Characteristics of missed prostate cancer lesions on 3T multiparametric-MRI in 518 patients: based on PI-RADSv2 and using whole-mount histopathology reference

Detection of MRI-Invisible Disease Using PSMA PET/CT in a Patient Considering Focal Therapy

Patient selection for focal therapy (FT) of prostate cancer requires the assessment of MRI and biopsy results. However, there is currently little guidance for the utility of PSMA PET/CT in FT planning. We describe the case of a man originally considered an ideal candidate for FT based on biopsy and MRI who was found to have a contralateral lesion-harboring cancer detected only on PSMA PET/CT. Trial Registration: ClinicalTrials.gov identifier: NCT05852041.

Synthetic DWI: contrast improvement for diffusion-weighted imaging in prostate using T1 shine-through by synthesizing images with adjusted TR and TE

OBJECTIVE

To investigate whether synthetic DWI (SyDWI) calculated with short TR and zero TE can improve diffusion contrast in prostate compared to conventional DWI acquired with standard TR and TE.

MATERIALS AND METHODS

Thirty-two patients who underwent multiparametric MRI (mp-MRI) on a 3.0 T scanner were enrolled. For SyDWI, DWIs at b0 were acquired with two different TRs and TEs in addition to b1000 and b2000 images acquired with single conventional TR and TE. Contrast ratio (CR) was compared between SyDWI calculated with TR of 1000 ms and TE of 0 ms and conventional DWI acquired with TR of 6000 ms and TE of 70 ms.

RESULTS

The mean CR between prostate cancer (PCa) and normal prostate, and between PCa and benign prostatic hyperplasia (BPH), is significantly higher in SyDWI compared to conventional DWI for both b-values of 1000 and 2000 s/mm2. In addition, contrast within some lesions is now visualized, suggesting that tumour heterogeneity can be observed that is not seen with conventional DWI.

CONCLUSION

SyDWI calculated with TR of 1000 ms and TE of 0 ms significantly improves diffusion contrast between PCa and normal prostate or BPH, and within the lesion, compared to conventional DWI as a result of T1 shine-through.

Physics-Informed Autoencoder for Prostate Tissue Microstructure Profiling with Hybrid Multidimensional MRI

Purpose To evaluate the performance of Physics-Informed Autoencoder (PIA), a self-supervised deep learning model, in measuring tissue-based biomarkers for prostate cancer (PCa) using hybrid multidimensional MRI. Materials and Methods This retrospective study introduces PIA, an emerging self-supervised deep learning model that integrates a three-compartment diffusion-relaxation model with hybrid multidimensional MRI. PIA was trained to encode the biophysical model into a deep neural network to predict measurements of tissue-specific biomarkers for PCa without extensive training data requirements. Comprehensive in silico and in vivo experiments, using histopathology measurements as the reference standard, were conducted to validate the model's efficacy in comparison to the traditional nonlinear least squares (NLLS) algorithm. PIA's robustness to noise was tested in in silico experiments with varying signal-to-noise ratio (SNR) conditions, and in vivo performance for estimating volume fractions was evaluated in 21 patients (mean age, 60 years ± 6.6 [SD]; all male) with PCa (71 regions of interest). Evaluation metrics included the intraclass correlation coefficient (ICC) and Pearson correlation coefficient. Results PIA predicted the reference standard tissue parameters with high accuracy, outperforming conventional NLLS methods, especially under noisy conditions (rs = 0.80 vs 0.65, P < .001 for epithelium volume at SNR of 20:1). In in vivo validation, PIA's noninvasive volume fraction estimates matched quantitative histology (ICC, 0.94, 0.85, and 0.92 for epithelium, stroma, and lumen compartments, respectively; P < .001 for all). PIA's measurements strongly correlated with PCa aggressiveness (r = 0.75, P < .001). Furthermore, PIA ran 10 000 faster than NLLS (0.18 second vs 40 minutes per image). Conclusion PIA provided accurate prostate tissue biomarker measurements from MRI data with better robustness to noise and computational efficiency compared with the NLLS algorithm. The results demonstrate the potential of PIA as an accurate, noninvasive, and explainable artificial intelligence method for PCa detection. Keywords: Prostate, Stacked Auto-Encoders, Tissue Characterization, MR-Diffusion-weighted Imaging Supplemental material is available for this article. ©RSNA, 2025 See also commentary by Adams and Bressem in this issue.

