Impact of Endorectal Coil Use on Extraprostatic Extension Detection in Prostate MRI: A Retrospective Monocentric Study.

Rationale And Objectives: Accurate preoperative mpMRI-based detection of extraprostatic extension (EPE) in prostate cancer (PCa) is critical for surgical planning and patient outcomes. This study aims to evaluate the impact of endorectal coil (ERC) use on the diagnostic performance of mpMRI in detecting EPE.

Materials And Methods: This retrospective study with prospectively collected data included participants who underwent mpMRI and subsequent radical prostatectomy for PCa between 2007 and 2024.

A Prostate Imaging-Reporting and Data System version 2.1-based predictive model for clinically significant prostate cancer diagnosis.

Objectives: To develop and validate a Prostate Imaging-Reporting and Data System (PI-RADS) version 2.1 (v2.1)-based predictive model for diagnosis of clinically significant prostate cancer (csPCa), integrating clinical and multiparametric magnetic resonance imaging (mpMRI) data, and compare its performance with existing models.

Multimodal approach to optimize biopsy decision-making for PI-RADS 3 lesions on multiparametric MRI.

Purpose: To develop and evaluate a multimodal approach including clinical parameters and biparametric MRI-based artificial intelligence (AI) model for determining the necessity of prostate biopsy in patients with PI-RADS 3 lesions.

Methods: This retrospective study included a prospectively recruited patient cohort with PI-RADS 3 lesions who underwent prostate MRI and MRI/US fusion-guided biopsy between April 2019 and February 2024 in a single institution. The study examined demographic data, PSA and PSA density (PSAD) levels, prostate volumes, prospective PI-RADS v2.

AI-ADC: Channel and Spatial Attention-Based Contrastive Learning to Generate ADC Maps from T2W MRI for Prostate Cancer Detection.

Background/objectives: Apparent Diffusion Coefficient (ADC) maps in prostate MRI can reveal tumor characteristics, but their accuracy can be compromised by artifacts related with patient motion or rectal gas associated distortions. To address these challenges, we propose a novel approach that utilizes a Generative Adversarial Network to synthesize ADC maps from T2-weighted magnetic resonance images (T2W MRI).

Methods: By leveraging contrastive learning, our model accurately maps axial T2W MRI to ADC maps within the cropped region of the prostate organ boundary, capturing subtle variations and intricate structural details by learning similar and dissimilar pairs from two imaging modalities.

Comparison of Transatlantic Recommendations for Prostate Gland Evaluation with MRI after Focal Therapy (TARGET) and Prostate Imaging after Focal Ablation (PI-FAB) for Detecting Recurrent Prostate Cancer at Prostate MRI.

Rationale And Objectives: The increasing use of focal therapy (FT) in localized prostate cancer (PCa) management requires a standardized MRI interpretation system to detect recurrent clinically significant PCa (csPCa). This pilot study evaluates the novel Transatlantic Recommendations for Prostate Gland Evaluation with MRI after Focal Therapy (TARGET) and compares its performance to that of the Prostate Imaging after Focal Ablation (PI-FAB) system.

Materials And Methods: This retrospective study included 38 patients who underwent primary FT for localized PCa, with follow-up multiparametric MRI (mpMRI) and biopsy.

Deep learning-based image quality assessment: impact on detection accuracy of prostate cancer extraprostatic extension on MRI.

Objective: To assess impact of image quality on prostate cancer extraprostatic extension (EPE) detection on MRI using a deep learning-based AI algorithm.

Materials And Methods: This retrospective, single institution study included patients who were imaged with mpMRI and subsequently underwent radical prostatectomy from June 2007 to August 2022. One genitourinary radiologist prospectively evaluated each patient using the NCI EPE grading system.

Evaluation of a Cascaded Deep Learning-based Algorithm for Prostate Lesion Detection at Biparametric MRI.

Background Multiparametric MRI (mpMRI) improves prostate cancer (PCa) detection compared with systematic biopsy, but its interpretation is prone to interreader variation, which results in performance inconsistency. Artificial intelligence (AI) models can assist in mpMRI interpretation, but large training data sets and extensive model testing are required. Purpose To evaluate a biparametric MRI AI algorithm for intraprostatic lesion detection and segmentation and to compare its performance with radiologist readings and biopsy results.

Automated Detection and Grading of Extraprostatic Extension of Prostate Cancer at MRI via Cascaded Deep Learning and Random Forest Classification.

Rationale And Objectives: Extraprostatic extension (EPE) is well established as a significant predictor of prostate cancer aggression and recurrence. Accurate EPE assessment prior to radical prostatectomy can impact surgical approach. We aimed to utilize a deep learning-based AI workflow for automated EPE grading from prostate T2W MRI, ADC map, and High B DWI.

Reducing False-Positives Due to Urinary Stagnation in the Prostatic Urethra on 18 F-DCFPyL PSMA PET/CT With MRI.

