Does the Prostate Imaging-Reporting and Data System (PI-RADS) version 2 improve accuracy in reporting anterior lesions on multiparametric magnetic resonance imaging (mpMRI)?

Structured reporting in radiologic education - Potential of different PI-RADS versions in prostate MRI controlled by in-bore MR-guided biopsies

Objectives:

To evaluate the efficiency of structured reporting in radiologic education – based on the example of different PI-RADS score versions for multiparametric MRI (mpMRI) of the prostate.

Methods:

MpMRI of 688 prostate lesions in 180 patients were retrospectively reviewed by an experienced radiologist and by a student using PI-RADS V1 and V2. Data sets were reviewed for changes according to PI-RADS V2.1. The results were correlated with results obtained by MR-guided biopsy. Diagnostic potency was evaluated by ROC analysis. Sensitivity, specificity and correct-graded samples were evaluated for different cutpoints. The agreement between radiologist and student was determined for the aggregation of the PI-RADS score in three categories. The student’s time needed for evaluation was measured.

Results:

The area under curve of the ROC analysis was 0.782/0.788 (V1/V2) for the student and 0.841/0.833 (V1/V2) for the radiologist. The agreement between student and radiologist showed a Cohen‘s weighted κ coefficient of 0.495 for V1 and 0.518 for V2. Median student’s time needed for score assessment was 4:34 min for PI-RADSv1 and 2:00 min for PI-RADSv2 (p < 0.001). Re-evaluation for V2.1 changed the category in 1.4% of all ratings.

Conclusion:

The capacity of prostate cancer detection using PI-RADS V1 and V2 is dependent on the reader‘s experience. The results from the two observers indicate that structured reporting using PI-RADS and, controlled by histopathology, can be a valuable and quantifiable tool in students‘ or residents’ education. Herein, V2 was superior to V1 in terms of inter-observer agreement and time efficacy.

Advances in knowledge:

Structured reporting can be a valuable and quantifiable tool in radiologic education. Structured reporting using PI-RADS can be used by a student with good performance. PI-RADS V2 is superior to V1 in terms of inter-observer agreement and time efficacy.

Use of Radiomics to Improve Diagnostic Performance of PI-RADS v2.1 in Prostate Cancer

Objective

To investigate whether a radiomics model can help to improve the performance of PI-RADS v2.1 in prostate cancer (PCa).

Methods

This was a retrospective analysis of 203 patients with pathologically confirmed PCa or non-PCa between March 2015 and December 2016. Patients were divided into a training set (n = 141) and a validation set (n = 62). The radiomics model (Rad-score) was developed based on multi-parametric MRI including T2 weighted imaging (T2WI), diffusion weighted imaging (DWI), apparent diffusion coefficient (ADC) imaging, and dynamic contrast enhanced (DCE) imaging. The combined model involving Rad-score and PI-RADS was compared with PI-RADS for the diagnosis of PCa by using the receiver operating characteristic curve (ROC) analysis.

Results

A total of 112 (55.2%) patients had PCa, and 91 (44.8%) patients had benign lesions. For PCa versus non-PCa, the Rad-score had a significantly higher area under the ROC curve (AUC) [0.979 (95% CI, 0.940–0.996)] than PI-RADS [0.905 (0.844–0.948), P = 0.002] in the training set. However, the AUC between them was insignificant in the validation set [0.861 (0.749–0.936) vs. 0.845 (0.731–0.924), P = 0.825]. When Rad-score was added to PI-RADS, the performance of the PI-RADS was significantly improved for the PCa diagnosis (AUC = 0.989, P < 0.001 for the training set and AUC = 0.931, P = 0.038 for the validation set).

Conclusions

The radiomics based on multi-parametric MRI can help to improve the diagnostic performance of PI-RADS v2.1 in PCa.

Improved COVID-19 ICU admission and mortality outcomes following treatment with statins: a systematic review and meta-analysis

INTRODUCTION

Approximately 1% of the world population has now been infected by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes coronavirus disease 2019 (COVID-19). With cases still rising and vaccines just beginning to rollout, we are still several months away from seeing reductions in daily case numbers, hospitalisations, and mortality. Therefore, there is a still an urgent need to control the disease spread by repurposing existing therapeutics. Owing to antiviral, anti-inflammatory, immunomodulatory, and cardioprotective actions, statin therapy has been considered as a plausible approach to improve COVID-19 outcomes.

MATERIAL AND METHODS

We carried out a meta-analysis to investigate the effect of statins on 3 COVID-19 outcomes: intensive care unit (ICU) admission, tracheal intubation, and death. We systematically searched the PubMed, Web of Science, Scopus, and ProQuest databases using keywords related to our aims up to November 2, 2020. All published observational studies and randomised clinical trials on COVID-19 and statins were retrieved. Statistical analysis with random effects modelling was performed using STATA16 software.

RESULTS

The final selected studies (n = 24 studies; 32,715 patients) showed significant reductions in ICU admission (OR = 0.78, 95% CI: 0.58-1.06; n = 10; I 2 = 58.5%) and death (OR = 0.70, 95% CI: 0.55-0.88; n = 21; I 2 = 82.5%) outcomes, with no significant effect on tracheal intubation (OR = 0.79; 95% CI: 0.57-1.11; n = 7; I 2= 89.0%). Furthermore, subgroup analysis suggested that death was reduced further by in-hospital application of stains (OR = 0.40, 95% CI: 0.22-0.73, n = 3; I 2 = 82.5%), compared with pre-hospital use (OR = 0.77, 95% CI: 0.60-0.98, n = 18; I 2 = 81.8%).