Prospective Validation of an Automated Hybrid Multidimensional MRI Tool for Prostate Cancer Detection Using Targeted Biopsy: Comparison with PI-RADS-based Assessment

Purpose To evaluate the use of an automated hybrid multidimensional MRI (HM-MRI)-based tool to prospectively identify prostate cancer targets before MRI/US fusion biopsy in comparison with Prostate Imaging and Reporting Data System (PI-RADS)-based multiparametric MRI (mpMRI) evaluation by expert radiologists. Materials and Methods In this prospective clinical trial (ClinicalTrials.gov registration no. NCT03585660), 91 male participants (mean age, 65 years ± 8 [SD]) with known or suspected prostate cancer underwent 3-T MRI with a conventional mpMRI protocol and HM-MRI followed by subsequent biopsy between August 2018 and March 2023. Using the HM-MRI tool, tissue composition was calculated using a three-compartment model, and suspected prostate cancer regions with elevated epithelium (>40%) and reduced lumen (<20%) meeting the minimum size requirement of 25 mm2 were identified. Up to two additional biopsy targets per participant were automatically selected with the HM-MRI tool in addition to the biopsy targets selected based on an expert radiologist's mpMRI interpretation (≥PI-RADS 3) using an MRI/US fusion biopsy device. Additional 12-core transrectal US-guided sextant random biopsy cores were also obtained. Detection of clinically significant prostate cancer (≥Gleason 3+4) was compared between HM-MRI and mpMRI by calculating area under the receiver operating characteristic curve and diagnostic accuracy metrics. Results The diagnostic performance of HM-MRI was either higher than mpMRI or showed no evidence of a difference when compared with mpMRI. On a per-participant basis, HM-MRI had significantly higher accuracy (55% vs 44%; P = .02) and specificity (36% vs 14%: P = .002) than mpMRI. On a per-lesion basis, HM-MRI had significantly higher accuracy (58% vs 39%; P < .001) and positive predictive value (31% vs 22%; P = .004) compared with mpMRI. Only one lesion was missed when using the combination of mpMRI and HM-MRI. On a per-sextant basis, HM-MRI showed significantly better performance than mpMRI for all metrics, including primary end points of the area under the receiver operating characteristic curve (0.76 vs 0.65; P < .001) and accuracy (83.9% vs 79.0%; P = .006). Conclusion This study demonstrates that HM-MRI has the potential to improve MRI/US fusion biopsy results for prostate cancer detection by providing complementary information to PI-RADS-based evaluation by expert radiologists. Keywords: Prostate Cancer, Hybrid Multidimensional MRI, Multiparametric MRI, PI-RADS Clinical trial registration no. NCT03585660 ©RSNA, 2025.

Clinical characteristics and pathological features of undetectable clinically significant prostate cancer on multiparametric magnetic resonance imaging: A single-center and retrospective study

Objectives

The objectives of this study were to clarify the pathological features of clinically significant prostate cancer (csPC) that is undetectable on multiparametric magnetic resonance imaging (mpMRI).

Material and Methods

This single-center and retrospective study enrolled 33 men with prostate cancer (PC), encompassing 109 PC lesions, who underwent mpMRI before radical prostatectomy. Two radiologists independently assessed the mpMR images of all lesions and compared them with the pathological findings of PC. All PC lesions were marked on resected specimens using prostate imaging reporting and data system version 2.1 and classified into magnetic resonance imaging (MRI)-detectable and MRI-undetectable PC lesions. Each lesion was classified into csPC and clinically insignificant PC. Pathological characteristics were compared between MRI-detectable and MRI-undetectable csPC. Statistical analysis was performed to identify factors associated with MRI detectability. A logistic regression model was used to determine the factors associated with MRI-detectable and MRI-undetectable csPC.

