A Grading System for the Assessment of Risk of Extraprostatic Extension of Prostate Cancer at Multiparametric MRI

External Validation of a Previously Developed Deep Learning-based Prostate Lesion Detection Algorithm on Paired External and In-House Biparametric MRI Scans

Purpose To evaluate the performance of an artificial intelligence (AI) model in detecting overall and clinically significant prostate cancer (csPCa)-positive lesions on paired external and in-house biparametric MRI (bpMRI) scans and assess performance differences between each dataset. Materials and Methods This single-center retrospective study included patients who underwent prostate MRI at an external institution and were rescanned at the authors' institution between May 2015 and May 2022. A genitourinary radiologist performed prospective readouts on in-house MRI scans following the Prostate Imaging Reporting and Data System (PI-RADS) version 2.0 or 2.1 and retrospective image quality assessments for all scans. A subgroup of patients underwent an MRI/US fusion-guided biopsy. A bpMRI-based lesion detection AI model previously developed using a completely separate dataset was tested on both MRI datasets. Detection rates were compared between external and in-house datasets with use of the paired comparison permutation tests. Factors associated with AI detection performance were assessed using multivariable generalized mixed-effects models, incorporating features selected through forward stepwise regression based on the Akaike information criterion. Results The study included 201 male patients (median age, 66 years [IQR, 62-70 years]; prostate-specific antigen density, 0.14 ng/mL2 [IQR, 0.10-0.22 ng/mL2]) with a median interval between external and in-house MRI scans of 182 days (IQR, 97-383 days). For intraprostatic lesions, AI detected 39.7% (149 of 375) on external and 56.0% (210 of 375) on in-house MRI scans (P < .001). For csPCa-positive lesions, AI detected 61% (54 of 89) on external and 79% (70 of 89) on in-house MRI scans (P < .001). On external MRI scans, better overall lesion detection was associated with a higher PI-RADS score (odds ratio [OR] = 1.57; P = .005), larger lesion diameter (OR = 3.96; P < .001), better diffusion-weighted MRI quality (OR = 1.53; P = .02), and fewer lesions at MRI (OR = 0.78; P = .045). Better csPCa detection was associated with a shorter MRI interval between external and in-house scans (OR = 0.58; P = .03) and larger lesion size (OR = 10.19; P < .001). Conclusion The AI model exhibited modest performance in identifying both overall and csPCa-positive lesions on external bpMRI scans. Keywords: MR Imaging, Urinary, Prostate Supplemental material is available for this article. © RSNA, 2024.

Target Volume Optimization for Localized Prostate Cancer

PURPOSE

To provide a comprehensive review of the means by which to optimize target volume definition for the purposes of treatment planning for patients with intact prostate cancer with a specific emphasis on focal boost volume definition.

METHODS

Here we conduct a narrative review of the available literature summarizing the current state of knowledge on optimizing target volume definition for the treatment of localized prostate cancer.

RESULTS

Historically, the treatment of prostate cancer included a uniform prescription dose administered to the entire prostate with or without coverage of all or part of the seminal vesicles. The development of prostate magnetic resonance imaging (MRI) and positron emission tomography (PET) using prostate-specific radiotracers has ushered in an era in which radiation oncologists are able to localize and focally dose-escalate high-risk volumes in the prostate gland. Recent phase 3 data has demonstrated that incorporating focal dose escalation to high-risk subvolumes of the prostate improves biochemical control without significantly increasing toxicity. Still, several fundamental questions remain regarding the optimal target volume definition and prescription strategy to implement this technique. Given the remaining uncertainty, a knowledge of the pathological correlates of radiographic findings and the anatomic patterns of tumor spread may help inform clinical judgement for the definition of clinical target volumes.

CONCLUSION

Advanced imaging has the ability to improve outcomes for patients with prostate cancer in multiple ways, including by enabling focal dose escalation to high-risk subvolumes. However, many questions remain regarding the optimal target volume definition and prescription strategy to implement this practice, and key knowledge gaps remain. A detailed understanding of the pathological correlates of radiographic findings and the patterns of local tumor spread may help inform clinical judgement for target volume definition given the current state of uncertainty.

ESR Essentials: using the right scoring system in prostate MRI-practice recommendations by ESUR

MRI has gained prominence in the diagnostic workup of prostate cancer (PCa) patients, with the Prostate Imaging Reporting and Data System (PI-RADS) being widely used for cancer detection. Beyond PI-RADS, other MRI-based scoring tools have emerged to address broader aspects within the PCa domain. However, the multitude of available MRI-based grading systems has led to inconsistencies in their application within clinical workflows. The Prostate Cancer Radiological Estimation of Change in Sequential Evaluation (PRECISE) assesses the likelihood of clinically significant radiological changes of PCa during active surveillance, and the Prostate Imaging for Local Recurrence Reporting (PI-RR) scoring system evaluates the risk of local recurrence after whole-gland therapies with curative intent. Underlying any system is the requirement to assess image quality using the Prostate Imaging Quality Scoring System (PI-QUAL). This article offers practicing radiologists a comprehensive overview of currently available scoring systems with clinical evidence supporting their use for managing PCa patients to enhance consistency in interpretation and facilitate effective communication with referring clinicians. KEY POINTS: Assessing image quality is essential for all prostate MRI interpretations and the PI-QUAL score represents  the standardized tool for this purpose. Current urological clinical guidelines for prostate cancer diagnosis and localization recommend adhering to the PI-RADS recommendations. The PRECISE and PI-RR scoring systems can be used for assessing radiological changes of prostate cancer during active surveillance and the likelihood of local recurrence after radical treatments respectively.

Accuracy of MRI in detecting seminal vesicle invasion in prostate cancer: a systematic review and meta-analysis

OBJECTIVE

To determine the diagnostic test accuracy of multiparametric magnetic resonance imaging (mpMRI) in detecting seminal vesicle invasion (SVI).

METHODS

The Medical Literature Analysis and Retrieval System Online (MEDLINE), PubMed, the Excerpta Medica dataBASE (EMBASE) and Cochrane databases were search up to May 2023. We included studies that investigated the accuracy of mpMRI in detecting SVI when compared to radical prostatectomy specimens as the reference standard. Data extraction was performed by two independent reviewers to construct 2 × 2 tables, as well as patient and study characteristics. The methodological quality of the included studies was assessed with the Quality of Assessment of Diagnostic Accuracy Studies-2 tool. Sensitivity and specificity were pooled and presented graphically with summary receiver operator characteristic (SROC) plots.

RESULTS

A total of 27 articles with 4862 patients were included for analysis. The summary sensitivity and specificity were 0.57 (95% confidence interval [CI] 0.45-0.68) and 0.95 (95% CI 0.92-0.99), respectively. Meta-regression indicated that there was no evidence that coil strength (P = 0.079), coil type (P = 0.589), year of publication (P = 0.503) or use of the Prostate Imaging-Reporting and Data System (P = 0.873) significantly influenced these results. The summary diagnostic odds ratio was 28.3 (95% CI 15.0-48.8) and the area under the curve for the SROC curve was 0.87. The I2 statistic was a modest 11.9%. In general, methodological quality was good.

CONCLUSION

The use of mpMRI in detecting SVI has excellent specificity but poor sensitivity. Both endorectal coils and magnetic field strength do not significantly impact the accuracy of MRI. These findings suggest that mpMRI cannot reliably rule out SVI in patients with prostate cancer.

Automated Detection and Grading of Extraprostatic Extension of Prostate Cancer at MRI via Cascaded Deep Learning and Random Forest Classification

RATIONALE AND OBJECTIVES

Extraprostatic extension (EPE) is well established as a significant predictor of prostate cancer aggression and recurrence. Accurate EPE assessment prior to radical prostatectomy can impact surgical approach. We aimed to utilize a deep learning-based AI workflow for automated EPE grading from prostate T2W MRI, ADC map, and High B DWI.

MATERIAL AND METHODS

An expert genitourinary radiologist conducted prospective clinical assessments of MRI scans for 634 patients and assigned risk for EPE using a grading technique. The training set and held-out independent test set consisted of 507 patients and 127 patients, respectively. Existing deep-learning AI models for prostate organ and lesion segmentation were leveraged to extract area and distance features for random forest classification models. Model performance was evaluated using balanced accuracy, ROC AUCs for each EPE grade, as well as sensitivity, specificity, and accuracy compared to EPE on histopathology.

RESULTS

A balanced accuracy score of .390 ± 0.078 was achieved using a lesion detection probability threshold of 0.45 and distance features. Using the test set, ROC AUCs for AI-assigned EPE grades 0-3 were 0.70, 0.65, 0.68, and 0.55 respectively. When using EPE≥ 1 as the threshold for positive EPE, the model achieved a sensitivity of 0.67, specificity of 0.73, and accuracy of 0.72 compared to radiologist sensitivity of 0.81, specificity of 0.62, and accuracy of 0.66 using histopathology as the ground truth.

CONCLUSION

Our AI workflow for assigning imaging-based EPE grades achieves an accuracy for predicting histologic EPE approaching that of physicians. This automated workflow has the potential to enhance physician decision-making for assessing the risk of EPE in patients undergoing treatment for prostate cancer due to its consistency and automation.

AI-derived Tumor Volume from Multiparametric MRI and Outcomes in Localized Prostate Cancer

Background An artificial intelligence (AI)-based method for measuring intraprostatic tumor volume based on data from MRI may provide prognostic information. Purpose To evaluate whether the total volume of intraprostatic tumor from AI-generated segmentations (VAI) provides independent prognostic information in patients with localized prostate cancer treated with radiation therapy (RT) or radical prostatectomy (RP). Materials and Methods For this retrospective, single-center study (January 2021 to August 2023), patients with cT1-3N0M0 prostate cancer who underwent MRI and were treated with RT or RP were identified. Patients who underwent RT were randomly divided into cross-validation and test RT groups. An AI segmentation algorithm was trained to delineate Prostate Imaging Reporting and Data System (PI-RADS) 3-5 lesions in the cross-validation RT group before providing segmentations for the test RT and RP groups. Cox regression models were used to evaluate the association between VAI and time to metastasis and adjusted for clinical and radiologic factors for combined RT (ie, cross-validation RT and test RT) and RP groups. Areas under the receiver operating characteristic curve (AUCs) were calculated for VAI and National Comprehensive Cancer Network (NCCN) risk categorization for prediction of 5-year metastasis (RP group) and 7-year metastasis (combined RT group). Results Overall, 732 patients were included (combined RT group, 438 patients; RP group, 294 patients). Median ages were 68 years (IQR, 62-73 years) and 61 years (IQR, 56-66 years) for the combined RT group and the RP group, respectively. VAI was associated with metastasis in the combined RT group (median follow-up, 6.9 years; adjusted hazard ratio [AHR], 1.09 per milliliter increase; 95% CI: 1.04, 1.15; P = .001) and the RP group (median follow-up, 5.5 years; AHR, 1.22; 95% CI: 1.08, 1.39; P = .001). AUCs for 7-year metastasis for the combined RT group for VAI and NCCN risk category were 0.84 (95% CI: 0.74, 0.94) and 0.74 (95% CI: 0.80, 0.98), respectively (P = .02). Five-year AUCs for the RP group for VAI and NCCN risk category were 0.89 (95% CI: 0.80, 0.98) and 0.79 (95% CI: 0.64, 0.94), respectively (P = .25). Conclusion The volume of AI-segmented lesions was an independent, prognostic factor for localized prostate cancer. © RSNA, 2024 Supplemental material is available for this article.

