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 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 Koay
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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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放疗后肝内胆管癌增强的可测量成像变化反映了反应的物理机制
背景:虽然肝内胆管癌(iCCA)的升级剂量放射治疗(RT)与持久的局部控制(LC)和延长生存期有关,但基于生物学因素的个性化 RT 仍存在不确定性。此外,通过计算机断层扫描(CT)成像使用基于大小的传统标准评估 RT 反应与预后的相关性也很差。我们假设,与单纯基于大小的评估相比,增强的定量指标能更准确地预测临床结果,并建立了一个模型来优化 RT。方法:回顾性分析了154例iCCA患者的RT前和RT后CT扫描,测量肿瘤尺寸(RECIST)和使用欧洲肝脏研究协会(qEASL)定量测量的存活肿瘤体积。为了评估 qEASL 预测治疗结果的能力,研究人员进行了二元分类和生存分析,并建立了数学模型,以确定治疗结果的机理决定因素,并预测最佳 RT 方案。结果显示考虑传统预后协变量的多变量分析表明,RT后存活体积百分比变化与OS显著相关,优于RECIST分层。二元分类确定了存活体积减少≥33%与RT反应的最佳对应关系。从模型得出的患者特异性肿瘤增强生长率是决定治疗结果的主要机制,对患者进行分层的准确率很高(80.5%),与基于qEASL的分类器密切相关。结论iCCA患者接受RT治疗后,在预测OS方面,存活体积的变化优于使用RECIST进行的基于放射学大小的评估。CT 导出的肿瘤特异性数学参数有助于优化耐药肿瘤的 RT 治疗。
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