Results

Among 109 PC lesions, MRI-detectable and MRI-undetectable PCs accounted for 31% (34/109) and 69% (75/109) of lesions, respectively. All MRI-detectable PCs were csPC. MRI-undetectable PCs included 30 cases of csPC (40%). The detectability of csPC on mpMRI was 53% (34/64). The MRI-undetectable csPC group had a shorter major diameter (10.6 ± 6.6 mm vs. 19.0 ± 6.9 mm, P < 0.001), shorter minor diameter (5.7 ± 2.9 mm vs. 10.7 ± 3.4 mm, P < 0.001), and lower percentage of lesions with Gleason pattern 5 (17% vs. 71%, P < 0.001). Shorter minor diameter (odds ratio [OR], 2.62; P = 0.04) and lower percentage of Gleason pattern 5 (OR, 24; P = 0.01) were independent predictors of MRI-undetectable csPC.

Conclusion

The pathological features of MRI-undetectable csPC included shorter minor diameter and lower percentage of Gleason pattern 5. csPC with shorter minor diameter may not be detected on mpMRI. Some MRI-undetectable csPC lesions exhibited sufficient size and Gleason pattern 5, emphasizing the need for further understanding of pathological factors contributing to MRI detectability.

MRI-based virtual pathology of the prostate

Prostate cancer poses significant diagnostic challenges, with conventional methods like prostate-specific antigen (PSA) screening and transrectal ultrasound (TRUS)-guided biopsies often leading to overdiagnosis or miss clinically significant cancers. Multiparametric MRI (mpMRI) has emerged as a more reliable tool. However, it is limited by high inter-observer variability and radiologists missing up to 30% of clinically significant cancers. This article summarizes a few of these recent advancements in quantitative MRI techniques that look at the "Virtual Pathology" of the prostate with an aim to enhance prostate cancer detection and characterization. These techniques include T2 relaxation-based techniques such as luminal water imaging, diffusion based such as vascular, extracellular, and restricted diffusion for cytometry in tumors (VERDICT) and restriction spectrum imaging or combined relaxation-diffusion techniques such as hybrid multi-dimensional MRI (HM-MRI), time-dependent diffusion imaging, and diffusion-relaxation correlation spectrum imaging. These methods provide detailed insights into underlying prostate microstructure and tissue composition and have shown improved diagnostic accuracy over conventional MRI. These innovative MRI methods hold potential for augmenting mpMRI, reducing variability in diagnosis, and paving the way for MRI as a 'virtual histology' tool in prostate cancer diagnosis. However, they require further validation in larger multi-center clinical settings and rigorous in-depth radiological-pathology correlation are needed for broader implementation.

Exploring the potential of ex-vivo 7-T magnetic resonance imaging on patients with clinically significant prostate cancer: visibility and size perspective

Background

Despite progress in multiparametric magnetic resonance imaging (MRI), issues of prostate cancer invisibility and underestimated tumor burden persist. This study investigates the potential of an ultra-high field MRI at 7-T in an ex-vivo setting to address these limitations.

Methods

This prospective study included 54 tumors from 20 treatment-naïve clinically significant prostate cancer patients, confirmed by biopsy, despite negative findings on preoperative 3-T MRI. Ex-vivo 7-T MRI of resected prostates was performed, with assessment on tumor visibility and size. Factors influencing visibility were analyzed using logistic regression analyses.

Results

Tumor visibility was confirmed in 80% of patients, and 48% of all tumors on ex-vivo imaging. Gleason pattern 4 percentage (odds ratio 1.09) and tumor size on pathology (odds ratio 1.36) were significantly associated with visibility (P < 0.05). Mean MRI-visible and invisible tumor sizes were 10.5 mm and 5.3 mm, respectively. The size discrepancy between MRI and pathology was 2.7 mm.

Conclusion

Tumor visibility on ex-vivo 7-T MRI was influenced by tumor grade and size. The notable tumor visibility initially overlooked on 3-T MRI, along with small size discrepancy with pathology, suggests potential improvements in resolution.

