Longitudinal NSCLC Treatment Progression via Multimodal Generative Models
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 radiothera...
| Authors | Massimiliano Mantegna, Elena Mulero Ayllón, Alice Natalina Caragliano et al. |
|---|---|
| Primary category | cs.CV |
| Published | 6 March 2026 |
| Last updated at source | 6 March 2026 |
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