Evaluating Artificial Intelligence-Assisted Prostate Biparametric MRI Interpretation: An International Multireader Study.

Variability in prostate biparametric MRI (bpMRI) interpretation limits diagnostic reliability for prostate cancer (PCa). Artificial intelligence (AI) has potential to reduce this variability and improve diagnostic accuracy. The objective of this study was to evaluate impact of a deep learning AI model on lesion- and patient-level clinically significant PCa (csPCa) and PCa detection rates and interreader agreement in bpMRI interpretations.

Restriction Spectrum Imaging as a Quantitative Biomarker for Prostate Cancer With Reliable Positive Predictive Value.

Purpose: The positive predictive value of the Prostate Imaging Reporting and Data System (PI-RADS) for clinically significant prostate cancer (csPCa, grade group [GG] ≥2) varies widely between radiologists. The restriction spectrum imaging restriction score (RSIrs) is a biophysics-based metric derived from diffusion MRI that could be an objectively interpretable biomarker for csPCa. We aimed to evaluate performance of RSIrs for patient-level detection of csPCa in a large and heterogenous dataset, and to combine RSIrs with clinical and imaging parameters for csPCa detection.

Multidisciplinary Consensus Prostate Contours on Magnetic Resonance Imaging: Educational Atlas and Reference Standard for Artificial Intelligence Benchmarking.

Purpose: Evaluation of artificial intelligence (AI) algorithms for prostate segmentation is challenging because ground truth is lacking. We aimed to: (1) create a reference standard data set with precise prostate contours by expert consensus, and (2) evaluate various AI tools against this standard.

Methods And Materials: We obtained prostate magnetic resonance imaging cases from six institutions from the Qualitative Prostate Imaging Consortium.

Advanced Restriction Imaging and Reconstruction Technology for Prostate Magnetic Resonance Imaging (ART-Pro): A Study Protocol for a Multicenter, Multinational Trial Evaluating Biparametric Magnetic Resonance Imaging and Advanced, Quantitative Diffusion Magnetic Resonance Imaging for the Detection of Prostate Cancer.

Multiparametric magnetic resonance imaging (mpMRI) is strongly recommended by current clinical guidelines for improved detection of clinically significant prostate cancer (csPCa). However, the major limitations are the need for intravenous (IV) contrast and dependence on reader expertise. Efforts to address these issues include use of biparametric magnetic resonance imaging (bpMRI) and advanced, quantitative magnetic resonance imaging (MRI) techniques.

Beyond Do No Harm: Introduction to Green Radiology.

In 2021, the Human Rights Council declared that having a clean, healthy, and sustainable environment is a human right. According to the WHO, 24% of deaths are attributable to environmental health risks and are largely preventable. Current predictions show that rising emissions will be linked to an enormous healthcare burden, especially for high-risk populations and historically disadvantaged communities.

Quantitative Prostate MRI, From the Special Series on Quantitative Imaging.

Prostate MRI has traditionally relied on qualitative interpretation. However, quantitative components hold the potential to markedly improve performance. The ADC from DWI is probably the most widely recognized quantitative MRI biomarker and has shown strong discriminatory value for clinically significant prostate cancer as well as for recurrent cancer after treatment.

Reasons for Discordance between Ga-PSMA-PET and Magnetic Resonance Imaging in Men with Metastatic Prostate Cancer.

Background: PSMA PET has emerged as a "gold standard" imaging modality for assessing prostate cancer metastases. However, it is not universally available, and this limits its impact. In contrast, whole-body MRI is much more widely available but misses more lesions.

MRI-Ultrasound Fused Approach for Prostate Biopsy-How It Is Performed.

The use of MRI-ultrasound image fusion targeted biopsy of the prostate in the face of an elevated serum PSA is now recommended by multiple societies, and results in improved detection of clinically significant cancer and, potentially, decreased detection of indolent disease. This combines the excellent sensitivity of MRI for clinically significant prostate cancer and the real-time biopsy guidance and confirmation of ultrasound. Both transperineal and transrectal approaches can be implemented using cognitive fusion, mechanical fusion with an articulated arm and electromagnetic registration, or pure software registration.