Four-quadrant vector mapping of hybrid multidimensional MRI data for the diagnosis of prostate cancer

PURPOSE

The interpretation of prostate multiparametric magnetic resonance imaging (MRI) is subjective in nature, and there is large inter-observer variability among radiologists and up to 30% of clinically significant cancers are missed. This has motivated the development of new MRI techniques and sequences, especially quantitative approaches to improve prostate cancer diagnosis. Using hybrid multidimensional MRI, apparent diffusion coefficient (ADC) and T2 have been shown to change as a function of echo time (TE) and b-values, and that this dependence is different for cancer and benign tissue, which can be exploited for prostate cancer diagnosis. The purpose of this study is to investigate whether four-quadrant vector mapping of hybrid multidimensional MRI (HM-MRI) data can be used to diagnose prostate cancer (PCa) and determine cancer aggressiveness.

METHODS

Twenty-one patients with confirmed PCa underwent preoperative MRI prior to radical prostatectomy. Axial HM-MRI were acquired with all combinations of TE = 47, 75, 100 ms and b-values of 0, 750, 1500 s/mm2 , resulting in a 3 × 3 data matrix associated with each voxel. Prostate Quadrant (PQ) mapping analysis represents HM-MRI data for each voxel as a color-coded vector in the four-quadrant space of HM-MRI parameters (a 2D matrix of signal values for each combination of b-value and TE) with associated amplitude and angle information representing the change in T2 and ADC as a function of b-value and TE, respectively.

RESULTS

Cancers have a higher PQ4 (22.50% ± 21.27%) and lower PQ2 (69.86% ± 28.24%) compared to benign tissue: peripheral, transition, and central zone (PQ4 = 0.13% ± 0.56%, 5.73% ± 15.07%, 2.66% ± 4.05%, and PQ2 = 98.51% ± 3.05%, 86.18% ± 21.75%, 93.38% ± 9.88%, respectively). Cancers have a higher vector angle (206.5 ± 41.8°) and amplitude (0.017 ± 0.013) compared to benign tissue. PQ metrics showed moderate correlation with Gleason score (|ρ| = 0.388-0.609), with more aggressive cancers being associated with increased PQ4 and angle and reduced PQ2 and amplitude. A combination of four-quadrant analysis metrics provided an area under the curve of 0.904 (p < 0.001) for the differentiation of prostate cancer from benign prostatic tissue.

CONCLUSIONS

Four-quadrant vector mapping of HM-MRI data provides effective cancer markers, with cancers associated with high PQ4 and high vector angle and lower PQ2 and vector amplitude.

Prostate Cancers Invisible on Multiparametric MRI: Pathologic Features in Correlation with Whole-Mount Prostatectomy

We investigated why some prostate cancers (PCas) are not identified on multiparametric MRI (mpMRI) by using ground truth reference from whole-mount prostatectomy specimens. A total of 61 patients with biopsy-confirmed PCa underwent 3T mpMRI followed by prostatectomy. Lesions visible on MRI prospectively or retrospectively identified after correlating with histology were considered "identified cancers" (ICs). Lesions that could not be identified on mpMRI were considered "unidentified cancers" (UCs). Pathologists marked the Gleason score, stage, size, and density of the cancer glands and performed quantitative histology to calculate the tissue composition. Out of 115 cancers, 19 were unidentified on MRI. The UCs were significantly smaller and had lower Gleason scores and clinical stage lesions compared with the ICs. The UCs had significantly (p < 0.05) higher ADC (1.34 ± 0.38 vs. 1.02 ± 0.30 μm2/ms) and T2 (117.0 ± 31.1 vs. 97.1 ± 25.1 ms) compared with the ICs. The density of the cancer glands was significantly (p = 0.04) lower in the UCs. The percentage of the Gleason 4 component in Gleason 3 + 4 lesions was nominally (p = 0.15) higher in the ICs (20 ± 12%) compared with the UCs (15 ± 8%). The UCs had a significantly lower epithelium (32.9 ± 21.5 vs. 47.6 ± 13.1%, p = 0.034) and higher lumen volume (20.4 ± 10.0 vs. 13.3 ± 4.1%, p = 0.021) compared with the ICs. Independent from size and Gleason score, the tissue composition differences, specifically, the higher lumen and lower epithelium in UCs, can explain why some of the prostate cancers cannot be identified on mpMRI.