Head-to-head comparison of GA-68 PSMA PET/CT and multiparametric MRI findings with postoperative results in preoperative locoregional staging and localization of prostate cancer

BACKGROUND

Accurate staging of prostate cancer (PCa) is essential for determining the appropriate treatment and predicting outcomes. This study is comparing the effectiveness of Gallium-68 Prostate-Specific Membrane Antigen Positron Emission Tomography/Computed Tomography (Ga-68 PSMA PET/CT) and multiparametric MRI (mpMRI) in preoperative locoregional staging and localizing PCa.

METHODS

A retrospective analysis was conducted on 78 patients who underwent both mpMRI and Ga-68 PSMA PET/CT scans before surgery. The imaging was reviewed by radiologists and nuclear medicine specialists and compared with the final histopathology, which was reviewed by an experienced uropathologist.

RESULTS

mpMRI demonstrated higher sensitivity in detecting extraprostatic extension (EPE) and bladder neck invasion (BNI) compared to Ga-68 PSMA PET/CT (83% vs. 44% and 29% vs. 17%, respectively). Conversely, Ga-68 PSMA PET/CT showed higher sensitivity in detecting seminal vesicle invasion (SVI) and lymph node metastasis (LNM) (75% vs. 55% and 50% vs. 30%, respectively). When both methods were combined, sensitivity increased in detecting both EPE and SVI. The index tumor localization in mpMRI and Ga-68 PSMA PET/CT was found to be in complete agreement with histopathological findings at 36.4% and 41.8%, respectively. When both imaging methods were combined, the agreement with histopathology in predicting index tumor localization reached 72.1%.

CONCLUSION

Both mpMRI and Ga-68 PSMA PET/CT provide valuable and complementary information for tumor localization and locoregional staging. While mpMRI showed higher sensitivity in detecting EPE, Ga-68 PSMA PET/CT demonstrated superior performance in detecting LNM and SVI. The combined use of these imaging modalities enhance accuracy of index tumor localizations.

A comparative analysis of techniques for measuring tumor contact length in predicting extraprostatic extension

PURPOSE

This study aims to evaluate the diagnostic performance of curvilinear and linear measurement methods in different magnetic resonance imaging (MRI) sequences for detecting extraprostatic extension (EPE) in prostate cancer, and to evaluate the added value of apparent diffusion coefficient (ADC) in detecting EPE.

METHODS

A retrospective analysis was conducted on 84 patients who underwent multiparametric MRI (mp-MRI) prior to radical prostatectomy between January 2019 and February 2022. Tumor contact length (TCL) was assessed curvilinearly and linearly on T2-weighted imaging (T2WI), ADC maps, and dynamic contrast-enhanced (DCE) MRI by two radiologists. MRI-based EPE positivity was defined as a curvilinear or linear contact length of >15 mm. Statistical comparisons were conducted using chi-squared and independent samples t-tests, with interreader agreement evaluated using weighted κ statistics. Univariate and multivariate logistic regression identified independent predictors of EPE, and two prediction models were constructed. Diagnostic performance was assessed using receiver operator characteristic (ROC) curve analysis.

RESULTS

A total of 32 (38%) and 52 (62%) patients with EPE and non-EPE, respectively, were included in this study. Patients with EPE demonstrated significantly larger tumor sizes, lower ADC values, and lower ADC ratios than those without EPE (p < 0.001). The curvilinear and linear TCL measurements for each sequence exhibited statistically significant correlations with EPE for both readers, with strong interreader agreement. Curvilinear TCL (c-TCL) and linear TCL (l-TCL) on DCE-MRI showed higher area under the curve (AUC) values than the other measurements for EPE prediction (reader 1: 0.815 and 0.803, reader 2: 0.746 and 0.713, respectively). However, there was no statistically significant difference between c-TCL and l-TCL. Multivariable models with mean ADC value improved predictive performance. Model 2 (ADC, ISUP, and c-TCL on DCE images) surpassed model 1 (ADC and c-TCL on DCE images) with an AUC of 0.919 and 0.874, respectively.

CONCLUSION

DCE-MRI demonstrated superior performance in predicting EPE compared to other sequences. Linear and curvilinear measurements had comparable diagnostic performance. Being more practical and easier, radiologists may use l-TCL measurement in daily practice. The mean ADC value provided additional diagnostic value.

A pictorial essay of PI-RADS pearls and pitfalls: toward less ambiguity and better practice

Prostate Imaging Reporting and Data System (PI-RADS) was designed to standardize the interpretation of multiparametric magnetic resonance imaging (MRI) of the prostate, aiding in assessing the probability of clinically significant prostate cancer. By providing a structured scoring system, it enables better risk stratification, guiding decisions regarding the need for biopsy and subsequent treatment options. In this article, we explore both the strengths and weaknesses of PI-RADS, offering insights into its updated diagnostic performance and clinical applications, while also addressing potential pitfalls using diverse, representative MRI cases.

Prediction of extracapsular extension of prostate cancer by MRI radiomic signature: a systematic review

The objective of this review is to survey radiomics signatures for detecting pathological extracapsular extension (pECE) on magnetic resonance imaging (MRI) in patients with prostate cancer (PCa) who underwent prostatectomy. Scientific Literature databases were used to search studies published from January 2007 to October 2023. All studies related to PCa MRI staging and using radiomics signatures to detect pECE after prostatectomy were included. Systematic review was performed according to Preferred Reporting Items for Systematic Review and Meta-analyses (PRISMA). The risk of bias and certainty of the evidence was assessed using QUADAS-2 and the radiomics quality score. From 1247 article titles screened, 16 reports were assessed for eligibility, and 11 studies were included in this systematic review. All used a retrospective study design and most of them used 3 T MRI. Only two studies were performed in more than one institution. The highest AUC of a model using only radiomics features was 0.85, for the test validation. The AUC for best model performance (radiomics associated with clinical/semantic features) varied from 0.72-0.92 and 0.69-0.89 for the training and validation group, respectively. Combined models performed better than radiomics signatures alone for detecting ECE. Most of the studies showed a low to medium risk of bias. After thorough analysis, we found no strong evidence supporting the clinical use of radiomics signatures for identifying extracapsular extension (ECE) in pre-surgery PCa patients. Future studies should adopt prospective multicentre approaches using large public datasets and combined models for detecting ECE.

CRITICAL RELEVANT STATEMENT

The use of radiomics algorithms, with clinical and AI integration, in predicting extracapsular extension, could lead to the development of more accurate predictive models, which could help improve surgical planning and lead to better outcomes for prostate cancer patients.

PROTOCOL OF SYSTEMATIC REVIEW REGISTRATION

PROSPERO CRD42021272088. Published: https://doi.org/10.1136/bmjopen-2021-052342 .

KEY POINTS

Radiomics can extract diagnostic features from MRI to enhance prostate cancer diagnosis performance. The combined models performed better than radiomics signatures alone for detecting extracapsular extension. Radiomics are not yet reliable for extracapsular detection in PCa patients.

Select black men are potential candidates for prostate hemi-ablation based on radical prostatectomy histopathology for intermediate-risk prostate cancer-a multicenter SEARCH cohort study

IMPORTANCE AND OBJECTIVE

Partial gland ablation (PGA) is increasingly popular as a treatment for men with intermediate-risk prostate cancer (IR-PCa) to preserve functional outcomes while controlling their cancer. We aimed to determine the impact of race and clinical characteristics on the risk of upstaging (≥pT2c) and having adverse pathological outcomes including seminal vesicle invasion (SVI), extra prostatic extension (EPE) and lymph node invasion (LNI) at radical prostatectomy (RP) among men with IR disease eligible for PGA with hemi-ablation (HA).

DESIGN

Retrospective analysis.

SETTING

Multicenter.

PARTICIPANTS AND MEASURES

We studied patients diagnosed with unilateral IR-PCa treated with RP between 1988 and 2020 at 9 different Veterans Affairs hospitals within the SEARCH cohort. We analyzed differences in clinicopathological characteristics and outcome variables (odds of ≥pT2c and SVI, EPE and LNI) by race using multivariable logistic regression after adjusting for covariates.

RESULTS

Among 3127 patients, 33% were African American (AA) men with unilateral IR-PCa undergoing RP. Compared to non-AA men, AA individuals were younger (61 vs. 65 years, p < 0.001), presented with a higher prostate specific antigen (PSA) category (≥10 ng/ml; 34 vs. 26%, p < 0.001), and had a lower clinical stage (p < 0.001). Among the 2,798 (89.5%) with ≥pT2c stage, AA men exhibited higher ≥ pT2c rates (93 vs. 89%, p < 0.001), primarily due to increased pT2c staging (64 vs. 57%), where upstaging beyond pT2 was lower than non-AA men (29 vs. 32%). On multivariable analysis, AA men were found to have higher odds of ≥pT2c (odds ratio [OR]: 1.39 CI, 1.02-1.88, p = 0.04), lower odds of EPE (OR: 0.73 CI, 0.58-0.91, p < 0.01) and no statistically significant associations with LNI (OR: 0.79 CI, 0.42-1.46, p = 0.45) and SVI (OR: 1 CI, 0.74-1.35, p = 0.99) compared to non-AA men. On multivariable analysis, clinical features associated with higher odds of ≥pT2c were pre-operative PSA ≥ 15 (OR = 2.07, P = 0.01) and higher number of positive cores (HPC) on biopsy (OR = 1.36, P < 0.001). Similarly, PSA ≥ 15, Gleason grade ≥3 and HPC on biopsy were associated with higher odds of SVI, EPE and LNI, respectively.

