Clinical Application of the New Prostate Imaging for Recurrence Reporting (PI-RR) Score Proposed to Evaluate the Local Recurrence of Prostate Cancer after Radical Prostatectomy

The importance and future of prostate MRI report templates: improving oncological care

The radiologist's report is crucial for guiding care post-imaging, with ongoing advancements in report construction. Recent studies across various modalities and organ systems demonstrate enhanced clarity and communication through structured reports. This article will explain the benefits of disease-state specific reporting templates using prostate MRI as the model system. We identify key reporting components for prostate cancer detection and staging as well as imaging in active surveillance and following therapy. We discuss relevant reporting systems including PI-QUAL, PI-RADS, PRECISE, PI-RR and PI-FAB systems. Additionally, we examine optimal reporting structure including disruptive technologies such as graphical reporting and using artificial intelligence to improve report clarity and applicability.

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.

Prostate MRI for the detection of clinically significant prostate cancer: Update and future directions

PURPOSE OF REVIEW

In recent decades, there has been an increasing role for magnetic resonance imaging (MRI) in the detection of clinically significant prostate cancer (csPC). The purpose of this review is to provide an update and outline future directions for the role of MRI in the detection of csPC.

RECENT FINDINGS

In diagnosing clinically significant prostate cancer pre-biopsy, advances include our understanding of MRI-targeted biopsy, the role of biparametric MRI (non-contrast) and changing indications, for example the role of MRI in screening for prostate cancer. Furthermore, the role of MRI in identifying csPC is maturing, with emphasis on standardization of MRI reporting in active surveillance (PRECISE), clinical staging (EPE grading, MET-RADS-P) and recurrent disease (PI-RR, PI-FAB). Future directions of prostate MRI in detecting csPC include quality improvement, artificial intelligence and radiomics, positron emission tomography (PET)/MRI and MRI-directed therapy.

SUMMARY

The utility of MRI in detecting csPC has been demonstrated in many clinical scenarios, initially from simply diagnosing csPC pre-biopsy, now to screening, active surveillance, clinical staging, and detection of recurrent disease. Continued efforts should be undertaken not only to emphasize the reporting of prostate MRI quality, but to standardize reporting according to the appropriate clinical setting.

Values of multiparametric and biparametric MRI in diagnosing clinically significant prostate cancer: a multivariate analysis

BACKGROUND

To investigate the value of semi-quantitative and quantitative parameters (PI-RADS score, T2WI score, ADC, Ktrans, and Kep) based on multiparametric MRI (mpMRI) or biparametric MRI (bpMRI) combined with prostate specific antigen density (PSAD) in detecting clinically significant prostate cancer (csPCa).

METHODS

A total of 561 patients (276 with csPCa; 285 with non-csPCa) with biopsy-confirmed prostate diseases who underwent preoperative mpMRI were included. Prostate volume was measured for calculation of PSAD. Prostate index lesions were scored on a five-point scale on T2WI images (T2WI score) and mpMRI images (PI-RADS score) according to the PI-RADS v2.1 scoring standard. DWI and DCE-MRI images were processed to measure the quantitative parameters of the index lesion, including ADC, Kep, and Ktrans values. The predictors of csPCa were screened by logistics regression analysis. Predictive models of bpMRI and mpMRI were established. ROC curves were used to evaluate the efficacy of parameters and the model in diagnosing csPCa.

RESULTS

The independent diagnostic accuracy of PSA density, PI-RADS score, T2WI score, ADCrec, Ktrans, and Kep for csPCa were 80.2%, 89.5%, 88.3%, 84.6%, 58.5% and 61.6%, respectively. The diagnostic accuracy of bpMRI T2WI score and ADC value combined with PSAD was higher than that of PI-RADS score. The combination of mpMRI PI‑RADS score, ADC value with PSAD had the highest diagnostic accuracy.

CONCLUSIONS

PI-RADS score according to the PI-RADS v2.1 scoring standard was the most accurate independent diagnostic index. The predictive value of bpMRI model for csPCa was slightly lower than that of mpMRI model, but higher than that of PI-RADS score.

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.

