Detection and Localization of Prostate Cancer at 3-T Multiparametric MRI Using PI-RADS Segmentation

Understanding Spatial Correlation Between Multiparametric MRI Performance and Prostate Cancer

BACKGROUND

Multiparametric MRI (mpMRI) has shown a substantial impact on prostate cancer (PCa) diagnosis. However, the understanding of the spatial correlation between mpMRI performance and PCa location is still limited.

PURPOSE

To investigate the association between mpMRI performance and tumor spatial location within the prostate using a prostate sector map, described by Prostate Imaging Reporting and Data System (PI-RADS) v2.1.

STUDY TYPE

Retrospective.

SUBJECTS

One thousand one hundred forty-three men who underwent mpMRI before radical prostatectomy between 2010 and 2022.

FIELD STRENGTH/SEQUENCE

3.0 T. T2-weighted turbo spin-echo, a single-shot spin-echo EPI sequence for diffusion-weighted imaging, and a gradient echo sequence for dynamic contrast-enhanced MRI sequences.

ASSESSMENT

Integrated relative cancer prevalence (rCP), detection rate (DR), and positive predictive value (PPV) maps corresponding to the prostate sector map for PCa lesions were created. The relationship between tumor location and its detection/missing by radiologists on mpMRI compared to WMHP as a reference standard was investigated.

STATISTICAL TESTS

A weighted chi-square test was performed to examine the statistical differences for rCP, DR, and PPV of the aggregated sectors within the zone, anterior/posterior, left/right prostate, and different levels of the prostate with a statistically significant level of 0.05.

RESULTS

A total of 1665 PCa lesions were identified in 1143 patients, and from those 1060 lesions were clinically significant (cs)PCa tumors (any Gleason score [GS] ≥7). Our sector-based analysis utilizing weighted chi-square tests suggested that the left posterior part of PZ had a high likelihood of missing csPCa lesions at a DR of 67.0%. Aggregated sector analysis indicated that the anterior or apex locations in PZ had the significantly lowest csPCa detection at 67.3% and 71.5%, respectively.

DATA CONCLUSION

Spatial characteristics of the per-lesion-based mpMRI performance for diagnosis of PCa were studied. Our results demonstrated that there is a spatial correlation between mpMRI performance and locations of PCa on the prostate.

EVIDENCE LEVEL

4 TECHNICAL EFFICACY: Stage 2.

Advantage of whole-mount histopathology in prostate cancer: current applications and future prospects

BACKGROUND

Whole-mount histopathology (WMH) has been a powerful tool to investigate the characteristics of prostate cancer. However, the latest advancement of WMH was yet under summarization. In this review, we offer a comprehensive exposition of current research utilizing WMH in diagnosing and treating prostate cancer (PCa), and summarize the clinical advantages of WMH and outlines potential on future prospects.

METHODS

An extensive PubMed search was conducted until February 26, 2023, with the search term "prostate", "whole-mount", "large format histology", which was limited to the last 4 years. Publications included were restricted to those in English. Other papers were also cited to contribute a better understanding.

RESULTS

WMH exhibits an enhanced legibility for pathologists, which improved the efficacy of pathologic examination and provide educational value. It simplifies the histopathological registration with medical images, which serves as a convincing reference standard for imaging indicator investigation and medical image-based artificial intelligence (AI). Additionally, WMH provides comprehensive histopathological information for tumor volume estimation, post-treatment evaluation, and provides direct pathological data for AI readers. It also offers complete spatial context for the location estimation of both intraprostatic and extraprostatic cancerous region.

CONCLUSIONS

WMH provides unique benefits in several aspects of clinical diagnosis and treatment of PCa. The utilization of WMH technique facilitates the development and refinement of various clinical technologies. We believe that WMH will play an important role in future clinical applications.

Prostate cancer lesion detection, volume quantification and high-grade cancer differentiation using cancer risk maps derived from multiparametric MRI with histopathology as the reference standard

Multi-parametric MRI (mpMRI) has proven itself a clinically useful tool to assess prostate cancer (PCa). Our objective was to generate PCa risk maps to quantify the volume and location of both all PCa and high grade (Gleason grade group ≥ 3) PCa. Such capabilities would aid physicians and patients in treatment decisions, targeting biopsy, and planning focal therapy. A cohort of men with biopsy proven prostate cancer and pre-prostatectomy mpMRI were studied. PCa and benign ROIs (1524) were identified on mpMRI and histopathology with histopathology serving as the reference standard. Logistic regression models were created to differentiate PCa from benign tissues. The MRI images were registered to ensure correct overlay. The cancer models were applied to each image voxel within prostates to create probability maps of cancer and of high-grade cancer. Use of an optimum probability threshold quantified PCa volume for all lesions >0.1 cc. Accuracies were calculated using area under the curve (AUC) for the receiver operating characteristic (ROC). The PCa models utilized apparent diffusion coefficient (ADC), T2 weighted (T2W), dynamic contrast-enhanced MRI (DCE MRI) enhancement slope, and DCE MRI washout as the statistically significant MRI scans. Application of the PCa maps method provided total PCa volume and individual lesion volumes. The AUCs derived from lesion analysis were 0.91 for all PCa and 0.73 for high-grade PCa. At the optimum threshold, the PCa maps detected 135 / 150 (90%) histopathological lesions >0.1 cc. This study showed the feasibility of cancer risk maps, created from pre-prostatectomy, mpMR images validated with histopathology, to detect PCa lesions >0.1 cc. The method quantified the volume of cancer within the prostate. Method improvements were identified by determining root causes for over and underestimation of cancer volumes. The maps have the potential for improved non-invasive capability in quantitative detection, localization, volume estimation, and MRI characterization of PCa.

