Recent Developments in Speeding up Prostate MRI

Multiparametric MRI lesion dimension as a significant predictor of positive surgical margins following laparoscopic radical prostatectomy for transitional zone prostate cancer

BACKGROUND

Positive surgical margins (PSM) after laparoscopic radical prostatectomy are a critical factor influencing treatment outcomes and prognosis in prostate cancer. Optional treatment strategies (neoadjuvant therapy, surgical techniques) and intraoperative margin monitoring highlight the importance of PSM risk assessment. This study aims to evaluate the potential PSM risk in transitional zone (TZ) tumors.

MATERIALS AND METHODS

This retrospective study included 434 patients who underwent laparoscopic radical prostatectomy after multiparametric magnetic resonance imaging at our center between 2019 and 2023.

RESULTS

The PSM rate was significantly higher in patients with TZ lesions compared to those with peripheral zone lesions (47%, n = 175 vs. 28%, n = 226, p < 0.01). Lesion location in TZ (OR: 4.29, 97.5% CI: 2.60-7.23, p < 0.01) was identified as independent risk factors for PSM. Further analysis identified largest dimension of lesions (OR: 1.27, 97.5% CI: 1.09-1.50, p < 0.01) and the number of positive biopsy cores (OR: 1.39, 97.5% CI: 1.16-1.70, p < 0.01) as independent risk factors for PSM in patients with TZ tumors. LASSO regression identified four significant variables (largest dimension of lesions-the most important variable, number of positive biopsy cores, prostate-specific antigen density, and International Society of Urological Pathology grade). These variables were used to construct three PSM risk prediction models, each demonstrating favorable predictive accuracy and clinical benefit.

CONCLUSIONS

Certain TZ prostate cancer patients demonstrate a higher predisposition to PSM occurrence. Lesion dimension as a significant predictor of PSM for TZ patients. Separate PSM risk assessments for subgroups, like TZ prostate cancer patients, may enhance predictive accuracy and clinical utility.

CLINICAL TRIAL REGISTRATION

China Clinical Trial Registry (ChiCTR2300075944, 2023).

Accelerated diffusion-weighted imaging of the prostate employing echo planar imaging with compressed SENSE based reconstruction

Aim was to evaluate accelerated diffusion-weighted imaging (DWI) of the prostate using echo planar imaging with compressed SENSE based reconstruction (EPICS) and assess its performance in comparison to conventional DWI with parallel imaging. In this single-center, prospective study, 35 men with clinically suspected prostate cancer underwent prostate MRI at 3T. In each patient, two different DWI sequences, one with 3 b-values (b = 100, 400, 800s/mm²) for ADC-calculation and one with b = 1500s/mm², were acquired with conventional SENSE and with EPICS. Quantitative evaluation was done by regions-of-interest (ROIs) analysis of prostate lesions and normal appearing peripheral zones (PZ). Apparent contrast-to-noise (aCNR) and apparent signal-to-noise ratios (aSNR) were calculated. Mean ADC and coefficient of variation (CV) of ADC were compared. For qualitative assessment, artifacts, lesion conspicuity, and overall image quality were rated using a 5-point-Likert-scale (1: nondiagnostic to 5: excellent). Additionally, the Prostate Imaging Reporting and Data System (PIRADS 2.1) was rated for DWI. The average total scan time reduction with EPICS was 43%. Quantitative analysis showed no significant differences between conventional SENSE and EPICS, neither for aSNRLesion (e.g. b1500conv: 24.37 ± 10.28 vs. b1500EPICS: 24.08 ± 12.2; p = 0.98) and aCNRLesion (e.g. b1500conv:9.53 ± 7.22 vs. b1500EPICS:8.88 ± 6.16; p = 0.55) nor for aSNRPZ (e.g. b1500conv:15.18 ± 6.48 vs. b1500EPICS: 15 ± 7.4; p = 0.94). Rating of artifacts, lesion conspicuity, overall image quality and PIRADS-scores yielded comparable results for the two techniques (e.g. lesion conspicuity for ADCconv: 4(2-5) vs. ADCEPICS 4(2-5); p = 0.99 and for b1500conv: 4(2-5) vs. b1500EPICS 4(2-5); p = 0.25). Overall, accelerated DWI of the prostate using EPICS significantly reduced acquisition time without compromising image quality compared to conventional DWI.

The scientific evidence of commercial AI products for MRI acceleration: a systematic review

OBJECTIVES

This study explores the methods employed by commercially available AI products to accelerate MRI protocols and investigates the strength of their diagnostic image quality assessment.