Purpose: Prostate-specific membrane antigen (PSMA)-targeting PET radiotracers reveal physiologic uptake in the urinary system, potentially misrepresenting activity in the prostatic urethra as an intraprostatic lesion. This study examined the correlation between midline 18 F-DCFPyL activity in the prostate and hyperintensity on T2-weighted (T2W) MRI as an indication of retained urine in the prostatic urethra.

Patients And Methods: Eighty-five patients who underwent both 18 F-DCFPyL PSMA PET/CT and prostate MRI between July 2017 and September 2023 were retrospectively analyzed for midline radiotracer activity and retained urine on postvoid T2W MRIs.

Evaluating Diagnostic Accuracy and Inter-reader Agreement of the Prostate Imaging After Focal Ablation Scoring System.

Background And Objective: Focal therapy (FT) is increasingly recognized as a promising approach for managing localized prostate cancer (PCa), notably reducing treatment-related morbidities. However, post-treatment anatomical changes present significant challenges for surveillance using current imaging techniques. This study aimed to evaluate the inter-reader agreement and efficacy of the Prostate Imaging after Focal Ablation (PI-FAB) scoring system in detecting clinically significant prostate cancer (csPCa) on post-FT multiparametric magnetic resonance imaging (mpMRI).

Deep Learning-Based Detection and Classification of Bone Lesions on Staging Computed Tomography in Prostate Cancer: A Development Study.

Rationale And Objectives: Efficiently detecting and characterizing metastatic bone lesions on staging CT is crucial for prostate cancer (PCa) care. However, it demands significant expert time and additional imaging such as PET/CT. We aimed to develop an ensemble of two automated deep learning AI models for 1) bone lesion detection and segmentation and 2) benign vs.

Quality of T2-weighted MRI re-acquisition versus deep learning GAN image reconstruction: A multi-reader study.

Purpose: To evaluate CycleGAN's ability to enhance T2-weighted image (T2WI) quality.

Method: A CycleGAN algorithm was used to enhance T2WI quality. 96 patients (192 scans) were identified from patients who underwent multiple axial T2WI due to poor quality on the first attempt (RAD1) and improved quality on re-acquisition (RAD2).

Deep Learning-Based Interpretable AI for Prostate T2W MRI Quality Evaluation.

Rationale And Objectives: Prostate MRI quality is essential in guiding prostate biopsies. However, assessment of MRI quality is subjective with variation. Quality degradation sources exert varying impacts based on the sequence under consideration, such as T2W versus DWI.

Deep Learning-Based T2-Weighted MR Image Quality Assessment and Its Impact on Prostate Cancer Detection Rates.

Background: Image quality evaluation of prostate MRI is important for successful implementation of MRI into localized prostate cancer diagnosis.

Purpose: To examine the impact of image quality on prostate cancer detection using an in-house previously developed artificial intelligence (AI) algorithm.

Study Type: Retrospective.

Is prostatic adenocarcinoma with cribriform architecture more difficult to detect on prostate MRI?

Background: Cribriform (CBFM) pattern on prostate biopsy has been implicated as a predictor for high-risk features, potentially leading to adverse outcomes after definitive treatment. This study aims to investigate whether the CBFM pattern containing prostate cancers (PCa) were associated with false negative magnetic resonance imaging (MRI) and determine the association between MRI and histopathological disease burden.

Methods: Patients who underwent multiparametric magnetic resonance imaging (mpMRI), combined 12-core transrectal ultrasound (TRUS) guided systematic (SB) and MRI/US fusion-guided biopsy were retrospectively queried for the presence of CBFM pattern at biopsy.

Comparison of MRI-Based Staging and Pathologic Staging for Predicting Biochemical Recurrence of Prostate Cancer After Radical Prostatectomy.

Currently most clinical models for predicting biochemical recurrence (BCR) of prostate cancer (PCa) after radical prostatectomy (RP) incorporate staging information from RP specimens, creating a gap in preoperative risk assessment. The purpose of our study was to compare the utility of presurgical staging information from MRI and postsurgical staging information from RP pathology in predicting BCR in patients with PCa. This retrospective study included 604 patients (median age, 60 years) with PCa who underwent prostate MRI before RP from June 2007 to December 2018.

Prospective Evaluation of PI-RADS Version 2.1 for Prostate Cancer Detection and Investigation of Multiparametric MRI-derived Markers.

Background Data regarding the prospective performance of Prostate Imaging Reporting and Data System (PI-RADS) version 2.1 alone and in combination with quantitative MRI features for prostate cancer detection is limited. Purpose To assess lesion-based clinically significant prostate cancer (csPCa) rates in different PI-RADS version 2.

Prostate MRI and image Quality: It is time to take stock.

Multiparametric magnetic resonance imaging (mpMRI) plays a vital role in prostate cancer diagnosis and management. With the increase in use of mpMRI, obtaining the best possible quality images has become a priority. The Prostate Imaging Reporting and Data System (PI-RADS) was introduced to standardize and optimize patient preparation, scanning techniques, and interpretation.