Prostate MRI and quality: lessons learned from breast imaging rad-path correlation

Decreased prostate MRI cancer detection rate due to moderate to severe susceptibility artifacts from hip prosthesis

OBJECTIVES

To evaluate the impact of susceptibility artifacts from hip prosthesis on cancer detection rate (CDR) in prostate MRI.

MATERIALS AND METHODS

This three-center retrospective study included prostate MRI studies for patients without known prostate cancer between 2017 and 2021. Exams with hip prosthesis were searched on MRI reports. The degree of susceptibility artifact on diffusion-weighted images was retrospectively categorized into mild, moderate, and severe (> 66%, 33-66%, and < 33% of the prostate volume are evaluable) by blind reviewers. CDR was defined as the number of exams with Gleason score ≥7 detected by MRI (PI-RADS ≥3) divided by the total number of exams. For each artifact grade, control exams without hip prosthesis were matched (1:6 match), and CDR was compared. The degree of CDR reduction was evaluated with ratio, and influential factors were evaluated by expanding the equation.

RESULTS

Hip arthroplasty was present in 548 (4.8%) of the 11,319 MRI exams. CDR of the cases and matched control exams for each artifact grade were as follows: mild (n = 238), 0.27 vs 0.25, CDR ratio = 1.09 [95% CI: 0.87-1.37]; moderate (n = 143), 0.18 vs 0.27, CDR ratio = 0.67 [95% CI: 0.46-0.96]; severe (n = 167), 0.22 vs 0.28, CDR ratio = 0.80 [95% CI: 0.59-1.08]. When moderate and severe artifact grades were combined, CDR ratio was 0.74 [95% CI: 0.58-0.93]. CDR reduction was mostly attributed to the increased frequency of PI-RADS 1-2.

CONCLUSION

With moderate to severe susceptibility artifacts from hip prosthesis, CDR was decreased to 74% compared to the matched control.

CLINICAL RELEVANCE STATEMENT

Moderate to severe susceptibility artifacts from hip prosthesis may cause a non-negligible CDR reduction in prostate MRI. Expanding indications for systematic prostate biopsy may be considered when PI-RADS 1-2 was assigned.

KEY POINTS

• We proposed cancer detection rate as a diagnostic performance metric in prostate MRI. • With moderate to severe susceptibility artifacts secondary to hip arthroplasty, cancer detection rate decreased to 74% compared to the matched control. • Expanding indications for systematic prostate biopsy may be considered when PI-RADS 1-2 is assigned.

Cancer Detection Rate and Abnormal Interpretation Rate of Prostate MRI in Patients With Low-Grade Cancer

PURPOSE

The aim of this study was to evaluate the utility of cancer detection rate (CDR) and abnormal interpretation rate (AIR) in prostate MRI for patients with low-grade prostate cancer (PCa).

METHODS

This three-center retrospective study included patients who underwent prostate MRI from 2017 to 2021 with known low-grade PCa (Gleason score 6) without prior treatment. Patient-level highest Prostate Imaging Reporting & Data System (PI-RADS®) score and pathologic diagnosis within 1 year after MRI were used to evaluate the diagnostic performance of prostate MRI in detecting clinically significant PCa (csPCa; Gleason score ≥ 7). The metrics AIR, CDR, and CDR adjusted for pathologic confirmation rate were calculated. Radiologist-level AIR-CDR plots were shown. Simulation AIR-CDR lines were created to assess the effects of different diagnostic performances of prostate MRI and the prevalence of csPCa.

RESULTS

A total of 3,207 examinations were interpreted by 33 radiologists. Overall AIR, CDR, and CDR adjusted for pathologic confirmation rate at PI-RADS 3 to 5 (PI-RADS 4 and 5) were 51.7% (36.5%), 22.1% (18.8%), and 30.7% (24.6%), respectively. Radiologist-level AIR and CDR at PI-RADS 3 to 5 (PI-RADS 4 and 5) were in the 36.8% to 75.6% (21.9%-57.5%) range and the 16.3%-28.7% (10.9%-26.5%) range, respectively. In the simulation, changing parameters of diagnostic performance or csPCa prevalence shifted the AIR-CDR line.

