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 language | English (US) |
|---|---|
| Article number | 140 |
| Journal | npj Systems Biology and Applications |
| Volume | 11 |
| Issue number | 1 |
| DOIs | |
| State | Published - 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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