Machine learning in predicting extracapsular extension (ECE) of prostate cancer with MRI: a protocol for a systematic literature review.

Introduction: In patients with prostate cancer (PCa), the detection of extracapsular extension (ECE) and seminal vesicle invasion is not only important for selecting the appropriate therapy but also for preoperative planning and patient prognosis. It is of paramount importance to stage PCa correctly before surgery, in order to achieve better surgical and outcome results. Over the last years, MRI has been incorporated in the classical prostate staging nomograms with clinical improvement accuracy in detecting ECE, but with variability between studies and radiologist's experience.
Methods and Analysis: The research question, based on patient, index test, comparator, outcome and study design criteria, was the following: what is the diagnostic performance of artificial intelligence algorithms for predicting ECE in PCa patients, when compared with that of histopathological results after radical prostatectomy. To answer this question, we will use databases (EMBASE, PUBMED, Web of Science and CENTRAL) to search for the different studies published in the literature and we use the QUADA tool to evaluate the quality of the research selection.
Ethics and Dissemination: This systematic review does not require ethical approval. The results will be disseminated through publication in a peer-review journal, as a chapter of a doctoral thesis and through presentations at national and international conferences.
Prospero Registration Number: CRD42020215671.
Competing Interests: Competing interests: None declared.
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