MRI and PSMA PET/CT of Biochemical Recurrence of Prostate Cancer

Prostate cancer may recur several years after definitive treatment, such as prostatectomy or radiation therapy. A rise in serum prostate-specific antigen (PSA) level is the first sign of disease recurrence, and this is termed biochemical recurrence. Patients with biochemical recurrence have worse survival outcomes. Radiologic localization of recurrent disease helps in directing patient management, which may vary from active surveillance to salvage radiation therapy, androgen-deprivation therapy, or other forms of systemic and local therapy. The likelihood of detecting the site of recurrence increases with higher serum PSA level. MRI provides optimal diagnostic performance for evaluation of the prostatectomy bed. Prostate-specific membrane antigen (PSMA) PET radiotracers currently approved by the U.S. Food and Drug Administration demonstrate physiologic urinary excretion, which can obscure recurrence at the vesicourethral junction. However, MRI and PSMA PET/CT have comparable diagnostic performance for evaluation of local recurrence after external-beam radiation therapy or brachytherapy. PSMA PET/CT outperforms MRI in identifying recurrence involving the lymph nodes and bones. Caveats for use of both PSMA PET/CT and MRI do exist and may cause false-positive or false-negative results. Hence, these techniques have complementary roles and should be interpreted in conjunction with each other, taking the patient history and results of any additional prior imaging studies into account. Novel PSMA agents at various stages of investigation are being developed, and preliminary data show promising results; these agents may revolutionize the landscape of prostate cancer recurrence imaging in the future. ©RSNA, 2023 Quiz questions for this article are available through the Online Learning Center. See the invited commentary by Turkbey in this issue. The slide presentation from the RSNA Annual Meeting is available for this article.

MRI for detection, staging, and follow-up of prostate cancer: Synthesis of the PI-RADS v2.1, MET-RADS, PRECISE, and PI-RR guidelines

Prostate cancer is very common among men. Radiology, mainly through MRI, plays a key role in the different stages of prostate cancer: diagnosis, staging and treatment assessment. The correct management of MRI requires knowledge and proper use of the different guidelines developed for the acquisition, interpretation and reporting of MRI in diagnosis (PI-RADS guide), whole body staging (MET-RADS guide), active surveillance (PRECISE guide) and local recurrence (PI-RR guide) in prostate cancer. The objective of this article is to show an update and synthesis of the most relevant aspects of these MRI guidelines for an optimal use and thus providing a more effective management of prostate cancer.

The Role of mpMRI in the Assessment of Prostate Cancer Recurrence Using the PI-RR System: Diagnostic Accuracy and Interobserver Agreement in Readers with Different Expertise

BACKGROUND

treated prostate cancer (PCa) patients develop biochemical recurrence (BCR) in 27-53% of cases; the role of MRI in this setting is still controversial. In 2021 a panel of experts proposed a "Prostate Imaging-Recurrence Reporting" (PI-RR) score, aiming to standardize the reporting. The aim of our study is to evaluate the reproducibility of the PI-RR scoring system among readers with different expertise.

METHODS

in this monocentric, retrospective observational study, the images of patients who underwent MRI with BCR from January 2017 to January 2022 were analyzed by two radiologists and a radiology resident. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy were obtained. Interobserver agreement was calculated. The percentage of the PI-RR score of 3 was estimated to find out the proportion of uncertain exams reported among the readers.

RESULTS

a total of seventy-six patients were included in our study: eight previously treated with RT and sixty-eight who underwent surgery. The accuracy range was 75-80%, the sensitivity 68.4-71.1%, the specificity 81.6-89.5%, PPV 78.8-87.1%, and NPV 72.1-75.6%. The inter-reader agreement using a binary evaluation (PI-RR ≥ 3 as positive mpMRI) demonstrated a correlation coefficient (k) of 0.74 (95% CI: 0.62-0.87). The percentage for the PI-RR score of 3 was 6.6% for reader one, 14.5% for reader two, and 2.6% for reader three.

CONCLUSION

this study confirmed the good accuracy of mpMRI in the detection of local recurrence of PCa and the good reproducibility of PI-RR score among all readers, confirming it to be a promising tool for the standardization of the assessment of patients with BCR.