Prostate magnetic resonance imaging and the value of experience: An intrareader variability study

Objective

To measure the intraobserver concordance of an experienced genitourinary radiologist reporting of multiparametric magnetic resonance imaging of the prostate (mpMRIp) scans over time.

Methods

An experienced genitourinary radiologist re-reported his original 100 consecutive mpMRIp scans using Prostate Imaging-Reporting and Data System version 2 (PI-RADS v2) after 5 years of further experience comprising >1000 scans. Intraobserver agreement was measured using Cohen's kappa. Sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), and accuracy were calculated, and comparison of sensitivity was performed using McNemar's test.

Results

Ninety-six mpMRIp scans were included in our final analysis. Of the 96 patients, 53 (55.2%) patients underwent subsequent biopsy (n=43) or prostatectomy (n=15), with 73 lesions targeted. Moderate agreement (Cohen's kappa 0.55) was seen in the number of lesions identified at initial reporting and on re-reading (81 vs. 39 total lesions; and 71 vs. 37 number of PI-RADS ≥3 lesions). For clinically significant prostate cancer, re-reading demonstrated an increase in specificity (from 43% to 89%) and PPV (from 62% to 87%), but a decrease in sensitivity (from 94% to 72%, p=0.01) and NPV (from 89% to 77%).

Conclusion

The intraobserver agreement for a novice to experienced radiologist reporting mpMRIp using PI-RADS v2 is moderate. Reduced sensitivity is off-set by improved specificity and PPV, which validate mpMRIp as a gold standard for prebiopsy screening.

A comprehensive comparison between mpMRI of the prostate, MR-US fusion biopsy and whole mount histopathology

OBJECTIVES

To compare multiparametric magnetic resonance imaging (mpMRI) findings, US-MR fusion prostate biopsy results and whole-mount thin-section histopathology after radical prostatectomy.

PATIENTS AND METHODS

Overall 259 patients, who had undergone mpMRI with lesions reported as PI-RADS 3-5, underwent a MR-US fusion biopsy between 2018 and 2020. Overall 186 biopsies yielded prostate cancer and 104 patients subsequently underwent endoscopic extraperitoneal radical prostatectomy. Histopathology of biopsies was compared to the final histopathology in whole mount thin sections after radical prostatectomy by means of descriptive statistics, and further, the lesions from mpMRT were compared to whole mount histology.

RESULTS

Prostate cancer was diagnosed in 186 (71.8%) of 259 patients (median age 69.2 y, range 42-82 y, median PSA 7.8 ng/ml, range 2.1-31.3 ng/ml). Of those, 95 (51,1%) underwent radical endoscopic prostatectomy, and 80 (43%) chose radiotherapy or active surveillance. In 52/95 (54,7%) with RPE additional lesions were found in the final histological whole mount sections not described at mpMRI. 22/95 (23,2%) of RPE patients had ≥ 1 additional Gleason score ≥ 7 lesions, 23 /259 (8,4%) of biopsies, respectively. The Gleason score after surgery was upgraded in 37/95 (38,9%) and downgraded in 18/95 (18,9%) patients.

CONCLUSION

If we compare all 259 performed biopsies with the final histological whole mount sections which showed additional lesions with Gleason ≥ 7 (23,2%), it can be assumed that up to 10% of clinical significant carcinomas are missed during primary assessment via mpMRI. The majority of additional findings after RP were intermediate/high risk tumors. Upgrades from low-risk to intermediate or high-risk occurred.

Combination of MRI prostate and 18F-DCFPyl PSMA PET/CT detects all clinically significant prostate cancers in treatment-naive patients: An international multicentre retrospective study

INTRODUCTION

Clinical and biochemical assessment and biopsies can miss clinically significant prostate cancers (csPCa) in up to 20% of patients and diagnose clinically insignificant tumours leading to overtreatment. This retrospective study analyses the accuracy of ¹⁸ F-DCFPyL PET/CT in detecting csPCa as a primary diagnostic tool and directly compares it with mpMRI prostate in treatment-naive patients. The two modalities are then correlated to determine whether they are better in combination, than either alone.

