
==== Front
medRxiv
MEDRXIV
medRxiv
Cold Spring Harbor Laboratory

10.1101/2024.09.11.24313334
preprint
1
Article
Measurable imaging-based changes in enhancement of intrahepatic cholangiocarcinoma after radiotherapy reflect physical mechanisms of response
De Brian
Dogra Prashant http://orcid.org/0000-0001-6722-7371

Zaid Mohamed
Elganainy Dalia
Sun Kevin
Amer Ahmed M.
Wang Charles
Rooney Michael K.
Chang Enoch
Kang Hyunseon C.
Wang Zhihui
Bhosale Priya
Odisio Bruno C.
Newhook Timothy E.
Tzeng Ching-Wei D.
Cao Hop S. Tran
Chun Yun S.
Vauthey Jean-Nicholas
Lee Sunyoung S.
Kaseb Ahmed
Raghav Kanwal
Javle Milind
Minsky Bruce D.
Noticewala Sonal S.
Holliday Emma B.
Smith Grace L.
Koong Albert C.
Das Prajnan
Cristini Vittorio
Ludmir Ethan B.
Koay Eugene J.
12 9 2024
2024.09.11.24313334https://creativecommons.org/licenses/by-nc-nd/4.0/ This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which allows reusers to copy and distribute the material in any medium or format in unadapted form only, for noncommercial purposes only, and only so long as attribution is given to the creator.
http://medrxiv.org/lookup/doi/10.1101/2024.09.11.24313334
nihpp-2024.09.11.24313334.pdf
Abstract

Background

Although escalated doses of radiation therapy (RT) for intrahepatic cholangiocarcinoma (iCCA) are associated with durable local control (LC) and prolonged survival, uncertainties persist regarding personalized RT based on biological factors. Compounding this knowledge gap, the assessment of RT response using traditional size-based criteria via computed tomography (CT) imaging correlates poorly with outcomes. We hypothesized that quantitative measures of enhancement would more accurately predict clinical outcomes than size-based assessment alone and developed a model to optimize RT.

Methods

Pre-RT and post-RT CT scans of 154 patients with iCCA were analyzed retrospectively for measurements of tumor dimensions (for RECIST) and viable tumor volume using quantitative European Association for Study of Liver (qEASL) measurements. Binary classification and survival analyses were performed to evaluate the ability of qEASL to predict treatment outcomes, and mathematical modeling was performed to identify the mechanistic determinants of treatment outcomes and to predict optimal RT protocols.

Results

Multivariable analysis accounting for traditional prognostic covariates revealed that percentage change in viable volume following RT was significantly associated with OS, outperforming stratification by RECIST. Binary classification identified ≥33% decrease in viable volume to optimally correspond to response to RT. The model-derived, patient-specific tumor enhancement growth rate emerged as the dominant mechanistic determinant of treatment outcome and yielded high accuracy of patient stratification (80.5%), strongly correlating with the qEASL-based classifier.

Conclusion

Following RT for iCCA, changes in viable volume outperformed radiographic size-based assessment using RECIST for OS prediction. CT-derived tumor-specific mathematical parameters may help optimize RT for resistant tumors.
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