A Patient-Specific Digital Twin for Adaptive Radiotherapy of Non-Small Cell Lung Cancer

• URL: http://arxiv.org/abs/2602.18496v1
• Date: Sun, 15 Feb 2026 01:47:31 GMT
• Title: A Patient-Specific Digital Twin for Adaptive Radiotherapy of Non-Small Cell Lung Cancer
• Authors: Anvi Sud, Jialu Huang, Gregory R. Hart, Keshav Saxena, John Kim, Lauren Tressel, Jun Deng,
• Abstract summary: We developed a temporal digital twin architecture for safe radiotherapy.<n>A GRU autoencoder was employed to learn organ specific latent trajectories, which were classified via logistic regression to predict toxicity.<n>Our findings revealed a viable AI driven early warning window, as increasing risk ratings occurred from several fractions before clinical toxicity.
• Score: 5.3024618575567235
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Radiotherapy continues to become more precise and data dense, with current treatment regimens generating high frequency imaging and dosimetry streams ideally suited for AI driven temporal modeling to characterize how normal tissues evolve with time. Each fraction in biologically guided radiotherapy(BGRT) treated non small cell lung cancer (NSCLC) patients records new metabolic, anatomical, and dose information. However, clinical decision making is largely informed by static, population based NTCP models which overlook the dynamic, unique biological trajectories encoded in sequential data. We developed COMPASS (Comprehensive Personalized Assessment System) for safe radiotherapy, functioning as a temporal digital twin architecture utilizing per fraction PET, CT, dosiomics, radiomics, and cumulative biologically equivalent dose (BED) kinetics to model normal tissue biology as a dynamic time series process. A GRU autoencoder was employed to learn organ specific latent trajectories, which were classified via logistic regression to predict eventual CTCAE grade 1 or higher toxicity. Eight NSCLC patients undergoing BGRT contributed to the 99 organ fraction observations covering 24 organ trajectories (spinal cord, heart, and esophagus). Despite the small cohort, intensive temporal phenotyping allowed for comprehensive analysis of individual dose response dynamics. Our findings revealed a viable AI driven early warning window, as increasing risk ratings occurred from several fractions before clinical toxicity. The dense BED driven representation revealed biologically relevant spatial dose texture characteristics that occur before toxicity and are averaged out with traditional volume based dosimetry. COMPASS establishes a proof of concept for AI enabled adaptive radiotherapy, where treatment is guided by a continually updated digital twin that tracks each patients evolving biological response.

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