Focal nodular enhancement on DCE MRI of the prostatectomy bed: radiologic-pathologic correlations and prognostic value

Prostate Imaging for Recurrence Reporting System Made Easy: A Case-Based Review of Prostate Cancer Local Recurrence on Magnetic Resonance Imaging

Prostate cancer (PCa) is one of the most prevalent cancers worldwide. Even following appropriate initial treatments, a subset of the patients develops tumor recurrence. Magnetic resonance imaging (MRI) is pivotal in investigating local recurrence, but its performance is limited in detecting recurrence at other sites (especially in subcentimeter lymph nodes). Recently, an expert consensus proposed a scoring system for MRI-based assessment of local recurrence, called prostate imaging for recurrence reporting system (PI-RR). This case-based review describes the expected post-treatment changes and MRI findings of local PCa recurrence after RP and RT.

Prostate Magnetic Resonance Imaging Using the Prostate Imaging for Recurrence Reporting (PI-RR) Scoring System to Detect Recurrent Prostate Cancer: A Systematic Review and Meta-analysis

BACKGROUND AND OBJECTIVE

Prostate Imaging for Recurrence Reporting (PI-RR) was introduced in 2021 to standardize the interpretation and reporting of multiparametric magnetic resonance imaging (MRI) for prostate cancer following whole-gland treatment. The system scores image on a scale from 1 to 5 and has shown promising results in single-center studies. The aim of our systematic review and meta-analysis was to assess the diagnostic performance of the PI-RR system in predicting the likelihood of local recurrence after whole-gland treatment.

METHODS

The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines for diagnostic test accuracy were followed. Relevant databases were searched up to December 2023. Primary studies met the eligibility criteria if they reported MRI diagnostic performance in prostate cancer recurrence using PI-RR. Diagnostic performance for MRI was assessed using two different cutoff points (≥3 or ≥4 for positivity according to the PI-RR system). A meta-analysis with a random-effects model was used to estimate pooled sensitivity and specificity values.

KEY FINDINGS AND LIMITATIONS

Sixteen articles were identified for full-text reading, of which six were considered eligible, involving a total of 467 patients. Using a cutoff of PI-RR ≥3 (4 studies) for recurrent disease, the sensitivity was 77.8% (95% confidence interval [CI] 69.9-84.1%) and the specificity was 80.2% (95% CI 58.2-92.2%). Using a cutoff of PI-RR ≥4 (4 studies), the sensitivity was 61.9% (95% CI 35.6-82.7%) and the specificity was 86.6% (95% CI 75.1-93.3%). Overall, the inter-rater agreement varied from fair to excellent.

CONCLUSIONS AND CLINICAL IMPLICATIONS

PI-RR is accurate in detecting local recurrence after whole-gland treatment for prostate cancer and shows fair-to-good to excellent inter-reader agreement. Overall, a PI-RR cutoff of ≥3 showed high sensitivity and specificity.

PATIENT SUMMARY

We reviewed studies that reported on how good MRI scans using a scoring system called PI-RR were in detecting recurrence of prostate cancer. We found that this system shows good performance, with fair to excellent agreement between different radiologists.

The practical clinical role of machine learning models with different algorithms in predicting prostate cancer local recurrence after radical prostatectomy

BACKGROUND

The detection of local recurrence for prostate cancer (PCa) patients following radical prostatectomy (RP) is challenging and can influence the treatment plan. Our aim was to construct and verify machine learning models with three different algorithms based on post-operative mpMRI for predicting local recurrence of PCa after RP and explore their potential clinical value compared with the Prostate Imaging for Recurrence Reporting (PI-RR) score of expert-level radiologists.

METHODS

A total of 176 patients were retrospectively enrolled and randomly divided into training (n = 123) and testing (n = 53) sets. The PI-RR assessments were performed by two expert-level radiologists with access to the operative histopathological and pre-surgical clinical results. The radiomics models to predict local recurrence were built by utilizing three different algorithms (i.e., support vector machine [SVM], linear discriminant analysis [LDA], and logistic regression-least absolute shrinkage and selection operator [LR-LASSO]). The combined model integrating radiomics features and PI-RR score was developed using the most effective classifier. The classification performances of the proposed models were assessed by receiver operating characteristic (ROC) curve analysis.

RESULTS

There were no significant differences between the training and testing sets concerning age, prostate-specific antigen (PSA), Gleason score, T-stage, seminal vesicle invasion (SVI), perineural invasion (PNI), and positive surgical margins (PSM). The radiomics model based on LR-LASSO exhibited superior performance than other radiomics models, with an AUC of 0.858 in the testing set; the PI-RR yielded an AUC of 0.833, and there was no significant difference between the best radiomics model and the PI-RR score. The combined model achieved the best predictive performance with an AUC of 0.924, and a significant difference was observed between the combined model and PI-RR score.

CONCLUSIONS

Our radiomics model is an effective tool to predict PCa local recurrence after RP. By integrating radiomics features with the PI-RR score, our combined model exhibited significantly better predictive performance of local recurrence than expert-level radiologists' PI-RR assessment.

Evaluation of Prostate Cancer Recurrence with MR Imaging and Prostate Imaging for Recurrence Reporting Scoring System

Detection of prostate cancer recurrence after whole-gland treatment with curative intent is critical to identify patients who may benefit from local salvage therapy. Among the different imaging modalities used in clinical practice, MR imaging is the most accurate in identifying local prostate cancer recurrence; indeed, it is an excellent technique for local recurrence detection superior to PET/CT, even at low PSA, but provides no information about extra-pelvic lymph nodes or bone metastasis. In 2021, a group of experts developed the Prostate Imaging for local Recurrence Reporting scoring system to standardize acquisition, interpretation, and reporting of prostate cancer recurrence.