CONCLUSIONS

The authors propose CDR and AIR as performance metrics in prostate MRI and report reference performance values in patients with known low-grade PCa. There was variability in radiologist-level AIR and CDR. Combined use of AIR and CDR could provide meaningful feedback for radiologists to improve their performance by showing relative performance to other radiologists.

Biparametric MR signal characteristics can predict histopathological measures of prostate cancer

OBJECTIVES

The aim of this study was to establish a new data-driven metric from MRI signal intensity that can quantify histopathological characteristics of prostate cancer.

METHODS

This retrospective study was conducted on 488 patients who underwent biparametric MRI (bp-MRI), including T2-weighted imaging (T2W) and apparent diffusion coefficient (ADC) of diffusion-weighted imaging, and having biopsy-proven prostate cancer between August 2011 and July 2015. Forty-two of the patients who underwent radical prostatectomy and the rest of 446 patients constitute the labeled and unlabeled datasets, respectively. A deep learning model was built to predict the density of epithelium, epithelial nuclei, stroma, and lumen from bp-MRI, called MR-driven tissue density. On both the labeled validation set and the whole unlabeled dataset, the quality of MR-driven tissue density and its relation to bp-MRI signal intensity were examined with respect to different histopathologic and radiologic conditions using different statistical analyses.

RESULTS

MR-driven tissue density and bp-MRI of 446 patients were evaluated. MR-driven tissue density was significantly related to bp-MRI (p < 0.05). The relationship was generally stronger in cancer regions than in benign regions. Regarding cancer grades, significant differences were found in the intensity of bp-MRI and MR-driven tissue density of epithelium, epithelial nuclei, and stroma (p < 0.05). Comparing MR true-negative to MR false-positive regions, MR-driven lumen density was significantly different, similar to the intensity of bp-MRI (p < 0.001).

CONCLUSIONS

MR-driven tissue density could serve as a reliable histopathological measure of the prostate on bp-MRI, leading to an improved understanding of prostate cancer and cancer progression.

KEY POINTS

• Semi-supervised deep learning enables non-invasive and quantitative histopathology in the prostate from biparametric MRI. • Tissue density derived from biparametric MRI demonstrates similar characteristics to the direct estimation of tissue density from histopathology images. • The analysis of MR-driven tissue density reveals significantly different tissue compositions among different cancer grades as well as between MR-positive and MR-negative benign.

Effect of Prostate MRI Interpretation Experience on PPV Using PI-RADS Version 2: A 6-Year Assessment Among Eight Fellowship-Trained Radiologists