Interpretation of Prostate Magnetic Resonance Imaging Using Prostate Imaging and Data Reporting System Version 2.1: A Primer.

Prostate magnetic resonance imaging (MRI) is increasingly being used to diagnose and stage prostate cancer. The Prostate Imaging and Data Reporting System (PI-RADS) version 2.1 is a consensus-based reporting system that provides a standardized and reproducible method for interpreting prostate MRI.

Management of Patients With a Negative Multiparametric Prostate MRI Examination: Expert Panel Narrative Review.

Multiparametric MRI (mpMRI) of the prostate aids risk stratification of patients with elevated PSA levels. Although most clinically significant prostate cancers are detected by mpMRI, insignificant cancers are less evident. Thus, multiple international prostate cancer guidelines now endorse routine use of prostate MRI as a secondary screening test before prostate biopsy.

Partial-gland Cryoablation Outcomes for Localized Prostate Cancer in Patients with Magnetic Resonance Imaging (MRI)-visible and MRI-invisible Lesions.

Background: Expert consensus recommends treatment of magnetic resonance imaging (MRI)-visible prostate cancer (PCa). Outcomes of partial-gland ablation (PGA) for MRI-invisible PCa remain unknown.

Objective: To compare recurrence-free survival, adverse events, and health-related quality of life (HRQoL) outcomes following cryoablation of MRI-visible vs invisible PCa.

The role of AI in prostate MRI quality and interpretation: Opportunities and challenges.

Prostate MRI plays an important role in imaging the prostate gland and surrounding tissues, particularly in the diagnosis and management of prostate cancer. With the widespread adoption of multiparametric magnetic resonance imaging in recent years, the concerns surrounding the variability of imaging quality have garnered increased attention. Several factors contribute to the inconsistency of image quality, such as acquisition parameters, scanner differences and interobserver variabilities.

Pulse Sequence Dependence of a Simple and Interpretable Deep Learning Method for Detection of Clinically Significant Prostate Cancer Using Multiparametric MRI.

Rationale And Objectives: Multiparametric magnetic resonance imaging (mpMRI) is increasingly used for risk stratification and localization of prostate cancer (PCa). Thanks to the great success of deep learning models in computer vision, the potential application for early detection of PCa using mpMRI is imminent.

Materials And Methods: Deep learning analysis of the PROSTATEx dataset.

Variability of Prostate MRI Charges Among U.S. Hospital-Based Facilities.

The Premier Healthcare Database was used to assess charge variation for prostate MRI examinations in U.S. hospitals from January 2010 to March 2020.

Pilot study of the diagnostic utility of Zr-df-IAB2M and Ga-PSMA-11 PET imaging and multiparametric MRI in localized prostate cancer.

Background: Accurate diagnosis of localized prostate cancer (PCa) is limited by inadequacy of multiparametric (mp) MRI to fully identify and differentiate localized malignant tissue from benign pathologies. Prostate-specific membrane antigen (PSMA) represents an excellent target for molecular imaging. IAB2M, an 85-kD minibody derived from a de-immunized monoclonal antibody directed at the extracellular domain of human PSMA (huJ591), and PSMA-11, a small molecule ligand have been previously tested as probes for visualization of recurrent/metastatic PCa with PET/CT.

Magnetic Resonance Imaging Radiomics-Based Machine Learning Prediction of Clinically Significant Prostate Cancer in Equivocal PI-RADS 3 Lesions.

Background: While Prostate Imaging Reporting and Data System (PI-RADS) 4 and 5 lesions typically warrant prostate biopsy and PI-RADS 1 and 2 lesions may be safely observed, PI-RADS 3 lesions are equivocal.

Purpose: To construct and cross-validate a machine learning model based on radiomics features from T -weighted imaging (T WI) of PI-RADS 3 lesions to identify clinically significant prostate cancer (csPCa), that is, pathological Grade Group ≥ 2.

Study Type: Single-center retrospective study.