CONCLUSIONS

In men with IR-PCa undergoing RP, AA men demonstrated an overall higher likelihood of ≥pT2c with lower upstaging beyond pT2, lower likelihood of EPE and no significant difference in likelihood of SVI and LNI compared to non-AA men. These findings support select AA men to be potential candidates for PGA, such as HA. Clinical factors are predictive of higher pathological stage and adverse pathological outcomes at RP and could be considered when selecting candidates for PGA.

Development of preoperative nomograms to predict the risk of overall and multifocal positive surgical margin after radical prostatectomy

OBJECTIVE

To develop preoperative nomograms using risk factors based on clinicopathological and MRI for predicting the risk of positive surgical margin (PSM) after radical prostatectomy (RP).

PATIENTS AND METHODS

This study retrospectively enrolled patients who underwent prostate MRI before RP at our center between January 2015 and November 2022. Preoperative clinicopathological factors and MRI-based features were recorded for analysis. The presence of PSM (overall PSM [oPSM]) at pathology and the multifocality of PSM (mPSM) were evaluated. LASSO regression was employed for variable selection. For the final model construction, logistic regression was applied combined with the bootstrap method for internal verification. The risk probability of individual patients was visualized using a nomogram.

RESULTS

In all, 259 patients were included in this study, and 76 (29.3%) patients had PSM, including 40 patients with mPSM. Final multivariate logistic regression revealed that the independent risk factors for oPSM were tumor diameter, frank extraprostatic extension, and annual surgery volume (all p < 0.05), and the nomogram for oPSM reached an area under the curve (AUC) of 0.717 in development and 0.716 in internal verification. The independent risk factors for mPSM included the percentage of positive cores, tumor diameter, apex depth, and annual surgery volume (all p < 0.05), and the AUC of the nomogram for mPSM was 0.790 in both development and internal verification. The calibration curve analysis showed that these nomograms were well-calibrated for both oPSM and mPSM.

CONCLUSIONS

The proposed nomograms showed good performance and were feasible in predicting oPSM and mPSM, which might facilitate more individualized management of prostate cancer patients who are candidates for surgery.

Deep learning-based image quality assessment: impact on detection accuracy of prostate cancer extraprostatic extension on MRI

OBJECTIVE

To assess impact of image quality on prostate cancer extraprostatic extension (EPE) detection on MRI using a deep learning-based AI algorithm.

MATERIALS AND METHODS

This retrospective, single institution study included patients who were imaged with mpMRI and subsequently underwent radical prostatectomy from June 2007 to August 2022. One genitourinary radiologist prospectively evaluated each patient using the NCI EPE grading system. Each T2WI was classified as low- or high-quality by a previously developed AI algorithm. Fisher's exact tests were performed to compare EPE detection metrics between low- and high-quality images. Univariable and multivariable analyses were conducted to assess the predictive value of image quality for pathological EPE.

RESULTS

A total of 773 consecutive patients (median age 61 [IQR 56-67] years) were evaluated. At radical prostatectomy, 23% (180/773) of patients had EPE at pathology, and 41% (131/318) of positive EPE calls on mpMRI were confirmed to have EPE. The AI algorithm classified 36% (280/773) of T2WIs as low-quality and 64% (493/773) as high-quality. For EPE grade ≥ 1, high-quality T2WI significantly improved specificity for EPE detection (72% [95% CI 67-76%] vs. 63% [95% CI 56-69%], P = 0.03), but did not significantly affect sensitivity (72% [95% CI 62-80%] vs. 75% [95% CI 63-85%]), positive predictive value (44% [95% CI 39-49%] vs. 38% [95% CI 32-43%]), or negative predictive value (89% [95% CI 86-92%] vs. 89% [95% CI 85-93%]). Sensitivity, specificity, PPV, and NPV for EPE grades ≥ 2 and ≥ 3 did not show significant differences attributable to imaging quality. For NCI EPE grade 1, high-quality images (OR 3.05, 95% CI 1.54-5.86; P < 0.001) demonstrated a stronger association with pathologic EPE than low-quality images (OR 1.76, 95% CI 0.63-4.24; P = 0.24).

CONCLUSION

Our study successfully employed a deep learning-based AI algorithm to classify image quality of prostate MRI and demonstrated that better quality T2WI was associated with more accurate prediction of EPE at final pathology.

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.

Comparing Magnetic Resonance Imaging and Prostate-Specific Membrane Antigen-Positron Emission Tomography for Prediction of Extraprostatic Extension of Prostate Cancer and Surgical Guidance: A Prospective Nonrandomized Clinical Trial

PURPOSE

Survivors of surgically managed prostate cancer may experience urinary incontinence and erectile dysfunction. Our aim was to determine if 68Ga-prostate-specific membrane antigen-11 positron emission tomography CT (PSMA-PET) in addition to multiparametric (mp) MRI scans improved surgical decision-making for nonnerve-sparing or nerve-sparing approach.

MATERIALS AND METHODS

We prospectively enrolled 50 patients at risk for extraprostatic extension (EPE) who were scheduled for prostatectomy. After mpMRI and PSMA-PET images were read for EPE prediction, surgeons prospectively answered questionnaires based on mpMRI and PSMA-PET scans on the decision for nerve-sparing or nonnerve-sparing approach. Final whole-mount pathology was the reference standard. Sensitivity, specificity, positive predictive value, negative predictive value, and receiver operating characteristic curves were calculated and McNemar's test was used to compare imaging modalities.

RESULTS

The median age and PSA were 61.5 years and 7.0 ng/dL. The sensitivity for EPE along the posterior neurovascular bundle was higher for PSMA-PET than mpMRI (86% vs 57%, P = .03). For MRI, the specificity, positive predictive value, negative predictive value, and area under the curve for the receiver operating characteristic curves were 77%, 40%, 87%, and 0.67, and for PSMA-PET were 73%, 46%, 95%, and 0.80. PSMA-PET and mpMRI reads differed on 27 nerve bundles, with PSMA-PET being correct in 20 cases and MRI being correct in 7 cases. Surgeons predicted correct nerve-sparing approach 74% of the time with PSMA-PET scan in addition to mpMRI compared to 65% with mpMRI alone (P = .01).

CONCLUSIONS

PSMA-PET scan was more sensitive than mpMRI for EPE along the neurovascular bundles and improved surgical decisions for nerve-sparing approach. Further study of PSMA-PET for surgical guidance is warranted in the unfavorable intermediate-risk or worse populations.

CLINICALTRIALS.GOV IDENTIFIER

NCT04936334.

A preoperative scoring system for predicting the extraprostatic extension of prostate cancer following radical prostatectomy using magnetic resonance imaging and clinical factors

PURPOSE

We aimed to develop a preoperative prediction model for extraprostatic extension (EPE) in prostate cancer (PCa) patients following radical prostatectomy (RP) using MRI and clinical factors.

METHODS

This retrospective study enrolled 266 consecutive patients who underwent RP for PCa in 2022. These patients were divided into a training set (n = 187) and a test set (n = 79) through random assignment. The evaluated variables included age, prostate-specific antigen (PSA) level, prostate volume, PSA density (PSAD), index tumor length on MRI, Prostate Imaging-Reporting and Data System (PI-RADS) category, and EPE-related MRI features as defined by PI-RADS v2.1. A predictive model was constructed through multivariable logistic regression and subsequently translated into a scoring system. The performance of this scoring system in terms of prediction and calibration was assessed using C statistics and the Hosmer‒Lemeshow test.

RESULTS

Among patients in the training and test cohorts, 74 (39.6%) and 25 (31.6%), respectively, exhibited EPE after RP. The formulated scoring system incorporated the following factors: PSAD, index tumor length, bulging prostatic contour, and tumor-capsule interface > 10 mm as identified on MRI. This scoring system demonstrated strong prediction performance for EPE in both the training (C statistic, 0.87 [95% confidence interval, 0.86-0.87]) and test cohorts (C statistic, 0.85 [0.83-0.89]). Furthermore, the scoring system exhibited good calibration in both cohorts (P = 0.988 and 0.402, respectively).

CONCLUSION

Our scoring system, built upon MRI features defined by the PI-RADS, offers valuable assistance in assessing the likelihood of EPE after RP.

Radiomics for the identification of extraprostatic extension with prostate MRI: a systematic review and meta-analysis

OBJECTIVES

Extraprostatic extension (EPE) of prostate cancer (PCa) is predicted using clinical nomograms. Incorporating MRI could represent a leap forward, although poor sensitivity and standardization represent unsolved issues. MRI radiomics has been proposed for EPE prediction. The aim of the study was to systematically review the literature and perform a meta-analysis of MRI-based radiomics approaches for EPE prediction.

MATERIALS AND METHODS

Multiple databases were systematically searched for radiomics studies on EPE detection up to June 2022. Methodological quality was appraised according to Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool and radiomics quality score (RQS). The area under the receiver operating characteristic curves (AUC) was pooled to estimate predictive accuracy. A random-effects model estimated overall effect size. Statistical heterogeneity was assessed with I2 value. Publication bias was evaluated with a funnel plot. Subgroup analyses were performed to explore heterogeneity.

RESULTS

Thirteen studies were included, showing limitations in study design and methodological quality (median RQS 10/36), with high statistical heterogeneity. Pooled AUC for EPE identification was 0.80. In subgroup analysis, test-set and cross-validation-based studies had pooled AUC of 0.85 and 0.89 respectively. Pooled AUC was 0.72 for deep learning (DL)-based and 0.82 for handcrafted radiomics studies and 0.79 and 0.83 for studies with multiple and single scanner data, respectively. Finally, models with the best predictive performance obtained using radiomics features showed pooled AUC of 0.82, while those including clinical data of 0.76.

CONCLUSION

MRI radiomics-powered models to identify EPE in PCa showed a promising predictive performance overall. However, methodologically robust, clinically driven research evaluating their diagnostic and therapeutic impact is still needed.

CLINICAL RELEVANCE STATEMENT

Radiomics might improve the management of prostate cancer patients increasing the value of MRI in the assessment of extraprostatic extension. However, it is imperative that forthcoming research prioritizes confirmation studies and a stronger clinical orientation to solidify these advancements.

KEY POINTS

• MRI radiomics deserves attention as a tool to overcome the limitations of MRI in prostate cancer local staging. • Pooled AUC was 0.80 for the 13 included studies, with high heterogeneity (84.7%, p < .001), methodological issues, and poor clinical orientation. • Methodologically robust radiomics research needs to focus on increasing MRI sensitivity and bringing added value to clinical nomograms at patient level.

