DataSheet_1_Dynamic radiomics for predicting the efficacy of antiangiogenic therapy in colorectal liver metastases.pdf

Background and objectiveFor patients with advanced colorectal liver metastases (CRLMs) receiving first-line anti-angiogenic therapy, an accurate, rapid and noninvasive indicator is urgently needed to predict its efficacy. In previous studies, dynamic radiomics predicted more accurately than conventional radiomics. Therefore, it is necessary to establish a dynamic radiomics efficacy prediction model for antiangiogenic therapy to provide more accurate guidance for clinical diagnosis and treatment decisions.
MethodsIn this study, we use dynamic radiomics feature extraction method that extracts static features using tomographic images of different sequences of the same patient and then quantifies them into new dynamic features for the prediction of treatmentefficacy. In this retrospective study, we collected 76 patients who were diagnosed with unresectable CRLM between June 2016 and June 2021 in the First Hospital of China Medical University. All patients received standard treatment regimen of bevacizumab combined with chemotherapy in the first-line treatment, and contrast-enhanced abdominal CT (CECT) scans were performed before treatment. Patients with multiple primary lesions as well as missing clinical or imaging information were excluded. Area Under Curve (AUC) and accuracy were used to evaluate model performance. Regions of interest (ROIs) were independently delineated by two radiologists to extract radiomics features. Three machine learning algorithms were used to construct two scores based on the best response and progression-free survival (PFS).
ResultsFor the task that predict the best response patients will achieve after treatment, by using ROC curve analysis, it can be seen that the relative change rate (RCR) feature performed best among all features and best in linear discriminantanalysis (AUC: 0.945 and accuracy: 0.855). In terms of predicting PFS, the Kaplan–Meier plots suggested that the score constructed using the RCR features could significantly distinguish patients with good response from those with poor response (Two-sided P<0.0001 for survival analysis).
ConclusionsThis study demonstrates that the application of dynamic radiomics features can better predict the efficacy of CRLM patients receiving antiangiogenic therapy compared with conventional radiomics features. It allows patients to have a more accurate assessment of the effect of medical treatment before receiving treatment, and this assessment method is noninvasive, rapid, and less expensive. Dynamic radiomics model provides stronger guidance for the selection of treatment options and precision medicine.

研究背景与目的
对于接受一线抗血管生成治疗的晚期结直肠癌肝转移（colorectal liver metastases, CRLMs）患者，目前亟需精准、快速且无创的指标以预测其治疗疗效。既往研究表明，动态组学（dynamic radiomics）的预测效能优于常规组学。因此，本研究旨在构建针对抗血管生成治疗的动态组学疗效预测模型，为临床诊疗决策提供更为精准的指导。

研究方法
本研究采用动态组学特征提取方法：先对同一患者不同序列的断层影像提取静态特征，再将其量化为全新的动态特征，用于治疗疗效预测。本研究为回顾性研究，共纳入2016年6月至2021年6月期间于中国医科大学附属第一医院确诊为不可切除性结直肠癌肝转移的76例患者。所有患者均接受贝伐珠单抗联合化疗的一线标准治疗方案，并于治疗前完成腹部增强CT（contrast-enhanced abdominal CT, CECT）扫描。排除存在多原发灶及临床或影像信息缺失的患者。本研究采用曲线下面积（Area Under Curve, AUC）与准确率评估模型性能。由两名放射科医师独立勾画感兴趣区（regions of interest, ROIs）以提取组学特征。基于治疗最佳应答与无进展生存期（progression-free survival, PFS），采用三种机器学习算法构建两类评分模型。

研究结果
针对预测患者治疗后可达到的最佳应答这一任务，经受试者工作特征（Receiver Operating Characteristic, ROC）曲线分析可见，相对变化率（relative change rate, RCR）特征在所有特征中表现最优，在线性判别分析中效能最佳（曲线下面积AUC=0.945，准确率=0.855）。在预测无进展生存期方面，Kaplan-Meier生存曲线图显示，基于RCR特征构建的评分模型可显著区分应答良好与应答不良的患者（生存分析双侧P<0.0001）。

研究结论
本研究证实，相较于常规组学特征，动态组学特征可更好地预测接受抗血管生成治疗的结直肠癌肝转移患者的疗效。该方法可使患者在接受治疗前更为精准地评估药物治疗效果，且具备无创、快速、低成本的优势。动态组学模型可为治疗方案选择与精准医学提供更强有力的指导。