow-up CT images reflecting treatment-induced anatomical changes. We evaluate the proposed framework on a longitudinal dataset of 222 stage III NSCLC patients, comprising 895 CT scans acquired during radiotherapy under irregular clinical schedules. The generative process is conditioned on delivered dose increments together with demographic and tumor-related clinical variables. Representative GAN-based and diffusion-based models are benchmarked across 2D and 2.5D configurations. Quantitative and qualitative results indicate that diffusion-based models benefit more consistently from multimodal, dose-aware conditioning and produce more stable and anatomically plausible tumor evolution trajectories than GAN-based baselines, supporting the potential of VT as a tool for in-silico treatment monitoring and adaptive radiotherapy research in NSCLC.
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