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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月12日

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  • 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 Kaseb,
  • Kanwal 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

摘要

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.