Because of the high diagnostic accuracy for sPC detection, upfront mpMRI has been recommended as a triage test to indicate the need for biopsy among biopsy-naïve men in whom sPC was suspected due to high PSA [17-19]. As a result of the high negative predictive value, men with no suspected evidence of sPC on MRI may defer systematic biopsy . Moreover, to improve predictive values, new multivariate risk prediction tools have recently been constructed using the mpMRI suspicion score [9,10,21].
Recently, performing prostate MRI without DCE, a procedure termed “bi-parametric MRI” (bpMRI) garners beneficial results. The effectiveness of bpMRI for the detecting sPC in biopsy-naïve patients has been reported. And the bpMRI has the advantage that there are no adverse events that have been associated with some gadolinium-based contrast agents, shortened examination time and reduced costs . On the other hand DCE MRI has been reported to improve the sensitivity of MRI for the detecting sPC. But at the same time the predictive models based on bpMRI findings and clinical parameters for risk assessment and selection of sPC have also recently been reported [14,15,23,24].
In a Japanese cohort, the efficacy of mpMRI and bpMRI for detecting sPC as a triage test was also reported [25-27]. However, no multivariate risk prediction models for detecting sPC based on PI-RADS scores of mpMRI or bpMRI as ordinal variables among Japanese populations have been reported previously.
The characteristics of our novel risk model were as follows. First, in all cases, bpMRI were performed on the pre-biopsy setting, because biopsy artifacts could affect bpMRI findings and this model was constructed to reduce unnecessary biopsy. Second, a variable of DRE used in other nomograms was not included in this study. Because anterior prostate cancer is less commonly palpable, if DRE is used as a variable in the prediction model, the dataset of the model should ideally be divided into two groups according to whether DRE findings are positive, and each model should be constructed independently . The small size of our dataset could not be divided into groups.
PI-RADS score contributed significantly to the model, like other parameters from multivariate logistic regression analysis. Interestingly, the odds ratio of PI-RADS score 2 compared to score1 was 0.292 (P=0.098) and PI-RADS score 3 compared to score 1 was 2.005 (P=0.332) (Table 2). PI-RADS score 1 and score 2 indicated normal prostate gland and benign prostate disease (inflammatory and/or hyperplasia) respectively. In a proportion of cases with PI-RADS score 2, PSA was elevated because of inflammation and hyperplasia. Therefore, among high-PSA cases, PI-RADS score 1 might carry a higher risk of sPC than PI-RADS score 2 in real clinical practice. Moreover, because of the low number of PI-RADS score 1 (only 11 cases (1.42%)), the odds ratio for PI-RADS score 2 to score 1 might not reach statistical significance. This also explained why lower PV cases tended to carry a higher risk of sPC. This was presumably because multicollinearity among parameters could not be excluded completely even if multivariate analysis was performed.
Low PI-RADS score harbors a 5–10% risk of sPC, allowing biopsy to be potentially avoided [29,30]. ROC analysis revealed this novel model offered a high AUC (c index=0.862) approximately equivalent to previous reports, although this novel model lacked external validation and should not be compared to other risk models constructed from different regional and ethnical cohorts . The risk model enable avoidance of unnecessary biopsies in more patients without increasing the risk of missing a diagnosis of sPC at an arbitrary probability threshold. More specifically, at probability thresholds of 10% and 20% in this model and with a cut-off PI-RADS score between 2 and 3, the net reductions in biopsies were 43.0%, 57.0% and 57.0% while the rates of missing sPC were 2.3%, 6.4% and 6.4%, respectively. Using DCA, the present study showed that the risk model using PI-RADS scores improved clinical decisions for biopsy of patients with suspected sPC, as compared with clinical parameter models or PI-RADS score alone. The risk model provided benefits in the decision to biopsy patients for sPC at probability thresholds exceeding 10%. From a practical perspective, at various probability cutoffs, the combined models demonstrated the best performance among all prediction parameters. Although cost-effectiveness remains an issue due to differences in social insurance situations and the high penetration rate of MRI in other countries, a protocol for biopsy indications for MRI in cases with high PSA value should be considered.
The present findings should be interpreted in the context of some limitations. First, this study represented a retrospective analysis that elevated the risk of selection biases. Second, inter-reader agreement on bpMRI was not evaluated in the present study. Third, low numbers of systemic biopsy cores were collected in our cohort. The number of sPC lesions detected by systemic biopsy was thought to be lower and could have improved model accuracy and internal validation. Last, no external validation was performed. If the excellent results obtained with bpMRI and other clinical parameters from a single institution like this study are not reproduced in other hospitals, the broad use of the novel risk model will lead to patient mismanagement in a substantial proportion of cases.
To the best of our knowledge, this represents the first report of a risk calculator and nomogram using PI-RADS version 2 score of bpMRI among Japanese males for detecting sPC in pre-biopsy settings. On the other hand, recent risk models have been reported to detect sPC using quantitative mpMRI, which may also help standardize mpMRI and bpMRI interpretation and image recognition using new statistical tools (machine learning, deep learning and neural network analysis) [31,32]. Risk models using genetic elements and molecular markers rather than image variables are also being reported . Lastly, prospective and multi-centric risk models for sPC risk prediction including such new biochemical parameters, financial aspects and novel MRI fusion biopsy data are expected to be established in the future.