METHODS

This is a retrospective dual-institution study of patients who underwent contemporaneous MRI and PSMA-PET between January 2017 and March 2020 with histologic confirmation. The images were re-reviewed and concordance between modalities assessed. Results were compared with histopathology to determine the ability of MRI and PSMA-PET to detect csPCA.

RESULTS

MRI and PSMA-PET detected the same index lesion in 90.8% of cases with a kappa of 0.82. PET detected an additional 6.2% of index lesions which were MRI occult. MRI detected an additional 3.1% which were PET occult. No additional csPCa was identified on pathology which was not seen on imaging. The sensitivity of PSMA-PET in detecting csPCa is 96.7% and that of MRI is 93.4% with no statistically significant difference between the two (P = 0.232). Both modalities detected all four cases of non-csPCa with these being considered false positives.

CONCLUSION

Both mpMRI and 18F-DCFPyL-PSMA-PET/CT have high sensitivity for detecting csPCa with high agreement between modalities. There were no synchronous csPCa lesions detected on pathology that were not detected on imaging too.

MRI-detectability and histological factors of prostate cancer including intraductal carcinoma and cribriform pattern

BACKGROUND

Histopathological characteristics affecting the detectability of clinically significant prostate cancer (csPCa) on magnetic resonance imaging (MRI) remain unclear. This study aimed to compare the histopathology between MRI-detectable and MRI-undetectable cancers, emphasizing intraductal carcinoma of the prostate (IDC-P) and predominant Gleason pattern 4 subtype.

METHODS

This single-center retrospective study enrolled 153 consecutive patients with 191 lesions who underwent preoperative multiparametric MRI and subsequent radical prostatectomy. MRI/histopathological findings and area fractions of histological components (cancer cells, stroma, and luminal spaces) of MRI-detectable and MRI-undetectable cancers were compared. Data were analyzed using Fisher's exact, independent t, or Mann-Whitney U tests.

RESULTS

Overall, 148 (77%) and 43 (23%) cancers were MRI-detectable and MRI-undetectable, respectively. MRI-detectable cancers were significantly larger than MRI-undetectable cancers (p = 0.03). The percentage of lesions in Grade Group 3 or higher was significantly higher among MRI-detectable cancers than among MRI-undetectable cancers (p = 0.02). MRI detectability of csPCa was associated with increases in relative area fractions of cancer cells (p < 0.001) and decreases in those of stroma (p < 0.001) and luminal spaces (p < 0.001) in prostate cancer (PCa) than the percentage of Gleason pattern 4 (p = 0.09). The percentage of lesions containing IDC-P was similar for MRI-detectable and MRI-undetectable cancers (40% vs. 33%; p = 0.48). The distribution of cribriform gland subtypes was not significantly different between MRI-detectable and MRI-undetectable Gleason pattern 4 subtype cancers (p > 0.99). Contrarily, the ratio of fused gland subtype was significantly higher in MRI-detectable than in MRI-undetectable cancers (p = 0.03). Furthermore, the ratio of poorly-formed gland subtype was significantly higher in MRI-undetectable than in MRI-detectable cancers (p = 0.01).

CONCLUSIONS

MRI detectability of csPCa is strongly associated with the relative area fractions of cancer cells, stroma, and luminal spaces in PCa rather than conventional histopathological parameters. Neither the presence nor the percentage of IDC-P affected MRI detectability.

Histological validation of prostate tissue composition measurement using hybrid multi-dimensional MRI: agreement with pathologists' measures

PURPOSE

To validate prostate tissue composition measured using hybrid multi-dimensional MRI (HM-MRI) by comparing with reference standard (ground truth) results from pathologists' interpretation of clinical histopathology slides following whole mount prostatectomy.

