整合临床变量、放射组学和肿瘤来源的游离 DNA，以增强对可切除食管腺癌结果的预测。
Integrating Clinical Variables, Radiomics, and Tumor-derived Cell-free DNA for Enhanced Prediction of Resectable Esophageal Adenocarcinoma Outcomes.
发表日期：2024 Oct 16
作者：
Tom van den Ende, Steven C Kuijper, Yousif Widaatalla, Wyanne A Noortman, Floris H P van Velden, Henry C Woodruff, Ymke van der Pol, Norbert Moldovan, D Michiel Pegtel, Sarah Derks, Maarten F Bijlsma, Florent Mouliere, Prof Lioe-Fee de Geus-Oei, Prof Philippe Lambin, Prof Hanneke W M van Laarhoven
来源：
Int J Radiat Oncol
摘要：
整合临床变量、放射组学和肿瘤源性无细胞 DNA (cfDNA) 对于预测可切除食管腺癌 (rEAC) 患者的生存和放化疗反应的价值尚不清楚。我们的目的是调查放射组学和 cfDNA 指标与临床变量相结合是否可以改善个性化预测。来自两个中心接受新辅助放化疗的 111 名 rEAC 患者组成的队列被用于探索性回顾性分析。使用弹性网络回归构建模型，将 SOURCE 生存模型的临床变量与放射组学特征和 cfDNA 相结合，并使用 5 倍交叉验证进行内部验证。使用 C 指数和病理完全缓解 (pCR) 曲线下面积 (AUC) 评估模型的总生存 (OS) 和进展时间 (TTP) 结果：OS 和 TTP 表现最佳的基线模型是基于 SOURCE-cfDNA 组合，其 C 指数达到 0.55 和 0.59，而单独使用 SOURCE 的 C 指数为 0.44-0.45。在 SOURCE 中添加重新分期 PET 放射组学是预测 OS（C 指数：0.65）和 TTP（C 指数：0.60）最有希望的补充。通过将 SOURCE 与放射组学或 cfDNA 相结合，实现了 OS 和 TTP 的基线风险分层，对数秩 p<0.01。用于预测 pCR 的最佳组合模型的 AUC 达到 0.61，而单独使用 SOURCE 变量的 AUC 为 0.47。放射组学和 cfDNA 的添加可以提高已建立的生存模型的性能。外部有效性需要在未来的研究中与放射组学管道的优化一起进一步评估。版权所有 © 2024。由 Elsevier Inc. 出版。
The value of integrating clinical variables, radiomics, and tumor-derived cell-free DNA (cfDNA) for the prediction of survival and response to chemoradiation of resectable esophageal adenocarcinoma (rEAC) patients is not yet known. Our aim was to investigate if radiomics and cfDNA metrics combined with clinical variables can improve personalized predictions.A cohort of 111 rEAC patients from two centers treated with neoadjuvant chemoradiotherapy was used for exploratory retrospective analyses. Models combining the clinical variables of the SOURCE survival model with radiomic features and cfDNA, were built using elastic net regression and internally validated using 5-fold cross validation. Model performance for overall survival (OS) and time to progression (TTP) were evaluated with the C-index and the area under the curve (AUC) for pathological complete response (pCR) RESULTS: The best performing baseline models for OS and TTP were based on the combination of SOURCE-cfDNA which reached a C-index of 0.55 and 0.59 compared to 0.44-0.45 with SOURCE alone. The addition of re-staging PET radiomics to SOURCE was the most promising addition for predicting OS (C-index: 0.65) and TTP (C-index: 0.60). Baseline risk-stratification was achieved for OS and TTP by combining SOURCE with radiomics or cfDNA, log-rank p<0.01. The best performing combination model for the prediction of pCR reached an AUC of 0.61 compared to 0.47 with SOURCE variables alone.The addition of radiomics and cfDNA can improve the performance of an established survival model. External validity needs to be further assessed in future studies together with the optimization of radiomic pipelines.Copyright © 2024. Published by Elsevier Inc.