Risk prediction models for biochemical recurrence of Chinese prostate cancer patients after radical prostatectomy based on magnetic resonance imaging examination: a systematic review.

Background: Biochemical recurrence (BCR) after radical prostatectomy (RP) affects the prognosis of patients and early accurate prediction is crucial. Magnetic resonance imaging (MRI) is of great value in the assessment of prostate cancer (PCa). However, there is a lack of a systematic summary of the current research status for the construction of a postoperative BCR prediction model applicable to Chinese PCa patients based on MRI features. This study aimed to systematically evaluate the predictive performance and clinical applicability of the available models.

Methods: A standardized search of relevant literature in the PubMed, Cochrane Library, Embase, Web of Science, CINAHL, CNKI, VIP, Wanfang Data, and CBM databases was performed, with the search time restricted to the establishment of the database to 11 September 2024. Studies that developed and/or validated prediction models based on MRI examination to identify and/or predict BCR in patients after RP in China were included. Two researchers independently screened the literature and used the prediction model risk of bias assessment tool to assess the quality of research on the prediction models and performed descriptive analyses of predictor variables for modeling.

Results: A total of 17 studies were included, and 41 prediction models for BCR risk in Chinese patients after RP based on MRI examination were constructed, with the area under the receiver operating characteristic curve (AUC) or concordance index (C-index) of the cases ranging from 0.610 to 0.982. A total of 36 prediction models had good predictive performance, eight studies performed model calibration, two studies performed internal validation, two studies performed external validation, and seven studies conducted both internal and external validation. The results of the quality assessment revealed that all 17 studies were at high risk of bias. The most frequent predictors were prostate-specific antigen (PSA) level, MRI image features, and Gleason score.

Conclusions: At present, a prediction model based on MRI examination for the risk of BCR in Chinese patients after RP is still in the development stage, and the overall quality of research needs to be further improved. In the future, the study design and reporting process should be improved, and the existing model should be validated to provide a basis for the development of effective prevention strategies.