Massimiliano Mantegna, Elena Mulero Ayllón, Alice Natalina Caragliano, Francesco Di Feola, Claudia Tacconi, Michele Fiore, Edy Ippolito, Carlo Greco, Sara Ramella, Philippe C. Cattin, Paolo Soda, Matteo Tortora, Valerio Guarrasi
3 min
Abstract
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.
Alex: They also focus training on the tumor area?
Sam: Yes. They target the Clinical Target Volume—the tumor plus a safety margin from the starting scan. Training penalizes errors only there, not the whole image. This prioritizes the key spot where radiation hits.
Alex: No need to re-outline the tumor each time. Do predictions better capture shrinkage?
Sam: The evidence points that way. Volume checks show errors under 25%—clinically acceptable—even at higher doses. Visuals confirm diffusion shows steady tumor regression without glitches.
Alex: Diffusion takes more computing power, but the study notes costs drop after training. VT could let doctors test dose plans on a computer first.
Sam: Yes. It closes the gap between scans, aiding adaptive radiotherapy by simulating responses quickly.
Alex: A practical step forward in tracking treatment effects. Thanks for joining us on ResearchPod.