MATERIALS AND METHODS

36 prospective participants with biopsy-confirmed prostate cancer underwent 3 T MRI prior to radical prostatectomy. Axial HM-MRI was acquired with all combinations of echo times of 57, 70, 150, 200 ms and b-values of 0, 150, 750, 1500 s/mm² and data were fitted using a 3-compartment signal model using custom software to generate volumes for each tissue component (stroma, epithelium, lumen). Three experienced genitourinary pathologists independently as well as in consensus reviewed each histology image and provide an estimate of percentage of epithelium and lumen for regions-of-interest corresponding to MRI (n = 165; 64 prostate cancers and 101 benign tissue). Agreement statistics using total deviation index (TDI_0.9) was performed for tissue composition measured using HM-MRI and reference standard results from pathologists' consensus.

RESULTS

Based on the initial results showing typical variation among pathologists TDI_0.9 = 25%, we determined we will declare acceptable agreement if the 95% one-sided upper confident limit of TDI_0.9 is less than 30%. The results of tissue composition measurement from HM-MRI compared to ground truth results from the consensus of 3 pathologists, reveal that ninety percent of absolute paired differences (TDI_0.9) were within 18.8% and 22.4% in measuring epithelium and lumen, respectively. We are 95% confident that 90% of absolute paired differences were within 20.6% and 24.2% in measuring epithelium and lumen, respectively. These were less than our criterion of 30% and inter-pathologists' agreement (22.3% for epithelium and 24.2% for lumen) and therefore we accept the agreement performance of HM-MRI. The results revealed excellent area under the ROC curve for differentiating cancer from benign tissue based on epithelium (HM-MRI: 0.87, pathologists: 0.97) and lumen volume (HM-MRI: 0.85, pathologists: 0.77).

CONCLUSION

The agreement in tissue composition measurement using hybrid multidimensional MRI and consensus of pathologists is on par with the inter-raters (pathologists) agreement.

Validation of Prostate Tissue Composition by Using Hybrid Multidimensional MRI: Correlation with Histologic Findings

Background Tissue estimates obtained by using microstructure imaging techniques, such as hybrid multidimensional (HM) MRI, may improve prostate cancer diagnosis but require histologic validation. Purpose To validate prostate tissue composition measured by using HM MRI, with quantitative histologic evaluation from whole-mount prostatectomy as the reference standard. Materials and Methods In this HIPAA-compliant study, from December 2016 to July 2018, prospective participants with biopsy-confirmed prostate cancer underwent 3-T MRI before radical prostatectomy. Axial HM MRI was performed with all combinations of echo times (57, 70, 150, and 200 msec) and b values (0, 150, 750, and 1500 sec/mm²). Data were fitted by using a three-compartment signal model to generate volumes for each tissue component (stroma, epithelium, lumen). Quantitative histologic evaluation was performed to calculate volume fractions for each tissue component for regions of interest corresponding to MRI. Tissue composition measured by using HM MRI and quantitative histologic evaluation were compared (paired t test) and correlated (Pearson correlation coefficient), and agreement (concordance correlation) was assessed. Receiver operating characteristic curve analysis for cancer diagnosis was performed. Results Twenty-five participants (mean age, 60 years ± 7 [standard deviation]; 30 cancers and 45 benign regions of interest) were included. Prostate tissue composition measured with HM MRI and quantitative histologic evaluation did not differ (stroma, 45% ± 11 vs 44% ± 11 [P = .23]; epithelium, 31% ± 15 vs 34% ± 15 [P = .08]; and lumen, 24% ± 13 vs 22% ± 11 [P = .80]). Between HM MRI and histologic evaluation, there was excellent correlation (Pearson r: overall, 0.91; stroma, 0.82; epithelium, 0.93; lumen, 0.90 [all P < .05]) and agreement (concordance correlation coefficient: overall, 0.91; stroma, 0.81; epithelium, 0.90; and lumen, 0.87). High areas under the receiver operating characteristic curve obtained with HM MRI (0.96 for epithelium and 0.94 for lumen, P < .001) and histologic evaluation (0.94 for epithelium and 0.88 for lumen, P < .001) were found for differentiation between benign tissue and prostate cancer. Conclusion Tissue composition measured by using hybrid multidimensional MRI had excellent correlation with quantitative histologic evaluation as the reference standard. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Muglia in this issue.