Detecting Adverse Pathology of Prostate Cancer With a Deep Learning Approach Based on a 3D Swin-Transformer Model and Biparametric MRI: A Multicenter Retrospective Study

BACKGROUND

Accurately detecting adverse pathology (AP) presence in prostate cancer patients is important for personalized clinical decision-making. Radiologists' assessment based on clinical characteristics showed poor performance for detecting AP presence.

PURPOSE

To develop deep learning models for detecting AP presence, and to compare the performance of these models with those of a clinical model (CM) and radiologists' interpretation (RI).

STUDY TYPE

Retrospective.

POPULATION

Totally, 616 men from six institutions who underwent radical prostatectomy, were divided into a training cohort (508 patients from five institutions) and an external validation cohort (108 patients from one institution).

FIELD STRENGTH/SEQUENCES

T2-weighted imaging with a turbo spin echo sequence and diffusion-weighted imaging with a single-shot echo plane-imaging sequence at 3.0 T.

ASSESSMENT

The reference standard for AP was histopathological extracapsular extension, seminal vesicle invasion, or positive surgical margins. A deep learning model based on the Swin-Transformer network (TransNet) was developed for detecting AP. An integrated model was also developed, which combined TransNet signature with clinical characteristics (TransCL). The clinical characteristics included biopsy Gleason grade group, Prostate Imaging Reporting and Data System scores, prostate-specific antigen, ADC value, and the lesion maximum cross-sectional diameter.

STATISTICAL TESTS

Model and radiologists' performance were assessed using area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. The Delong test was used to evaluate difference in AUC. P < 0.05 was considered significant.

RESULTS

The AUC of TransCL for detecting AP presence was 0.813 (95% CI, 0.726-0.882), which was higher than that of TransNet (0.791 [95% CI, 0.702-0.863], P = 0.429), and significantly higher than those of CM (0.749 [95% CI, 0.656-0.827]) and RI (0.664 [95% CI, 0.566-0.752]).

DATA CONCLUSION

TransNet and TransCL have potential to aid in detecting the presence of AP and some single adverse pathologic features.

LEVEL OF EVIDENCE

4 TECHNICAL EFFICACY: Stage 4.

Histogram analysis of MR quantitative parameters: are they correlated with prognostic factors in prostate cancer?

PURPOSE

To investigate the correlation between quantitative MR parameters and prognostic factors in prostate cancer (PCa).

METHOD

A total of 186 patients with pathologically confirmed PCa who underwent preoperative multiparametric MRI (mpMRI), including synthetic MRI (SyMRI), were enrolled from two medical centers. The histogram metrics of SyMRI [T1, T2, proton density (PD)] and apparent diffusion coefficient (ADC) values were extracted. The Mann‒Whitney U test or Student's t test was employed to determine the association between these histogram features and the prognostically relevant factors. Receiver operating characteristic (ROC) curve analysis was conducted to evaluate the differentiation performance. Spearman's rank correlation coefficients were calculated to determine the correlations between histogram parameters and the International Society of Urological Pathology (ISUP) grade group as well as pathological T stage.

RESULTS

Significant correlations were found between the histogram parameters and the ISUP grade as well as pathological T stage of PCa. Among these histogram parameters, ADC_minimum had the strongest correlation with the ISUP grade (r = - 0.481, p < 0.001), and ADC_Median showed the strongest association with pathological T stage (r = - 0.285, p = 0.008). The ADC_10th percentile exhibited the highest performance in identifying clinically significant prostate cancer (csPCa) (AUC 0.833; 95% CI 0.771-0.883). When discriminating between the status of different prognostically relevant factors, a significant difference was observed between extraprostatic extension-positive and -negative cancers with regard to histogram parameters of the ADC map (10th percentile, 90th percentile, mean, median, minimum) and T1 map (minimum) (p = 0.002-0.032). Moreover, histogram parameters of the ADC map (90th percentile, maximum, mean, median), T2 map (10th percentile, median), and PD map (10th percentile, median) were significantly lower in PCa with perineural invasion (p = 0.009-0.049). The T2 values were significantly lower in patients with seminal vesicle invasion (minimum, p = 0.036) and positive surgical margin (10th percentile, 90th percentile, mean, median, and minimum, p = 0.015-0.025).

CONCLUSION

Quantitative histogram parameters derived from synthetic MRI and ADC maps may have great potential for predicting the prognostic features of PCa.

Histological parameters and stromal desmoplastic status affecting accurate diagnosis of extraprostatic extension of prostate cancer using multi-parametric magnetic resonance imaging

OBJECTIVE

To investigate the clinicopathological factors affecting discrepancies between multi-parametric magnetic resonance imaging (mpMRI) and histopathological evaluation for diagnosis of extraprostatic extension (EPE) of prostate cancer.

METHODS

One hundred-and-three lesions from 96 cases with suspected EPE on preoperative mpMRI, of which 60 and 43 showed bulging and frank capsular breach, respectively, were grouped according to pathological (p)EPE in radical prostatectomy specimens. Additionally, clinicopathological/immunohistochemical findings for periostin reflecting a desmoplastic stromal reaction were compared between these groups.

RESULTS

pEPE was detected in 49 (48%) of the 103 lesions. Of these, 25 (42%) showed bulging and 24 (56%) showed frank capsular breach on MRI. In the total cohort, the absence of pEPE was significantly associated with a lower Gleason Grade Group (GG) (p < 0.0001), anterior location (p = 0.003), absence of intraductal carcinoma of the prostate (IDC-P) (p = 0.026), and high stromal periostin expression (p < 0.0001). These trends were preserved in subgroups defined by MRI findings, except for anterior location/IDC-P in the bulging subgroup.

CONCLUSIONS

GG, anterior location, and periostin expression may cause mpMRI-pathological discrepancies regarding EPE. Periostin expression was a significant pEPE-negative factor in all subgroup analyses. Our results indicate that patients with suspected EPE on MRI, regardless of their pEPE results, should be followed as carefully as those with definite pEPE.

MRI-based radiomics for prediction of extraprostatic extension of prostate cancer: a systematic review and meta-analysis

PURPOSE

We to systematically evaluate the diagnostic performance of MRI radiomics in detecting extracapsular extension (EPE) of prostate cancer (PCa).

METHODS

A literature search of online databases of PubMed, EMBASE, Cochrane Library, Web of Science, and Google Scholar online scientific publication databases was performed to identify studies published up to July 2023. The summary estimates were pooled with the hierarchical summary receiver-operating characteristic (HSROC) model. This study was reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement, the quality of included studies was assessed with the Quality Assessment of Diagnostic Accuracy Studies-2 tool (QUADAS-2) and the radiomics quality score (RQS). Meta-regression and subgroup analyses were performed to explore the impact of varying clinical settings.

RESULTS

A total of ten studies met the inclusion criteria. The pooled sensitivity and specificity were 0.77 (95% CI 0.68-0.84, I2 = 83.5%) and 0.75 (95% CI 0.67-0.82, I2 = 83.5%), respectively, with an area under the HSROC curve of 0.88 (95% CI 0.85-0.91). Study quality was not high while assessing with the RQS. Substantial heterogeneity was observed between studies; however, meta-regression analysis did not reveal any significant contributing factors.

CONCLUSIONS

MRI radiomics demonstrated moderate sensitivity and specificity, offering similar diagnostic performance with previous risk stratifications and models that primarily based on radiologists' subjective experience. However, all studies included were retrospective, thus the performance of radiomics needs to validate in prospective, multicenter studies.

How Can We Identify Extraprostatic Extension (EPE) Before Surgery? The Use of a Preoperative Prostate MRI EPE Scoring System to Assess Postprostatectomy Locally Advanced Prostate Cancer

Background: Distinguishing between organ-confined disease and extraprostatic extension (EPE) is crucial for the treatment of patients with prostate cancer. EPE is associated with an increased risk of biochemical recurrence, positive surgical margins, and metastatic disease. An MRI-based EPE scoring system was developed by Mehralivand in 2019; however, it has not been adopted in the Urology community. The purpose of this study is to evaluate the association of MRI-based EPE scoring with the pathologic EPE (pEPE) after radical prostatectomy. Methods: We conducted a retrospective review on a prospectively collected database of male patients who underwent a prostate MRI with EPE scoring by a trained genitourinary radiologist and subsequent robotic radical prostatectomy at our institution from September 2020 to December 2022. The associations between MRI EPE (mEPE) score and the presence of EPE on surgical pathology (pEPE) were examined using multivariable logistic regression. Results: A total of 194 patients met inclusion criteria with a median age of 63 years and prostate specific antigen (PSA) 7 ng/mL. Among those with mEPE score 3, 96% had pEPE. Those patients with an mEPE score ≥2 had an increased risk of pEPE compared with those with mEPE score 0 (odds ratio 3.79; 95% confidence interval 1.28-11.3) Furthermore, those with an mEPE score 3 were significantly more likely to have pEPE compared with those with mEPE score 0, 1 and 2 independently. Conclusion: MRI EPE is a straightforward tool that strongly correlates with the presence of pEPE. If validated prospectively, this scoring system could assist in counseling patients regarding nerve-sparing approach.

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.

Addressing racial disparities in prostate cancer pathology prediction models: external validation and comparison of four models of pathological outcome prediction before radical prostatectomy in the multiethnic SEARCH cohort

BACKGROUND

Certain widely used pathological outcome prediction models that were developed in tertiary centers tend to overpredict outcomes in the community setting; thus, the Michigan Urological-Surgery Improvement Collaborative (MUSIC) model was developed in general urology practice to address this issue. Additionally, the development of these models involved a relatively small proportion of Black men, potentially compromising the accuracy of predictions in this patient group. We tested the validity of the MUSIC and three widely used nomograms to compare their overall and race-stratified predictive performance.

METHODS

We extracted data from 4139 (1138 Black) men from the Shared Equal Access Regional Cancer Hospital (SEARCH) database of the Veterans Affairs health system. The predictive performance of the MUSIC model was compared to the Memorial-Sloan Kettering (MSK), Briganti-2012, and Partin-2017 models for predicting lymph-node invasion (LNI), extra-prostatic extension (EPE), and seminal vesicle invasion (SVI).

