Measurable imaging-based changes in enhancement of intrahepatic cholangiocarcinoma after radiotherapy reflect physical mechanisms of response

Brian De, Prashant Dogra, Mohamed Zaid, Dalia Elganainy, Kevin Sun, Ahmed M. Amer, Charles Wang, Michael K. Rooney, Enoch Chang, Hyunseon C. Kang, Zhihui Wang, Priya Bhosale, Bruno C. Odisio, Timothy E. Newhook, Ching Wei D. Tzeng, Hop S. Tran Cao, Yun S. Chun, Jean Nicholas Vauthey, Sunyoung S. Lee, Ahmed KasebKanwal Raghav, Milind Javle, Bruce D. Minsky, Sonal S. Noticewala, Emma B. Holliday, Grace L. Smith, Albert C. Koong, Prajnan Das, Vittorio Cristini, Ethan B. Ludmir, Eugene J. Koay

Research output: Contribution to journalArticlepeer-review

Abstract

Escalated doses of radiotherapy associate with improved local control and overall survival (OS) in intrahepatic cholangiocarcinoma (iCCA), but personalization remains limited because conventional size-based CT criteria correlate poorly with outcomes. We hypothesized that quantitative enhancement measurements would better predict clinical outcomes and guide individualized RT optimization. In a retrospective cohort of 154 patients, we analyzed pre- and post-RT CT scans using quantitative European Association for Study of Liver (qEASL) to derive viable tumor volumes, comparing enhancement-based metrics with size-based RECIST and linking them to outcomes via survival and mathematical modeling. Change in enhancement volume was strongly associated with OS after adjustment, outperforming RECIST, and a ≥ 33% reduction optimally distinguished responders. From modeling analyses, the patient-specific tumor growth rate parameter emerged as the dominant mechanistic predictor, achieving 80.5% classification accuracy. Importantly, CT-derived mathematical parameters from this framework may inform RT planning and dose adaptation, particularly for resistant tumors, by bridging imaging with mechanistic insight.

Original languageEnglish (US)
Article number140
Journalnpj Systems Biology and Applications
Volume11
Issue number1
DOIs
StatePublished - Dec 2025

ASJC Scopus subject areas

  • Modeling and Simulation
  • General Biochemistry, Genetics and Molecular Biology
  • Drug Discovery
  • Computer Science Applications
  • Applied Mathematics

Divisions

  • Medical Oncology

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