Novel Functional Radiomics for Prediction of Cardiac Positron Emission Tomography Avidity in Lung Cancer Radiotherapy.

Abstract

Purpose: 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 (¹⁸F) 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 ¹⁸F-FDG PET/CT scans before thoracic radiation therapy.

Methods: Pretreatment ¹⁸F-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.

Results: 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.

Conclusion: This work used an extensive patient data set to develop a functional cardiac radiomic model from standard-of-care ¹⁸F-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.