RESULTS

The median PSA of Black men was higher than White men (7.8 vs. 6.8 ng/ml), although they were younger by a median of three years and presented at a lower-stage disease. MUSIC model showed comparable discriminatory capacity (AUC:77.0%) compared to MSK (79.2%), Partin-2017 (74.6%), and Briganti-2012 (76.3%), with better calibration for LNI. AUCs for EPE and SVI were 72.7% and 76.9%, respectively, all comparable to the MSK and Partin models. LNI AUCs for Black and White men were 69.6% and 79.6%, respectively, while EPE and SVI AUCs were comparable between races. EPE and LNI had worse calibration in Black men. Decision curve analysis showed MUSIC superiority over the MSK model in predicting LNI, especially among Black men.

CONCLUSION

Although the discriminatory performance of all models was comparable for each outcome, the MUSIC model exhibited superior net benefit to the MSK model in predicting LNI outcomes among Black men in the SEARCH population.

Integration of Multiparameter MRI into Conventional Pretreatment Risk Factors to Predict Positive Surgical Margins After Radical Prostatectomy

INTRODUCTION/BACKGROUND

Positive surgical margins (PSMs) after radical prostatectomy (RP) can increase the risk of biochemical recurrence in prostate cancer (PCa) patients. However, the prediction of the likelihood of PSMs in patients undergoing similar surgical procedures remains a challenge. We aim to develop a predictive model for PSMs in patients undergoing non-nerve-sparing RP.

PATIENTS AND METHODS

In this retrospective study, we analyzed data from PCa patients who underwent minimally invasive non-nerve-sparing RP at our hospital between June 2017 and June 2021. We identified independent risk factors associated with PSMs using clinical and MRI-based parameters in univariate and multivariate logistic regression analyzes. These factors were then used to develop a nomogram for predicting the probability of PSMs. The predictive performance was validated using calibration and receiver operating characteristic curve, area under the curve ,and decision curve analysis.

RESULTS

Multivariate analyzes revealed prostate-specific antigen density, tumor size, tumor location at the apex, tumor contact length, extracapsular extension (ECE) level, and apparent diffusion coefficient value as independent risk factors. A nomogram was developed and validated with high accuracy (C-index = 0.78). Furthermore, we found that 44.2% of patients diagnosed with organ-confined disease had ECE after surgery, and 29.1% of patients with Gleason scores ≤7 had higher pathological scores. Interestingly, the tumor burden calculated from PCa biopsy cores was overestimated when compared to postoperative PCa specimens.

CONCLUSION

We developed a reliable nomogram for predicting the risk of PSMs in PCa patients undergoing non-nerve-sparing RP. The study highlights the importance of incorporating these parameters in personalized surgical management.

Clinical application of machine learning models in patients with prostate cancer before prostatectomy

BACKGROUND

To build machine learning predictive models for surgical risk assessment of extracapsular extension (ECE) in patients with prostate cancer (PCa) before radical prostatectomy; and to compare the use of decision curve analysis (DCA) and receiver operating characteristic (ROC) metrics for selecting input feature combinations in models.

METHODS

This retrospective observational study included two independent data sets: 139 participants from a single institution (training), and 55 from 15 other institutions (external validation), both treated with Robotic Assisted Radical Prostatectomy (RARP). Five ML models, based on different combinations of clinical, semantic (interpreted by a radiologist) and radiomics features computed from T2W-MRI images, were built to predict extracapsular extension in the prostatectomy specimen (pECE+). DCA plots were used to rank the models' net benefit when assigning patients to prostatectomy with non-nerve-sparing surgery (NNSS) or nerve-sparing surgery (NSS), depending on the predicted ECE status. DCA model rankings were compared with those drived from ROC area under the curve (AUC).

RESULTS

In the training data, the model using clinical, semantic, and radiomics features gave the highest net benefit values across relevant threshold probabilities, and similar decision curve was observed in the external validation data. The model ranking using the AUC was different in the discovery group and favoured the model using clinical + semantic features only.

CONCLUSIONS

The combined model based on clinical, semantic and radiomic features may be used to predict pECE + in patients with PCa and results in a positive net benefit when used to choose between prostatectomy with NNS or NNSS.

Multimodal radiomics based on 18F-Prostate-specific membrane antigen-1007 PET/CT and multiparametric MRI for prostate cancer extracapsular extension prediction

OBJECTIVES

To compare the performance of the multiparametric magnetic resonance imaging (mpMRI) radiomics and 18F-Prostate-specific membrane antigen (PSMA)-1007 PET/CT radiomics model in diagnosing extracapsular extension (EPE) in prostate cancer (PCa), and to evaluate the performance of a multimodal radiomics model combining mpMRI and PET/CT in predicting EPE.

METHODS

We included 197 patients with PCa who underwent preoperative mpMRI and PET/CT before surgery. mpMRI and PET/CT images were segmented to delineate the regions of interest and extract radiomics features. PET/CT, mpMRI, and multimodal radiomics models were constructed based on maximum correlation, minimum redundancy, and logistic regression analyses. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC) and indices derived from the confusion matrix.

RESULTS

AUC values for the mpMRI, PET/CT, and multimodal radiomics models were 0.85 (95% CI, 0.78-0.90), 0.73 (0.64-0.80), and 0.83 (0.75-0.89), respectively, in the training cohort and 0.74 (0.61-0.85), 0.62 (0.48-0.74), and 0.77 (0.64-0.87), respectively, in the testing cohort. The net reclassification improvement demonstrated that the mpMRI radiomics model outperformed the PET/CT one in predicting EPE, with better clinical benefits. The multimodal radiomics model performed better than the single PET/CT radiomics model (P < .05).

CONCLUSION

The mpMRI and 18F-PSMA-PET/CT combination enhanced the predictive power of EPE in patients with PCa. The multimodal radiomics model will become a reliable and robust tool to assist urologists and radiologists in making preoperative decisions.

ADVANCES IN KNOWLEDGE

This study presents the first application of multimodal radiomics based on PET/CT and MRI for predicting EPE.

Quantitative MRI in the Local Staging of Prostate Cancer: A Systematic Review and Meta-Analysis

BACKGROUND

Local staging of prostate cancer (PCa) is important for treatment planning. Radiologist interpretation using qualitative criteria is variable with high specificity but low sensitivity. Quantitative methods may be useful in the diagnosis of extracapsular extension (ECE).

PURPOSE

To assess the performance of quantitative MRI markers for detecting ECE.

STUDY TYPE

Systematic review and meta-analysis.

SUBJECTS

4800 patients from 28 studies with histopathologically confirmed PCa on radical prostatectomy were pooled for meta-analysis. Patients from 46 studies were included for systematic review.

FIELD STRENGTH/SEQUENCE

Diffusion-weighted, T2-weighted, and dynamic contrast-enhanced MRI at 1.5 T or 3 T.

ASSESSMENT

PubMed, Embase, Web of Science, Scopus, and Cochrane databases were searched to identify studies on diagnostic test accuracy or association of any quantitative MRI markers with ECE. Results extracted by two independent reviewers for tumor contact length (TCL) and mean apparent diffusion coefficient (ADC-mean) were pooled for meta-analysis, but not for other quantitative markers including radiomics due to low number of studies available.

STATISTICAL TESTS

Hierarchical summary receiver operating characteristic (HSROC) curves were computed for both TCL and ADC-mean, but summary operating points were computed for TCL only. Heterogeneity was investigated by meta-regression. Results were significant if P ≤ 0.05.

RESULTS

At the 10 mm threshold for TCL, summary sensitivity and specificity were 0.76 [95% confidence interval (CI) 0.71-0.81] and 0.68 [95% CI 0.63-0.73], respectively. At the 15 mm threshold, summary sensitivity and specificity were 0.70 [95% CI 0.53-0.83] and 0.74 [95% CI 0.60-0.84] respectively. The area under the HSROC curves for TCL and ADC-mean were 0.79 and 0.78, respectively. Significant sources of heterogeneity for TCL included timing of MRI relative to biopsy.

DATA CONCLUSION

Both 10 mm and 15 mm thresholds for TCL may be reasonable for clinical use. From comparison of the HSROC curves, ADC-mean may be superior to TCL at higher sensitivities.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY STAGE: 2.

Optimal size threshold for PIRADSv2 category 5 upgrade and its positive predictive value: is it predictive of "very high" likelihood of clinically-significant cancer?

AIM

To identify the optimal size metric and threshold for Prostate Imaging Reporting and Data System (PIRADS) 5 upgrade, calculate its positive predictive value (PPV) for clinically-significant prostate cancer (csPCA), and determine if it is indicative of a "very high" likelihood of csPCA.

MATERIALS AND METHODS

One hundred and forty-three PIRADS 4 or 5 lesions were evaluated. Lesion diameters were used to calculate lesion volume (LV). Pearson correlation between maximum lesion diameter (MLD) and LV was calculated. Area under the curve (AUC) for discriminating csPCA (Gleason grade ≥ 3 + 4) was calculated using MLD and LV. Optimal size thresholds (using Youden index) and highly predictive size thresholds were identified for the whole prostate (WP), peripheral zone (PZ), and transitional zone (TZ).

RESULTS

There was high correlation between MLD and LV (r=0.77-0.81), with comparable AUCs for MLD and LV in the identification of csPCA in the WP (0.73, 0.72), PZ (0.73, 0.73), and TZ (0.79, 0.75). Optimal MLD thresholds were 1.4, 1.4, and 1.6 cm in the WP, PZ, and TZ respectively, with PPVs of 76%, 81%, and 69%, respectively. An MLD threshold of 2.7 cm would be needed in the WP to achieve a PPV approaching 90%, with sensitivity decreasing to 10%.

CONCLUSIONS

There is high correlation between MLD and LV with comparable discrimination of csPCA using each. PIRADSv2's 1.5 cm MLD threshold is near the optimal threshold for PIRADS 5 upgrade but has moderate PPV. A much higher threshold would be needed to increase its PPV, with significant sacrifice in sensitivity.

Prostate-Specific Membrane Antigen PET/Computed Tomography: Pearls and Pitfalls

Prostate-specific membrane antigen PET (PSMA-PET) has emerged as a powerful imaging tool for prostate cancer primary staging, biochemical recurrence, and advanced disease assessment. This article offers a concise overview of the benefits and challenges associated with PSMA-PET for prostate cancer evaluation. The article highlights the advantages of PSMA-PET over conventional imaging, such as its higher sensitivity and specificity for detecting metastases, and the potential for guiding personalized treatment decisions. However, it also explores the limitations and potential pitfalls for interpretation. Overall, the article aims to provide valuable insights for clinicians and diagnostic imaging physicians in clinical practice.

