Evaluating Automated Tools for Lesion Detection on F Fluoroestradiol PET/CT Images and Assessment of Concordance with Standard-of-Care Imaging in Metastatic Breast Cancer.

Purpose To evaluate two automated tools for detecting lesions on fluorine 18 (F) fluoroestradiol (FES) PET/CT images and assess concordance of F-FES PET/CT with standard diagnostic CT and/or F fluorodeoxyglucose (FDG) PET/CT in patients with breast cancer. Materials and Methods This retrospective analysis of a prospective study included participants with breast cancer who underwent F-FES PET/CT examinations ( = 52), F-FDG PET/CT examinations ( = 13 of 52), and diagnostic CT examinations ( = 37 of 52). A convolutional neural network was trained for lesion detection using manually contoured lesions. Concordance in lesions labeled by a nuclear medicine physician between F-FES and F-FDG PET/CT and between F-FES PET/CT and diagnostic CT was assessed using an automated software medical device. Lesion detection performance was evaluated using sensitivity and false positives per participant. Wilcoxon tests were used for statistical comparisons. Results The study included 52 participants. The lesion detection algorithm achieved a median sensitivity of 62% with 0 false positives per participant. Compared with sensitivity in overall lesion detection, the sensitivity was higher for detection of high-uptake lesions (maximum standardized uptake value > 1.5, = .002) and similar for detection of large lesions (volume > 0.5 cm, = .15). The artificial intelligence (AI) lesion detection tool was combined with a standardized uptake value threshold to demonstrate a fully automated method of labeling patients as having FES-avid metastases. Additionally, automated concordance analysis showed that 17 of 25 participants (68%) had over half of the detected lesions across two modalities present on F-FES PET/CT images. Conclusion An AI model was trained to detect lesions on F-FES PET/CT images and an automated concordance tool measured heterogeneity between F-FES PET/CT and standard-of-care imaging. Molecular Imaging-Cancer, Neural Networks, PET/CT, Breast, Computer Applications-General (Informatics), Segmentation, F-FES PET, Metastatic Breast Cancer, Lesion Detection, Artificial Intelligence, Lesion Matching Clinical Trials Identifier: NCT04883814 Published under a CC BY 4.0 license.