Biomarkers for evaluating tumor response to therapy and estimating the risk of disease relapse represent tremendous areas of clinical need. To evaluate treatment efficacy, tumor response is routinely assessed using different imaging modalities like positron emission tomography/computed tomography or magnetic resonance imaging. More recently, the development of circulating tumor DNA detection assays has provided a minimally invasive approach to evaluate tumor response and prognosis through a blood test (liquid biopsy). Integrating imaging- and circulating tumor DNA-based biomarkers may lead to improvements in the prediction of patient outcomes. For this mini-review, we searched the scientific literature to find original articles that combined quantitative imaging and circulating tumor DNA biomarkers to build prediction models. Seven studies reported building prognostic models to predict distant recurrence-free, progression-free, or overall survival. Three discussed building models to predict treatment response using tumor volume, pathologic complete response, or objective response as endpoints. The limited number of articles and the modest cohort sizes reported in these studies attest to the infancy of this field of study. Nonetheless, these studies demonstrate the feasibility of developing multivariable response-predictive and prognostic models using regression and machine learning approaches. Larger studies are warranted to facilitate the building of highly accurate response-predictive and prognostic models that are generalizable to other datasets and clinical settings.
用于评估肿瘤对治疗反应及预测疾病复发风险的生物标志物是临床需求巨大的研究领域。目前常规通过正电子发射断层扫描/计算机断层扫描或磁共振成像等不同影像学方法评估肿瘤治疗反应。近年来，循环肿瘤DNA检测技术的发展为通过血液检测（液体活检）评估肿瘤反应和预后提供了微创方法。整合影像学与循环肿瘤DNA生物标志物有望提升患者预后预测的准确性。本微型综述通过检索科学文献，筛选出结合定量影像学与循环肿瘤DNA生物标志物构建预测模型的研究。其中七项研究建立了预测无远处复发生存期、无进展生存期或总生存期的预后模型，三项研究以肿瘤体积、病理完全缓解或客观缓解率为终点构建治疗反应预测模型。现有研究数量有限且队列规模较小，表明该领域尚处于起步阶段。尽管如此，这些研究证实了运用回归分析和机器学习方法构建多变量治疗反应预测及预后模型的可行性。未来需要开展更大规模的研究，以建立具有高度准确性、可推广至其他数据集和临床场景的预测模型。
Integrating Imaging and Circulating Tumor DNA Features for Predicting Patient Outcomes
肿瘤(癌症)患者之家