ImmunoDiff: A Diffusion Model for Immunotherapy Response Prediction in Lung Cancer
Journal:
arXiv
Published Date:
May 29, 2025
Abstract
Accurately predicting immunotherapy response in Non-Small Cell Lung Cancer
(NSCLC) remains a critical unmet need. Existing radiomics and deep
learning-based predictive models rely primarily on pre-treatment imaging to
predict categorical response outcomes, limiting their ability to capture the
complex morphological and textural transformations induced by immunotherapy.
This study introduces ImmunoDiff, an anatomy-aware diffusion model designed to
synthesize post-treatment CT scans from baseline imaging while incorporating
clinically relevant constraints. The proposed framework integrates anatomical
priors, specifically lobar and vascular structures, to enhance fidelity in CT
synthesis. Additionally, we introduce a novel cbi-Adapter, a conditioning
module that ensures pairwise-consistent multimodal integration of imaging and
clinical data embeddings, to refine the generative process. Additionally, a
clinical variable conditioning mechanism is introduced, leveraging demographic
data, blood-based biomarkers, and PD-L1 expression to refine the generative
process. Evaluations on an in-house NSCLC cohort treated with immune checkpoint
inhibitors demonstrate a 21.24% improvement in balanced accuracy for response
prediction and a 0.03 increase in c-index for survival prediction. Code will be
released soon.