Comparison of MRI, PSMA PET/CT, and Fusion PSMA PET/MRI for Detection of Clinically Significant Prostate Cancer

PURPOSE

The aim of our study is to compare the efficacy of positron emission tomography (PET) and magnetic resonance imaging (MRI) for detecting intraprostatic lesions in patients with clinically significant prostate cancer who underwent radical prostatectomy; additionally, investigate the benefits of rostate-specific membrane antigen (PSMA) PET-MR software fusion images to the diagnosis.

METHODS

Thirty patients, who underwent radical prostatectomy between June 2015 and April 2018, were included in the study. Subjects with gallium PSMA PET-CT and multiparametric prostate MRI performed according to Prostate Imaging Reporting and Data System v2 criteria in our clinic were included in the study. 68Ga-PSMA PET-CT images were fused with MR sequences for analysis.

RESULTS

The mean age of cases was 63.2 years (ranged from 45 to 79 years). Index lesions of 29 cases were detected by MRI and 22 of them by PET CT. Both modalities were found to be less sensitive for detection of bilaterality and multifocality (42.85% and 20% for MRI, 28.57% and 20% for PET CT, respectively). There was no statistically significant difference between modalities. It was observed that if a clinically significant tumor focus was not detected by MRI, it was small (6 mm or less) in diameter or had a low Gleason score.

CONCLUSIONS

Software fusion PSMA PET-MRI increased the sensitivity of the index lesion identification compared with PSMA PET-CT and also increased the sensitivity of real lesion size identification compared with multiparametric prostate MRI.

Influence of the Location and Zone of Tumor in Prostate Cancer Detection and Localization on 3-T Multiparametric MRI Based on PI-RADS Version 2

OBJECTIVE. The objective of our study was to determine the performance of 3-T multiparametric MRI (mpMRI) for prostate cancer (PCa) detection and localization, stratified by anatomic zone and level, using Prostate Imaging Reporting and Data System version 2 (PI-RADSv2) and whole-mount histopathology (WMHP) as reference. MATERIALS AND METHODS. Multiparametric MRI examinations of 415 consecutive men were compared with thin-section WMHP results. A genitourinary radiologist and pathologist collectively determined concordance. Two radiologists assigned PI-RADSv2 scores and sector location to all detected foci by consensus. Tumor detection rates were calculated for clinical and pathologic (tumor location and zone) variables. Both rigid and adjusted sector-matching models were used to account for fixation-related issues. RESULTS. Of 863 PCa foci in 16,185 prostate sectors, the detection of overall and index PCa lesions in the midgland, base, and apex was 54.9% and 83.1%, 42.1% and 64.0% (p = 0.04, p = 0.02), and 41.9% and 71.4% (p = 0.001, p = 0.006), respectively. Tumor localization sensitivity was highest in the midgland compared with the base and apex using an adjusted match compared with a rigid match (index lesions, 71.3% vs 43.7%; all lesions, 70.8% vs 36.0%) and was greater in the peripheral zone (PZ) than in the transition zone. Three-Tesla mpMRI had similarly high specificity (range, 93.8-98.3%) for overall and index tumor localization when using both rigid and adjusted sector-matching approaches. CONCLUSION. For 3-T mpMRI, the highest sensitivity (83.1%) for detection of index PCa lesions was in the midgland, with 98.3% specificity. Multiparametric MRI performance for sectoral localization of PCa within the prostate was moderate and was best for index lesions in the PZ using an adjusted model.

Assessment of the Minimal Targeted Biopsy Core Number per MRI Lesion for Improving Prostate Cancer Grading Prediction

Background: To study the impact of MRI characteristics and of targeted biopsy (TB) core number on the final grade group (GG) prediction. Materials and Methods: The cohort was 478 consecutive patients who underwent radical prostatectomy (RP) after positive mpMRI (multiparametric magnetic resonance imaging) followed by fusion TB. Endpoints were the upgrading and concordance rates between TB and RP specimens. Results: Upgrading rate after TB was 40.6%. Patients with upgrading had lower PIRADS (Prostate Imaging-Reporting and Data System) scores (p < 0.001), smaller lesion size (p = 0.017), fewer TB cores (p < 0.001), and lower TB density (p = 0.015) compared with cases with grade concordance. There was a significant continuous improvement in upgrading rate when TB core number per lesion increased from 56.3% to 25.6% when <2 or ≥5 TB cores were taken, respectively (p = 0.002). The minimal TB number per lesion to reduce upgrading risk to approximately 30%was 4 in PIRADS 3, and 3 in PIRADS 4–5 cases. Conclusions: Grade group prediction by TB is significantly improved by higher PIRADS score, larger lesion size, and increased TB per lesion. At least four TB cores should be taken in PIRADS 3 score lesions, whereas three cores seem enough in PIRADS 4–5 cases to improve GG prediction and limit upgrading risk.