MATERIALS AND METHODS

All commercial AI products for MRI acceleration were identified from the exhibitors presented at the RSNA 2023 and ECR 2024 annual meetings. Peer-reviewed scientific articles describing validation of clinical performance were searched for each product. Information was extracted regarding the MRI acceleration technique, achieved acceleration, diagnostic performance metrics, test cohort, and hallucinatory artifacts. The strength of the diagnostic image quality was assessed using scientific evidence levels ranging from "product's technical feasibility for clinical purposes" to "product's economic impact on society".

RESULTS

Out of 1046 companies, 14 products of 14 companies were included. No scientific articles were found for four products (29%). For the remaining ten products (71%), 21 articles were retrieved. Four acceleration methods were identified: noise reduction, raw data reconstruction, personalized scanning protocols, and synthetic image generation. Only a limited number of articles prospectively demonstrated impact on patient outcomes (n = 4, 19%), and no articles discussed an evaluation in a prospective cohort of > 100 patients or performed an economic analysis. None of the articles performed an analysis of hallucinatory artifacts.

CONCLUSION

Currently, commercially available AI products for MRI acceleration can be categorized into four main methods. The acceleration methods lack prospective scientific evidence on clinical performance in large cohorts and economic analysis, which would help to get a better insight into their diagnostic performance and enable safe and effective clinical implementation.

KEY POINTS

Question There is a growing interest in AI products that reduce MRI scan time, but an overview of these methods and their scientific evidence is missing. Findings Only a limited number of articles (n = 4, 19%) prospectively demonstrated the impact of the software for accelerating MRI on diagnostic performance metrics. Clinical relevance Although various commercially available products shorten MRI acquisition time, more studies in large cohorts are needed to get a better insight into the diagnostic performance of AI-constructed MRI.

Prostate Cancer Classification and Interpretation With Multiparametric Magnetic Resonance Imaging and Gleason Grade Score Using DarkNet53 Model

BACKGROUND

Prostate Cancer (PCa) increases the mortality rate of males worldwide and is caused by genetics, lifestyle, and age reasons. The existing automated PCa classification systems face difficulties with overfitting issues, and non-generalizability, leading to poor classification performance.

OBJECTIVE

On this account, this study proposes an automated classification of PCa from MRI images using a hybrid weighted mean of vectors-optimized DarkNet53 classifier model.

METHODOLOGY

The proposed method suggests nonlocal mean filtering for noise reduction, N4ITK bias field correction to enhance image quality, and active contour-based segmentation for accurately identifying the disease region. The feature extraction utilizes the gray level run length matrix and shape features for effective feature extraction. A weighted mean of vectors optimization is used to optimize the feature selection process by hybridizing it with the DarkNet53 model for classification. Finally, the interpretation of achieving the classification has been demonstrated using the explainable AI Grad-CAM model.

RESULTS

After comparing the proposed work with various state-of-the-art algorithms, the proposed model achieves 99.31% accuracy, 98.24% sensitivity, and 98.46% specificity, respectively, highlighting the model's accomplishment using the DarkNet53 classifier.

Using deep learning to optimize the prostate MRI protocol by assessing the diagnostic efficacy of MRI sequences

PURPOSE

To explore diagnostic deep learning for optimizing the prostate MRI protocol by assessing the diagnostic efficacy of MRI sequences.

METHOD

This retrospective study included 840 patients with a biparametric prostate MRI scan. The MRI protocol included a T2-weighted image, three DWI sequences (b50, b400, and b800 s/mm2), a calculated ADC map, and a calculated b1400 sequence. Two accelerated MRI protocols were simulated, using only two acquired b-values to calculate the ADC and b1400. Deep learning models were trained to detect prostate cancer lesions on accelerated and full protocols. The diagnostic performances of the protocols were compared on the patient-level with the area under the receiver operating characteristic (AUROC), using DeLong's test, and on the lesion-level with the partial area under the free response operating characteristic (pAUFROC), using a permutation test. Validation of the results was performed among expert radiologists.

RESULTS

No significant differences in diagnostic performance were found between the accelerated protocols and the full bpMRI baseline. Omitting b800 reduced 53% DWI scan time, with a performance difference of + 0.01 AUROC (p = 0.20) and -0.03 pAUFROC (p = 0.45). Omitting b400 reduced 32% DWI scan time, with a performance difference of -0.01 AUROC (p = 0.65) and + 0.01 pAUFROC (p = 0.73). Multiple expert radiologists underlined the findings.

CONCLUSIONS

This study shows that deep learning can assess the diagnostic efficacy of MRI sequences by comparing prostate MRI protocols on diagnostic accuracy. Omitting either the b400 or the b800 DWI sequence can optimize the prostate MRI protocol by reducing scan time without compromising diagnostic quality.