Effect of Echo Times on Prostate Cancer Detection on T2-Weighted Images

PURPOSE

To compare the effect of different echo times (TE) on the detection of prostate cancer (PCa) on T2-weighted MR images.

MATERIALS AND METHODS

This study recruited patients (n = 38) with histologically confirmed PCa who underwent preoperative 3T MRI. Three radiologists independently marked region on interests (ROIs) on suspected PCa lesions on T2-weighted images at different TEs: 90, 150, and 180 ms obtained with Turbo Spin Echo imaging protocol with multiple echoes. The ROIs were assigned a value 1-5 indicating the reviewer's confidence in accurately detecting PCa. These ROIs were compared to histologically confirmed PCa (n = 95) on whole mount prostatectomy sections to calculate sensitivity, positive predictive value (PPV), and confidence score.

RESULTS

Two radiologists (R1, R2) showed significantly increased sensitivity for PCa detection at 180 ms TE compared to 90 ms (R1: 43.2, 50.5, 50.5%, R2: 45.3, 44.2, 53.7% at TE of 90, 150, 180 ms, respectively) (p = 0.048, 0.033 for R1 and R2). Sensitivity was similar for radiologist 3 (45.3%-46.3%) at different TE values (p = 0.953). No significant difference in the PPV (R1: 64.1%-70.6%, R2: 46.7%-56.0%, R3: 70.5%-81.5%) and the confidence score assigned (R1: 4.6-4.8, R2: 4.6-4.8 R3: 4.3-4.4) was found for either of the radiologists.

CONCLUSION

Our results suggest improved detection of PCa with similar PPV and confidence scores when higher TE values are utilized for T2-weighted image acquisition.

Multimodality Imaging of Prostate Cancer

Prostate cancer is a very heterogeneous disease, and contemporary management is focused on identification and treatment of the prognostically adverse high-risk tumors while minimizing overtreatment of indolent, low-risk tumors. In recent years, imaging has gained increasing importance in the detection, staging, posttreatment assessment, and detection of recurrence of prostate cancer. Several imaging modalities including conventional and functional methods are used in different clinical scenarios with their very own advantages and limitations. This continuing medical education article provides an overview of available imaging modalities currently in use for prostate cancer followed by a more specific section on the value of these different imaging modalities in distinct clinical scenarios, ranging from initial diagnosis to advanced, metastatic castration-resistant prostate cancer. In addition to established imaging indications, we will highlight some potential future applications of contemporary imaging modalities in prostate cancer.

Extracapsular extension on MRI indicates a more aggressive cell cycle progression genotype of prostate cancer

PURPOSE

To explore associations between magnetic resonance imaging (MRI) features of prostate cancer and expression levels of cell cycle genes, as assessed by the Prolaris^® test.

MATERIALS AND METHODS

Retrospective analysis of 118 PCa patients with genetic testing of biopsy specimen and prostate MRI from 08/2013 to 11/2015. Associations between the cell cycle risk (CCR) score and MRI features [i.e., PI-RADSv2 score, extracapsular extension (ECE), quantitative metrics] were analyzed with Fisher's exact test, nonparametric tests, and Spearman's correlation coefficient. In 41 patients (34.7%), test results were compared to unfavorable features on prostatectomy specimen (i.e., Gleason group ≥ 3, ECE, lymph node metastases).

RESULTS

Fifty-four (45.8%), 60 (50.8%), and 4 (3.4%) patients had low-, intermediate-, and high-risk cancers according to American Urological Association scoring system. Patients with ECE on MRI had significantly higher mean CCR scores (reader 1: 3.9 vs. 3.2, p = 0.015; reader 2: 3.6 vs. 3.2, p = 0.045). PI-RADSv2 scores and quantitative MRI features were not associated with CCR scores. In the prostatectomy subset, ECE on MRI (p = < 0.001-0.001) and CCR scores (p = 0.049) were significantly associated with unfavorable histopathologic features.

CONCLUSION

The phenotypic trait of ECE on MRI indicates a more aggressive genotype of prostate cancer.