BACKGROUND. Understanding the effect of specific experience in prostate MRI interpretation on diagnostic performance would help inform the minimum interpretation volume to establish proficiency. OBJECTIVE. The purpose of this article is to assess for an association between increasing experience in prostate MRI interpretation and change in radiologist-level PPVs for PI-RADS version 2 (v2) categories 3, 4, and 5. METHODS. This retrospective study included prostate MRI examinations performed between July 1, 2015, and August 13, 2021, that were assigned a PI-RADS v2 category of 3, 4, or 5 and with an MRI-ultrasound fusion biopsy available as the reference standard. All examinations were among the first 100-200 prostate MRI examinations interpreted using PI-RADS v2 by fellowship-trained abdominal radiologists. Radiologists received feedback through a quality assurance program. Radiologists' experience levels were classified using progressive subsets of 50 interpreted examinations. Change with increasing experience in distribution of individual radiologists' whole-gland PPVs for Gleason sum score 7 or greater prostate cancer, stratified by PI-RADS category, was assessed by hierarchic linear mixed models. RESULTS. The study included 1300 prostate MRI examinations in 1037 patients (mean age, 66 ± 7 [SD] years), interpreted by eight radiologists (median, 13 years of postfellow-ship experience; range, 5-22 years). Aggregate PPVs were 20% (68/340) for PI-RADS category 3, 49% (318/652) for category 4, and 71% (220/308) for category 5. Interquartile ranges (IQRs) of PPVs overlapped for category 4 (51%; IQR, 42-60%) and category 5 (70%; IQR, 54-75%) for radiologists' first 50 examinations. IQRs of PPVs did not overlap between categories of greater experience; for example, at the 101-150 examination level, PPV for category 3 was 24% (IQR, 20-29%), category 4 was 55% (IQR, 54-63%), and category 5 was 81% (IQR, 77-82%). Hierarchic modeling showed no change in radiologists' absolute PPV with increasing experience (category 3, p = .27; category 4, p = .71; category 5, p = .38). CONCLUSION. Absolute PPVs at specific PI-RADS categories did not change during radiologists' first 200 included examinations. However, resolution of initial overlap in IQRs indicates improved precision of PPVs after the first 50 examinations. CLINICAL IMPACT. If implementing a minimum training threshold for fellowship-trained abdominal radiologists, 50 prostate MRI examinations may be sufficient in the context of a quality assurance program with feedback.

ACR-RADS Programs Current State and Future Opportunities: Defining a Governance Structure to Enable Sustained Success

In the spring of 2021, the ACR approved a proposal to improve the consistency, transparency, and administrative oversight of the ACR Reporting and Data Systems (RADS). A working group of experts and stakeholders was convened to draft this governance document. Major advances include (1) forming a RADS Steering Committee, (2) establishing minimum requirements and evidence standards for new and existing RADS, and (3) outlining a governance structure and communication strategy for RADS.

Prostate Imaging and Data Reporting System Version 2 as a Radiology Performance Metric: An Analysis of 18 Abdominal Radiologists

PURPOSE

To determine expected trained provider performance dispersion in Prostate Imaging and Data Reporting System version 2 (PI-RADS v2) positive predictive values (PPVs).

METHODS

This single-center quality assurance retrospective cohort study evaluated 5,556 consecutive prostate MRIs performed on 4,593 patients. Studies were prospectively interpreted from October 8, 2016, to July 31, 2020, by 18 subspecialty-trained abdominal radiologists (1-22 years' experience; median MRIs per radiologist: 232, first-to-third quartile range [Q1-Q3]: 128-440; 13 interpreted at least 30 MRIs with a reference standard). Maximum prospectively reported whole-gland PI-RADS v2 score was compared to post-MRI biopsy histopathology obtained within 2 years. The primary outcome was PPV of MRI by provider stratified by maximum whole-gland PI-RADS v2 score.

RESULTS

Median provider-level PPVs for the radiologists who interpreted ≥30 MRIs with a reference standard were PI-RADS 3 (22.1%; Q1-Q3: 10.0%-28.6%), PI-RADS 4 (49.2%; Q1-Q3: 41.4%-50.0%), PI-RADS 5 (81.8%; Q1-Q3: 77.1%-84.4%). Overall, the maximum whole-gland PI-RADS v2 score was PI-RADS 1 to 2 (34.6% [1,925]), PI-RADS 3 (8.5% [474]), PI-RADS 4 (21.0% [1,166]), PI-RADS 5 (18.3% [1,018]), no PI-RADS score (17.5% [973]). System-level (all providers) PPVs for maximum PI-RADS v2 scores were 20.0% (95% confidence interval [CI]: 15.7%-24.9%) for PI-RADS 3, 48.5% (95% CI: 44.8%-52.2%) for PI-RADS 4, and 80.1% for PI-RADS 5 (95% CI: 75.7%-83.9%).

CONCLUSION

Subspecialty-trained abdominal radiologists with a wide range of experience can obtain consistent positive predictive values for PI-RADS v2 scores of 3 to 5. These data can be used for quality assurance benchmarking.