PI-RADS Version 2.0 Versus Version 2.1: Comparison of Prostate Cancer Gleason Grade Upgrade and Downgrade Rates From MRI-Targeted Biopsy to Radical Prostatectomy

BACKGROUND. Precise risk stratification through MRI/ultrasound (US) fusion-guided targeted biopsy (TBx) can guide optimal prostate cancer (PCa) management. OBJECTIVE. The purpose of this study was to compare PI-RADS version 2.0 (v2.0) and PI-RADS version 2.1 (v2.1) in terms of the rates of International Society of Urological Pathology (ISUP) grade group (GG) upgrade and downgrade from TBx to radical prostatectomy (RP). METHODS. This study entailed a retrospective post hoc analysis of patients who underwent 3-T prostate MRI at a single institution from May 2015 to March 2023 as part of three prospective clinical trials. Trial participants who underwent MRI followed by MRI/US fusion-guided TBx and RP within a 1-year interval were identified. A single genitourinary radiologist performed clinical interpretations of the MRI examinations using PI-RADS v2.0 from May 2015 to March 2019 and PI-RADS v2.1 from April 2019 to March 2023. Upgrade and downgrade rates from TBx to RP were compared using chi-square tests. Clinically significant cancer was defined as ISUP GG2 or greater. RESULTS. The final analysis included 308 patients (median age, 65 years; median PSA density, 0.16 ng/mL2). The v2.0 group (n = 177) and v2.1 group (n = 131) showed no significant difference in terms of upgrade rate (29% vs 22%, respectively; p = .15), downgrade rate (19% vs 21%, p = .76), clinically significant upgrade rate (14% vs 10%, p = .27), or clinically significant downgrade rate (1% vs 1%, p > .99). The upgrade rate and downgrade rate were also not significantly different between the v2.0 and v2.1 groups when stratifying by index lesion PI-RADS category or index lesion zone, as well as when assessed only in patients without a prior PCa diagnosis (all p > .01). Among patients with GG2 or GG3 at RP (n = 121 for v2.0; n = 103 for v2.1), the concordance rate between TBx and RP was not significantly different between the v2.0 and v2.1 groups (53% vs 57%, p = .51). CONCLUSION. Upgrade and downgrade rates from TBx to RP were not significantly different between patients whose MRI examinations were clinically interpreted using v2.0 or v2.1. CLINICAL IMPACT. Implementation of the most recent PI-RADS update did not improve the incongruence in PCa grade assessment between TBx and surgery.

Interpretation of Prostate Magnetic Resonance Imaging Using Prostate Imaging and Data Reporting System Version 2.1: A Primer

Prostate magnetic resonance imaging (MRI) is increasingly being used to diagnose and stage prostate cancer. The Prostate Imaging and Data Reporting System (PI-RADS) version 2.1 is a consensus-based reporting system that provides a standardized and reproducible method for interpreting prostate MRI. This primer provides an overview of the PI-RADS system, focusing on its current role in clinical interpretation. It discusses the appropriate use of PI-RADS and how it should be applied by radiologists in clinical practice to assign and report PI-RADS assessments. We also discuss the changes from prior versions and published validation studies on PI-RADS accuracy and reproducibility.

Prostate Cancer Local Staging with Magnetic Resonance Imaging

Accurate determination of the local stage of prostate cancer is crucial for treatment planning and prognosis. The primary objective of local staging is to distinguish between organ-confined and locally advanced disease, with the latter carrying a worse clinical prognosis. The presence of locally advanced disease features of prostate cancer, such as extra-prostatic extension, seminal vesicle invasion, and positive surgical margin, can impact the choice of treatment. Over the past decade, multiparametric MRI (mpMRI) has become the preferred imaging modality for the local staging of prostate cancer and has been shown to provide accurate information on the location and extent of disease. It has demonstrated superior performance compared to staging based on traditional clinical nomograms. Despite being a relatively new technique, mpMRI has garnered considerable attention and ongoing investigations. Therefore, in this review, we will discuss the current use of mpMRI on prostate cancer local staging.

Periprostatic Adipose Tissue: A New Perspective for Diagnosing and Treating Prostate Cancer

Prostate cancer (PCa) is the most common tumor of the male genitourinary system. It will eventually progress to fatal metastatic castration-resistant prostate cancer, for which treatment options are limited. Adipose tissues are distributed in various parts of the body. They have different morphological structures and functional characteristics and are associated with the development of various tumors. Periprostatic adipose tissue (PPAT) is the closest white visceral adipose tissue to the prostate and is part of the PCa tumor microenvironment. Studies have shown that PPAT is involved in PCa development, progression, invasion, and metastasis through the secretion of multiple active molecules. Factors such as obesity, diet, exercise, and organochlorine pesticides can affect the development of PCa indirectly or directly through PPAT. Based on the mechanism of PPAT's involvement in regulating PCa, this review summarized various diagnostic and therapeutic approaches for PCa with potential applications to assess the progression of patients' disease and improve clinical outcomes.

Less qualitative multiparametric magnetic resonance imaging in prostate cancer can underestimate extraprostatic extension in higher grade tumors

BACKGROUND

Multiparametric magnetic resonance imaging (mpMRI) is increasingly used for risk stratification and preoperative staging of prostate cancer. It remains unclear how Grade Group (GG) interacts with the ability of mpMRI to determine the presence of extraprostatic extension (EPE) on surgical pathology.

METHODS

A retrospective review of a robotic assisted laparoscopic radical prostatectomy (RALP) database from 2016-2020 was performed. Radiology mpMRI reports by multiple attending radiologists and without clear standardization or quality control were retrospectively assessed for EPE findings and compared with surgical pathology reports. The data were stratified by biopsy-based GG and a multivariable cluster analysis was performed to incorporate additional preoperative variables (age at diagnosis, PSA, etc.). Hazard ratios were calculated to determine how mpMRI findings and radiographic EPE relate to positive surgical margins.

RESULTS

289 patients underwent at least one mpMRI prior to RALP. Preoperative mpMRI demonstrated sensitivity of 39.3% and specificity of 88.8% for pathological EPE and had a negative predictive value (NPV) of 49.5%, and positive predictive value (PPV) of 84.0%. Stratification of NPV by GG yielded the following values: GG 1-5 (49.5%), GG 3-5 (40.8%), GG 4-5 (43.4%), and GG 5 (30.4%). Additionally, positive EPE on preoperative mpMRI was associated with a significantly decreased risk of positive surgical margins (RR: 0.655; 95% CI: 0.557-0.771).

CONCLUSIONS

NPV of prostate mpMRI for EPE may be decreased for higher grade tumors. A detailed reference reading and image quality optimization may improve performance. However, urologists should exercise caution in nerve sparing approaches in these patients.

Prostate Imaging after Focal Ablation (PI-FAB): A Proposal for a Scoring System for Multiparametric MRI of the Prostate After Focal Therapy

At present there is no standardised system for scoring the appearance of the prostate on multiparametric magnetic resonance imaging (MRI) after focal ablation for localised prostate cancer. We propose a novel scoring system, the Prostate Imaging after Focal Ablation (PI-FAB) score, to fill this gap. PI-FAB involves a 3-point scale for rating MRI sequences in sequential order: (1) dynamic contrast-enhanced sequences; (2) diffusion-weighted imaging, split into assessment of the high-b-value sequence first and then the apparent diffusion coefficient map; and (3) T2-weighted imaging. It is essential that the pretreatment scan is also available to help with this assessment. We designed PI-FAB using our experience of reading postablation scans over the past 15 years and include details for four representative patients initially treated with high-intensity focus ultrasound at our institution to demonstrate the scoring system. We propose PI-FAB as a standardised method for evaluating prostate MRI scans after treatment with focal ablation. The next step is to evaluate its performance across multiple experienced readers of MRI after focal therapy in a clinical data set. PATIENT SUMMARY: We propose a scoring system called PI-FAB for assessing the appearance of magnetic resonance imaging scans of the prostate after focal treatment for localised prostate cancer. This will help clinicians in deciding on further follow-up.

Comparison of MRI-Based Staging and Pathologic Staging for Predicting Biochemical Recurrence of Prostate Cancer After Radical Prostatectomy

BACKGROUND. Currently most clinical models for predicting biochemical recurrence (BCR) of prostate cancer (PCa) after radical prostatectomy (RP) incorporate staging information from RP specimens, creating a gap in preoperative risk assessment. OBJECTIVE. The purpose of our study was to compare the utility of presurgical staging information from MRI and postsurgical staging information from RP pathology in predicting BCR in patients with PCa. METHODS. This retrospective study included 604 patients (median age, 60 years) with PCa who underwent prostate MRI before RP from June 2007 to December 2018. A single genitourinary radiologist assessed MRI examinations for extraprostatic extension (EPE) and seminal vesicle invasion (SVI) during clinical interpretations. The utility of EPE and SVI on MRI and RP pathology for BCR prediction was assessed through Kaplan-Meier and Cox proportional hazards analyses. Established clinical BCR prediction models, including the University of California San Francisco Cancer of the Prostate Risk Assessment (UCSF-CAPRA) model and the Cancer of the Prostate Risk Assessment Postsurgical (CAPRA-S) model, were evaluated in a subset of 374 patients with available Gleason grade groups from biopsy and RP pathology; two CAPRA-MRI models (CAPRA-S model with modifications to replace RP pathologic staging features with MRI staging features) were also assessed. RESULTS. Univariable predictors of BCR included EPE on MRI (HR = 3.6), SVI on MRI (HR = 4.4), EPE on RP pathology (HR = 5.0), and SVI on RP pathology (HR = 4.6) (all p < .001). Three-year BCR-free survival (RFS) rates for patients without versus with EPE were 84% versus 59% for MRI and 89% versus 58% for RP pathology, and 3-year RFS rates for patients without versus with SVI were 82% versus 50% for MRI and 83% versus 54% for RP histology (all p < .001). For patients with T3 disease on RP pathology, 3-year RFS rates were 67% and 41% for patients without and with T3 disease on MRI. AUCs of CAPRA models, including CAPRA-MRI models, ranged from 0.743 to 0.778. AUCs were not significantly different between CAPRA-S and CAPRA-MRI models (p > .05). RFS rates were significantly different between low- and intermediate-risk groups for only CAPRA-MRI models (80% vs 51% and 74% vs 44%; both p < .001). CONCLUSION. Presurgical MRI-based staging features perform comparably to postsurgical pathologic staging features for predicting BCR. CLINICAL IMPACT. MRI staging can preoperatively identify patients at high BCR risk, helping to inform early clinical decision-making. TRIAL REGISTRATION. ClinicalTrials.gov NCT00026884 and NCT02594202.