CONCLUSIONS

These findings call attention to the need for systematic clinical studies to assess both pre- and in-hospital use of statins as a potential means of reducing COVID-19 disease severity, particularly in terms of reduction of ICU admission and total mortality reduction.

Active surveillance in prostate cancer management: where do we stand now?

Prostate cancer (PCa) is the most common cancer in men, with a steadily rising incidence, affecting on average one in six men during their lifetime. The increase in morbidity is related to the increasing overall life expectancy, prostate-specific antigen testing, implementation of new molecular markers for cancer detection and the more frequent application of multiparametric magnetic resonance imaging. There is growing evidence demonstrating that active surveillance (AS) is an alternative to immediate intervention in patients with very low- and low-risk prostate cancer. Ongoing reports from multiple studies have consistently demonstrated a very low rate of metastases and prostate cancer specific mortality in selected cohorts of patients. As a matter of fact, AS has been adopted by many institutions as a safe and effective management strategy. The aim of our review is to summarize the contemporary data on AS in patients affected with PCa with the intention to present the most clinically useful and pertinent AS protocols.

Comparison of PI-RADS v1 and v2 for multiparametric MRI detection of prostate cancer with whole-mount histological workup as reference standard

PURPOSE

The aim of this study was to compare Prostate Imaging Reporting and Data System (PI-RADS) versions v1 and v2 for the detection of prostate cancer (PCa) in multiparametric MRI (mpMRI) using whole-mount histological workup as reference standard.

MATERIAL AND METHODS

MRI data of 40 patients with positive transrectal ultrasound-guided biopsy were analyzed retrospectively by two blinded readers (5 and 4 years' experience) with PI-RADS v1 and v2 for cancer-suspicious lesions. Prior to radical prostatectomy, patients had undergone IRB-approved mpMRI at 3 T according to PI-RADS recommendations: T2-weighted (T2w), diffusion-weighted (DWI) and dynamic contrast-enhanced (DCE) imaging. The reference standard was provided by whole-mount sections of the prostatectomy specimens. Versions v1 and v2 were compared with respect to sensitivity and positive predictive value (PPV) per lesion. Subgroups stratified by tumor location (peripheral vs. transition zone) and aggressiveness (high vs. low grade) were also analyzed. We also evaluated the concordance of the dominant MRI sequence in v2 (DWI or T2w) and the highest individual score under v1. Interobserver agreement for PI-RADS v1 and v2 was assessed by Cohen's kappa statistics.

RESULTS

Reader 1 (R1) described 66 and Reader 2 (R2) 72 MRI lesions. The average Gleason score of 58 PCa lesions was 6.5 (range: 6 = 3 + 3 to 8 = 4 + 4), most of them (65.5%) located in the peripheral zone. PI-RADS v2 showed a trend towards lower sensitivities, but differences were not significant for both readers: R1 72.4% (v1) vs. 63.8% (v2) (P = 0.426) and R2 77.6% (v1) vs. 69.0% (v2) (P = 0.402). The trends were more pronounced in the transition zone and for low-grade cancers but remained insignificant (p-values from 0.313 to 0.691). Likewise, the apparent PPV differences, overall as well as in each zone, were not significant. Agreement between high-score v1 and dominant v2 sequence was 48% for R1 and 53% for R2. Cohen's κ of PCa detection for two readers was 0.48 for both v1 and v2.

CONCLUSION

Our findings indicate that the simplified, zone-specific approach of PI-RADS v2 (2015) for MRI assessment of prostate cancer may not necessarily be better than the original v1 criteria (2012). In specific cases, a strict interpretation of v2 criteria may even lead to false-negative findings. Therefore, the current PI-RADS criteria should be reconsidered, despite the low statistical evidence here.

Can computer-aided diagnosis assist in the identification of prostate cancer on prostate MRI? a multi-center, multi-reader investigation

For prostate cancer detection on prostate multiparametric MRI (mpMRI), the Prostate Imaging-Reporting and Data System version 2 (PI-RADSv2) and computer-aided diagnosis (CAD) systems aim to widely improve standardization across radiologists and centers. Our goal was to evaluate CAD assistance in prostate cancer detection compared with conventional mpMRI interpretation in a diverse dataset acquired from five institutions tested by nine readers of varying experience levels, in total representing 14 globally spread institutions. Index lesion sensitivities of mpMRI-alone were 79% (whole prostate (WP)), 84% (peripheral zone (PZ)), 71% (transition zone (TZ)), similar to CAD at 76% (WP, p=0.39), 77% (PZ, p=0.07), 79% (TZ, p=0.15). Greatest CAD benefit was in TZ for moderately-experienced readers at PI-RADSv2 <3 (84% vs mpMRI-alone 67%, p=0.055). Detection agreement was unchanged but CAD-assisted read times improved (4.6 vs 3.4 minutes, p<0.001). At PI-RADSv2 ≥ 3, CAD improved patient-level specificity (72%) compared to mpMRI-alone (45%, p<0.001). PI-RADSv2 and CAD-assisted mpMRI interpretations have similar sensitivities across multiple sites and readers while CAD has potential to improve specificity and moderately-experienced radiologists' detection of more difficult tumors in the center of the gland. The multi-institutional evidence provided is essential to future prostate MRI and CAD development.