Department of Radiation Oncology, Sidney Kimmel Medical College at Thomas Jefferson University, Philadelphia, PA.
Department of Radiology, University of Colorado School of Medicine, Aurora, CO.
JCO Clin Cancer Inform. 2024 Mar;8:e2300241. doi: 10.1200/CCI.23.00241.
Traditional methods of evaluating cardiotoxicity focus on radiation doses to the heart. Functional imaging has the potential to provide improved prediction for cardiotoxicity for patients with lung cancer. Fluorine-18 (F) fluorodeoxyglucose (FDG)-positron emission tomography (PET)/computed tomography (CT) imaging is routinely obtained in a standard cancer staging workup. This work aimed to develop a radiomics model predicting clinical cardiac assessment using F-FDG PET/CT scans before thoracic radiation therapy.
Pretreatment F-FDG PET/CT scans from three study populations (N = 100, N = 39, N = 70) were used, comprising two single-institutional protocols and one publicly available data set. A clinician (V.J.) classified the PET/CT scans per clinical cardiac guidelines as no uptake, diffuse uptake, or focal uptake. The heart was delineated, and 210 novel functional radiomics features were selected to classify cardiac FDG uptake patterns. Training data were divided into training (80%)/validation (20%) sets. Feature reduction was performed using the Wilcoxon test, hierarchical clustering, and recursive feature elimination. Ten-fold cross-validation was carried out for training, and the accuracy of the models to predict clinical cardiac assessment was reported.
From 202 of 209 scans, cardiac FDG uptake was scored as no uptake (39.6%), diffuse uptake (25.3%), and focal uptake (35.1%), respectively. Sixty-two independent radiomics features were reduced to nine clinically pertinent features. The best model showed 93% predictive accuracy in the training data set and 80% and 92% predictive accuracy in two external validation data sets.
This work used an extensive patient data set to develop a functional cardiac radiomic model from standard-of-care F-FDG PET/CT scans, showing good predictive accuracy. The radiomics model has the potential to provide an automated method to predict existing cardiac conditions and provide an early functional biomarker to identify patients at risk of developing cardiac complications after radiotherapy.
传统的心脏毒性评估方法侧重于心脏的辐射剂量。功能成像有可能为肺癌患者的心脏毒性提供更好的预测。氟-18（F）氟脱氧葡萄糖（FDG）-正电子发射断层扫描（PET）/计算机断层扫描（CT）成像在标准癌症分期检查中常规获得。本研究旨在开发一种使用氟-18 FDG PET/CT 扫描在胸部放疗前预测临床心脏评估的放射组学模型。
使用来自三个研究人群的预处理氟-18 FDG PET/CT 扫描（N=100、N=39、N=70），包括两个单机构协议和一个公开可用的数据集。临床医生（V.J.）根据临床心脏指南对 PET/CT 扫描进行分类，无摄取、弥漫摄取或局灶摄取。对心脏进行轮廓勾画，并选择 210 个新的功能放射组学特征来分类心脏 FDG 摄取模式。训练数据分为训练（80%）/验证（20%）集。使用 Wilcoxon 检验、层次聚类和递归特征消除进行特征减少。对训练集进行十折交叉验证，并报告模型预测临床心脏评估的准确性。
在 209 次扫描中的 202 次中，心脏 FDG 摄取分别被评为无摄取（39.6%）、弥漫摄取（25.3%）和局灶摄取（35.1%）。从 202 次扫描中的 209 次中，62 个独立的放射组学特征减少到 9 个临床相关特征。最佳模型在训练数据集的预测准确率为 93%，在两个外部验证数据集的预测准确率分别为 80%和 92%。
本研究使用了广泛的患者数据集，从标准护理 F-FDG PET/CT 扫描中开发了一种功能性心脏放射组学模型，显示出良好的预测准确性。该放射组学模型有可能提供一种自动方法来预测现有的心脏状况，并提供早期的功能生物标志物，以识别放疗后发生心脏并发症风险的患者。