The feasibility of distance to the tumor of biopsy cores to estimate the extracapsular extension

Background

To investigate the predictive capability of a new parameter, the distance between the fibromuscular capsule and the tumor as measured using a prostate biopsy core (referred to as "distance to the tumor" [DTT]), for the presence of extracapsular extension (ECE).

Materials and methods

We analyzed specimens obtained from 246 patients diagnosed with prostate cancer. All patients underwent prebiopsy, prostate magnetic resonance imaging (MRI), and subsequent prostatectomy. DTT measurements were obtained for each prostate biopsy core, and the minimum (min) DTT was extracted. We assessed the relationship between min DTT, MRI-estimated ECE, and pathological ECE, considering factors such as the PI-RADS score and tumor location.

Results

In this study of 246 patients, the mean age was 65.8 years, and the mean prostate-specific antigen (PSA) level was 18.9 ng/ml. Patients with suspicious lesions in the peripheral zone and pathological ECE displayed higher rates of positive digital rectal examination (DRE), elevated PSA levels, and shorter DTT values in the biopsy cores. DTT demonstrated an accurate estimation of the presence of ECE, similar to MRI findings. Min DTT exhibited higher accuracy for peripheral zone masses, with a cutoff value of 1.0 mm for min DTT predicting ECE (AUC: 0.84, sensitivity: 72.23%, specificity: 77.78%, P < 0.01). Of the 246 patients, 66 had no ECE on MRI; however, 18 of these patients displayed pathological ECE, with 14 having DTT values <1.0 mm.

Conclusions

Min DTT, positive DRE results, and a higher Gleason grade were significantly associated with ECE. DTT measurements of <1 mm can provide a more accurate prediction of ECE in the peripheral zone of the prostate than MRI-based assessments.

Prostate zones and tumor morphological parameters on magnetic resonance imaging for predicting the tumor-stage diagnosis of prostate cancer

PURPOSE

To determine whether the morphological parameters of prostate zones and tumors on magnetic resonance imaging (MRI) can predict the tumor-stage (T-stage) of prostate cancer (PCa) and establish an optimal T-stage diagnosis protocol based on three-dimensional reconstruction and quantization after image segmentation.

METHODS

A dataset of the prostate MRI scans and clinical data of 175 patients who underwent biopsy and had pathologically proven PCa from January 2018 to November 2020 was retrospectively analyzed. The authors manually segmented and measured the volume, major axis, and cross-sectional area of the peripheral zone (PZ), transition zone, central zone (CZ), anterior fibromuscular stroma, and tumor. The differences were evaluated by the One-Way analysis of variance, Pearson's chi-squared test, or independent samples t-test. Spearman's correlation coefficient and receiver operating characteristic curve analyses were also performed. The cut-off values of the T-stage diagnosis were generated using Youden's J index.

RESULTS

The prostate volume (PV), PZ volume (PZV), CZ volume, tumor's major axis (TA), tumor volume (TV), and volume ratio of the TV and PV were significantly different among stages T1 to T4. The cut-off values of the PV, PZV, CZV, TA, TV, and the ratio of TV/PV for the discrimination of the T1 and T2 stages were 53.63 cm3, 11.60 cm3, 1.97 cm3, 2.30 mm, 0.90 cm3, and 0.03 [area under the curves (AUCs): 0.628, 0.658, 0.610, 0.689, 0.724, and 0.764], respectively. The cut-off values of the TA, TV, and the ratio of TV/PV for the discrimination of the T2 and T3 stages were 2.80 mm, 8.29 cm3, and 0.12 (AUCs: 0.769, 0.702, and 0.688), respectively. The cut-off values of the TA, TV, and the ratio of TV/PV for the discrimination of the T3 and T4 stages were 4.17 mm, 18.71 cm3, and 0.22 (AUCs: 0.674, 0.709, and 0.729), respectively.

CONCLUSION

The morphological parameters of the prostate zones and tumors on the MRIs are simple and valuable diagnostic factors for predicting the T-stage of patients with PCa, which can help make accurate diagnoses and lateral treatment decisions.

An MRI-based grading system for preoperative risk estimation of positive surgical margin after radical prostatectomy

OBJECTIVE

To construct a simplified grading system based on MRI features to predict positive surgical margin (PSM) after radical prostatectomy (RP).

METHODS

Patients who had undergone prostate MRI followed by RP between January 2017 and January 2021 were retrospectively enrolled as the derivation group, and those between February 2021 and November 2022 were enrolled as the validation group. One radiologist evaluated tumor-related MRI features, including the capsule contact length (CCL) of lesions, frank extraprostatic extension (EPE), apex abutting, etc. Binary logistic regression and decision tree analysis were used to select risk features for PSM. The area under the curve (AUC), sensitivity, and specificity of different systems were calculated. The interreader agreement of the scoring systems was evaluated using the kappa statistic.

RESULTS

There were 29.8% (42/141) and 36.4% (32/88) of patients who had PSM in the derivation and validation cohorts, respectively. The first grading system was proposed (mrPSM1) using two imaging features, namely, CCL ≥ 20 mm and apex abutting, and then updated by adding frank EPE (mrPSM2). In the derivation group, the AUC was 0.705 for mrPSM1 and 0.713 for mrPSM2. In the validation group, our grading systems showed comparable AUC with Park et al.'s model (0.672-0.686 vs. 0.646, p > 0.05) and significantly higher specificity (0.732-0.750 vs. 0.411, p < 0.001). The kappa value was 0.764 for mrPSM1 and 0.776 for mrPSM2. Decision curve analysis showed a higher net benefit for mrPSM2.

CONCLUSION

The proposed grading systems based on MRI could benefit the risk stratification of PSM and are easily interpretable.

CRITICAL RELEVANCE STATEMENT

The proposed mrPSM grading systems for preoperative prediction of surgical margin status after radical prostatectomy are simplified compared to a previous model and show high specificity for identifying the risk of positive surgical margin, which might benefit the management of prostate cancer.

KEY POINTS

• CCL ≥ 20 mm, apex abutting, and EPE were important MRI features for PSM. • Our proposed MRI-based grading systems showed the possibility to predict PSM with high specificity. • The MRI-based grading systems might facilitate a structured risk evaluation of PSM.

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.

Comprehensive analysis of predictive factors for upstaging in intraprostatic cancer after radical prostatectomy: Different patterns of spread exist in lesions at different locations

BACKGROUND

Accurate assessment of the clinical staging is crucial for determining the need for radical prostatectomy (RP) in prostate cancer (PCa). However, the current methods for PCa staging may yield incorrect results. This study aimed to comprehensively analyze independent predictors of postoperative upstaging of intraprostatic cancer.

METHODS

We conducted a retrospective analysis of data from intraprostatic cancer patients who underwent radical surgery between March 2019 and December 2022. Intraprostatic cancer was defined as a lesion confined to the prostate, excluding cases where multiparameter magnetic resonance imaging (mpMRI) showed the lesion in contact with the prostatic capsule. We assessed independent predictors of extraprostatic extension (EPE) and analyzed their association with positive surgical margin (PSM) status. In addition, based on the distance of the lesion from the capsule on mpMRI, we divided the patients into non-transition zone and transition zone groups for further analysis.

RESULTS

A total of 500 patients were included in our study. Logistic regression analysis revealed that biopsy Gleason grade group (GG) (odds ratio, OR: 1.370, 95% confidence interval, CI: 1.093-1.718) and perineural invasion (PNI) (OR: 2.746, 95% CI: 1.420-5.309) were predictive factors for postoperative EPE. Both biopsy GG and PNI were associated with lateral (GG: OR: 1.270, 95% CI: 1.074-1.501; PNI: OR: 2.733, 95% CI: 1.521-4.911) and basal (GG: OR: 1.491, 95% CI: 1.194-1.862; PNI: OR: 3.730, 95% CI: 1.929-7.214) PSM but not with apex PSM (GG: OR: 1.176, 95% CI: 0.989-1.399; PNI: OR: 1.204, 95% CI: 0.609-2.381) after RP. Finally, PNI was an independent predictor of EPE in the transition zone (OR: 11.235, 95% CI: 2.779-45.428) but not in the non-transition zone (OR: 1.942, 95% CI: 0.920-4.098).

CONCLUSION

PNI and higher GG may indicate upstaging of tumors in patients with intraprostatic carcinoma. These two factors are associated with PSM in locations other than the apex of the prostate. Importantly, cancer in the transition zone of the prostate is more likely to spread externally through nerve invasion than cancer in the non-transition zone.

MR image quality in local staging of prostate cancer: Role of PI-QUAL in the detection of extraprostatic extension

PURPOSE

To assess the impact of prostate MRI image quality by means of the Prostate Imaging Quality (PI-QUAL) score, on the identification of extraprostatic extension of disease (EPE), predicted using the EPE Grade Score, Likert Scale Score (LSS) and a clinical nomogram (MSKCCn).

METHODS

We retrospectively included 105 patients with multiparametric prostate MRI prior to prostatectomy. Two radiologists evaluated image quality using PI-QUAL (≥4 was considered high quality) in consensus. All cases were also scored using the EPE Grade, the LSS, and the MSKCCn (dichotomized). Inter-rater reproducibility for each score was also assessed. Accuracy was calculated for the entire population and by image quality, considering two thresholds for EPE Grade (≥2 and = 3) and LSS (≥3 and ≥ 4) and using McNemar's test for comparison.

RESULTS

Overall, 66 scans achieved high quality. The accuracy of EPE Grade ranged from 0.695 to 0.743, while LSS achieved values between 0.705 and 0.733. Overall sensitivity for the radiological scores (range = 0.235-0.529) was low irrespective of the PI-QUAL score, while specificity was higher (0.775-0.986). The MSKCCn achieved an AUC of 0.76, outperforming EPE Grade (=3 threshold) in studies with suboptimal image quality (0.821 vs 0.564, p = 0.016). EPE Grade (=3 threshold) accuracy was also better in high image quality studies (0.849 vs 0.564, p = 0.001). Reproducibility was good to excellent overall (95 % Confidence Interval range = 0.782-0.924).

CONCLUSION

Assessing image quality by means of PI-QUAL is helpful in the evaluation of EPE, as a scan of low quality makes its performance drop compared to clinical staging tools.

Diagnostic performance of prediction models for extraprostatic extension in prostate cancer: a systematic review and meta-analysis

PURPOSE

In recent decades, diverse nomograms have been proposed to predict extraprostatic extension (EPE) in prostate cancer (PCa). We aimed to systematically evaluate the accuracy of MRI-inclusive nomograms and traditional clinical nomograms in predicting EPE in PCa. The purpose of this meta-analysis is to provide baseline summative and comparative estimates for future study designs.

