Semiautomated Pelvic Lymph Node Treatment Response Evaluation for Patients with Advanced Prostate Cancer: Based on MET-RADS-P Guidelines

This retrospective study aims to develop and evaluate a deep learning-based algorithm for semiautomated treatment response assessment of pelvic lymph nodes. A total of 162 patients who had undergone at least two scans for follow-up assessment after advanced prostate cancer metastasis treatment were enrolled. A previously reported deep learning model was used to perform automated segmentation of pelvic lymph nodes. Our results showed that the accuracies of automated segmentation-based response assessment were high for all the target lesions, nontarget lesions and nonpathological lesions according to MET-RADS-P criteria and achieved good consistency with the attending radiologist and fellow radiologist.