Keywords: Cancer, Prostate, metastasesThis 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.
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