MATERIALS AND METHODS

The PubMed, Embase, and Cochrane databases were searched up to May 17, 2023, to identify studies on prediction nomograms for EPE of PCa. The risk of bias in studies was assessed by using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). Summary estimates of sensitivity and specificity were obtained with bivariate random-effects model. Heterogeneity was investigated through meta-regression and subgroup analysis.

RESULTS

Forty-eight studies with a total of 57 contingency tables and 20,395 patients were included. No significant publication bias was observed for either the MRI-inclusive nomograms or clinical nomograms. For MRI-inclusive nomograms predicting EPE, the pooled AUC of validation cohorts was 0.80 (95% CI: 0.76, 0.83). For traditional clinical nomograms predicting EPE, the pooled AUCs of the Partin table and Memorial Sloan Kettering Cancer Center (MSKCC) nomogram were 0.72 (95% CI: 0.68, 0.76) and 0.79 (95% CI: 0.75, 0.82), respectively.

CONCLUSION

Preoperative risk stratification is essential for PCa patients; both MRI-inclusive nomograms and traditional clinical nomograms had moderate diagnostic performance for predicting EPE in PCa. This study provides baseline comparative values for EPE prediction for future studies which is useful for evaluating preoperative risk stratification in PCa patients.

CRITICAL RELEVANCE STATEMENT

This meta-analysis firstly evaluated the diagnostic performance of preoperative MRI-inclusive nomograms and clinical nomograms for predicting extraprostatic extension (EPE) in prostate cancer (PCa) (moderate AUCs: 0.72-0.80). We provide baseline estimates for EPE prediction, these findings will be useful in assessing preoperative risk stratification of PCa patients.

KEY POINTS

• MRI-inclusive nomograms and traditional clinical nomograms had moderate AUCs (0.72-0.80) for predicting EPE. • MRI combined clinical nomogram may improve diagnostic accuracy of MRI alone for EPE prediction. • MSKCC nomogram had a higher specificity than Partin table for predicting EPE. • This meta-analysis provided baseline and comparative estimates of nomograms for EPE prediction for future studies.

Predictors of Extraprostatic Extension in Patients with Prostate Cancer

PURPOSE

To identify effective factors predicting extraprostatic extension (EPE) in patients with prostate cancer (PCa).

METHODS

This retrospective cohort study recruited 898 consecutive patients with PCa treated with robot-assisted laparoscopic radical prostatectomy. The patients were divided into EPE and non-EPE groups based on the analysis of whole-mount histopathologic sections. Histopathological analysis (ISUP biopsy grade group) and magnetic resonance imaging (MRI) (PI-RADS v2.1 scores [1-5] and the Mehralivand EPE grade [0-3]) were used to assess the prediction of EPE. We also assessed the clinical usefulness of the prediction model based on decision-curve analysis.

RESULTS

Of 800 included patients, 235 (29.3%) had EPE, and 565 patients (70.7%) did not (non-EPE). Multivariable logistic regression analysis showed that the biopsy ISUP grade, PI-RADS v2.1 score, and Mehralivand EPE grade were independent risk factors for EPE. In the regression assessment of the models, the best discrimination (area under the curve of 0.879) was obtained using the basic model (age, serum PSA, prostate volume at MRI, positive biopsy core, clinical T stage, and D'Amico risk group) and Mehralivand EPE grade 3. Decision-curve analysis showed that combining Mehralivand EPE grade 3 with the basic model resulted in superior net benefits for predicting EPE.

CONCLUSION

Mehralivand EPE grades and PI-RADS v2.1 scores, in addition to basic clinical and demographic information, are potentially useful for predicting EPE in patients with PCa.

Value of a combined magnetic resonance imaging-based radiomics-clinical model for predicting extracapsular extension in prostate cancer: a preliminary study

Background

Extracapsular extension (ECE) of prostate cancer (PCa) is closely related to the treatment and prognosis of patients, and radiomics has been widely used in the study of PCa. This study aimed to evaluate the value of a combined model considering magnetic resonance imaging (MRI)-based radiomics and clinical parameters for predicting ECE in PCa.

Methods

A total of 392 PCa patients enrolled in this retrospective study were randomly divided into the training and validation sets at a ratio of 7:3. Radiologists assessed all lesions by Mehralivand grade. Radiomics features were extracted and selected to build a radiomics model, while clinical parameters were noted to construct the clinical model. The combined model was constructed by the integration of the radiomics model and clinical model. Meanwhile, the nomogram for predicting ECE was constructed based on the combined model. Then, the area under the receiver operating characteristic (ROC) curve (AUC), Delong test and the decision curve analysis (DCA) were used to compare the performance among the combined model, radiomics model, clinical model and Mehralivand grade.

Results

The AUC of the combined model in the validation set was comparable to that of the radiomics model [AUC =0.894 (95% confidence interval (CI): 0.837-0.950) vs. 0.835 (95% CI: 0.763-0.908), P>0.05]. In addition, the sensitivity of the combined model and radiomics model was 90.7% and 77.8%, with an accuracy of 81.4% and 76.3%, respectively. On the other hand, the AUCs of the Mehralivand grade of radiologists and clinical model were 0.774 (95% CI: 0.691-0.857) and 0.749 (95% CI: 0.658-0.840), respectively, in the validation set, which were lower than those in the combined model (P<0.05). The DCA implied that the combined model could obtain the maximum net clinical benefits compared with the clinical model, the Mehralivand grade and radiomics model.

Conclusions

The combined model has a satisfactory predictive value for ECE in PCa patients compared with the clinical model, Mehralivand grade of radiologists, and the radiomics model.

Nomogram Models for Distinguishing Intraductal Carcinoma of the Prostate From Prostatic Acinar Adenocarcinoma Based on Multiparametric Magnetic Resonance Imaging

OBJECTIVE

To compare multiparametric magnetic resonance imaging (MRI) features of intraductal carcinoma of the prostate (IDC-P) with those of prostatic acinar adenocarcinoma (PAC) and develop prediction models to distinguish IDC-P from PAC and IDC-P with a high proportion (IDC ≥ 10%, hpIDC-P) from IDC-P with a low proportion (IDC < 10%, lpIDC-P) and PAC.

MATERIALS AND METHODS

One hundred and six patients with hpIDC-P, 105 with lpIDC-P and 168 with PAC, who underwent pretreatment multiparametric MRI between January 2015 and December 2020 were included in this study. Imaging parameters, including invasiveness and metastasis, were evaluated and compared between the PAC and IDC-P groups as well as between the hpIDC-P and lpIDC-P subgroups. Nomograms for distinguishing IDC-P from PAC, and hpIDC-P from lpIDC-P and PAC, were made using multivariable logistic regression analysis. The discrimination performance of the models was assessed using the receiver operating characteristic area under the curve (ROC-AUC) in the sample, where the models were derived from without an independent validation sample.

RESULTS

The tumor diameter was larger and invasive and metastatic features were more common in the IDC-P than in the PAC group (P < 0.001). The distribution of extraprostatic extension (EPE) and pelvic lymphadenopathy was even greater, and the apparent diffusion coefficient (ADC) ratio was lower in the hpIDC-P than in the lpIDC-P group (P < 0.05). The ROC-AUCs of the stepwise models based solely on imaging features for distinguishing IDC-P from PAC and hpIDC-P from lpIDC-P and PAC were 0.797 (95% confidence interval, 0.750-0.843) and 0.777 (0.727-0.827), respectively.

CONCLUSION

IDC-P was more likely to be larger, more invasive, and more metastatic, with obviously restricted diffusion. EPE, pelvic lymphadenopathy, and a lower ADC ratio were more likely to occur in hpIDC-P, and were also the most useful variables in both nomograms for predicting IDC-P and hpIDC-P.

Perineural Invasion and Spread in Common Abdominopelvic Diseases: Imaging Diagnosis and Clinical Significance

Malignancies and other diseases may spread by multiple pathways, including direct extension, hematogenous spread, or via lymphatic vessels. A less-well-understood route is the peripheral nervous system, which is known as perineural spread (PNS). In addition to accounting for pain and other neurologic symptoms, PNS affects both disease prognosis and management. Although PNS is commonly discussed in relation to head and neck tumors, there is emerging data regarding PNS in abdominopelvic malignancies and other conditions such as endometriosis. Due to improved contrast and spatial resolution, perineural invasion, a finding heretofore diagnosed only at pathologic examination, can be detected at CT, MRI, and PET/CT. PNS most commonly manifests as abnormal soft-tissue attenuation extending along neural structures, and diagnosis of it is aided by optimizing imaging parameters, understanding pertinent anatomy, and becoming familiar with the typical neural pathways of spread that largely depend on the disease type and location. In the abdomen, the celiac plexus is a central structure that innervates the major abdominal organs and is the principal route of PNS in patients with pancreatic and biliary carcinomas. In the pelvis, the lumbosacral plexus and inferior hypogastric plexus are the central structures and principal routes of PNS in patients with pelvic malignancies. Although the imaging findings of PNS may be subtle, a radiologic diagnosis can have a substantial effect on patient care. Knowledge of anatomy and known routes of PNS and optimizing imaging parameters is of utmost importance in providing key information for prognosis and treatment planning. © RSNA, 2023 Supplemental material and the slide presentation from the RSNA Annual Meeting are available for this article. Quiz questions for this article are available through the Online Learning Center.

Staging of Prostate Cancer: Role of Multiparametric Magnetic Resonance Imaging in Different Risk Classes

Using multiparametric magnetic resonance imaging, it is now possible to diagnose prostate cancer and categorize its risk. As it can accurately determine the extracapsu- lar extension of the tumor, invasion of seminal vesicles, involvement of lymph nodes, and the potential presence of bone metastases, multiparametric magnetic resonance imaging plays a crucial role not only in the diagnosis but also in the local staging of prostate cancer. The patients with a history of negative biopsy/increasing prostate- specific antigen and the existence of further data supporting its use in biopsy-naive patients and active surveillance are the most blatant indications for multiparametric magnetic resonance imaging in guidelines. The traditional clinical examination, pros- tate-specific antigen tests, and systematic biopsy are all enhanced by multiparametric magnetic resonance imaging, which will miss certain cancers due to insufficient size or changes in tissue density. The use of multiparametric magnetic resonance imaging is expected to rise, and further advances in the method will be crucial for the secure adoption of targeted therapeutic ideas. Here, we give a succinct overview of multipa- rametric magnetic resonance imaging's application to the identification and risk clas- sification of prostate cancer.