[Submitted on 17 Oct 2026]
Title:CaMeWM: A Causal Medical World Model for Generative Tumor Evolution and Treatment Planning
View a PDF of the paper titled CaMeWM: A Causal Medical World Model for Generative Tumor Evolution and Treatment Planning, by Huanlong Gao and 3 other authors
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Abstract:Treatment planning is intrinsically counterfactual: clinicians must compare how a patient's tumor and survival would evolve under competing regimens, whereas a retrospective cohort records only the regimen received and one follow-up scan. A treatment-conditioned generator trained on such data can therefore reproduce historical treatment allocation rather than simulate the interventional disease dynamics needed to rank regimens. We introduce \textbf{CaMeWM}, a causal medical world model for supported post-TACE tumor evolution and treatment planning. CaMeWM implements one intervention-to-decision loop rather than three independent prediction tasks: it first admits a queried regimen only for clinically comparable histories with empirical treatment support; it then generates a distribution of lesion-local follow-up worlds through an action-conditioned response descriptor, deterministic anchor, and residual 3D diffusion transition; and it finally maps sampled worlds to restricted mean survival time (RMST) with a response-Cox utility model. Under pre-treatment adjustment and positivity, this loop estimates a prespecified, support-restricted g-formula target; all selection and nuisance estimation are fold-local, and the induced policy is evaluated by cross-fitted, censoring-adjusted doubly robust estimation. Across 79 paired out-of-fold episodes, CaMeWM achieves 0.618 SSIM and 0.528 change-map SSIM for follow-up CT transition, while its generated-world survival pathway attains a 0.615 C-index; factual-only AIPW estimates a 9.60-week RMST gain (95\% CI 0.61--18.13) for the frozen policy. CaMeWM provides an inspectable basis for retrospective, support-restricted treatment planning.
Comments: 30 pages,8 figures,12 tables,including supplementary material
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
Cite as: arXiv:2610.26923 [cs.CV]
(or arXiv:2610.26923v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2610.26923
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Huanlong Gao [view email] [v1] Sat, 17 Oct 2026 11:53:00 UTC (5,390 KB)
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