A Systematic Review of the Diagnostic Accuracy of Deep Learning Models for the Automatic Detection, Localization, and Characterization of Clinically Significant Prostate Cancer on Magnetic Resonance Imaging.

Background And Objective: Magnetic resonance imaging (MRI) plays a critical role in prostate cancer diagnosis, but is limited by variability in interpretation and diagnostic accuracy. This systematic review evaluates the current state of deep learning (DL) models in enhancing the automatic detection, localization, and characterization of clinically significant prostate cancer (csPCa) on MRI.

Methods: A systematic search was conducted across Medline/PubMed, Embase, Web of Science, and ScienceDirect for studies published between January 2020 and September 2023.

Reference standard for the evaluation of automatic segmentation algorithms: Quantification of inter observer variability of manual delineation of prostate contour on MRI.

Purpose: The purpose of this study was to investigate the relationship between inter-reader variability in manual prostate contour segmentation on magnetic resonance imaging (MRI) examinations and determine the optimal number of readers required to establish a reliable reference standard.

Materials And Methods: Seven radiologists with various experiences independently performed manual segmentation of the prostate contour (whole-gland [WG] and transition zone [TZ]) on 40 prostate MRI examinations obtained in 40 patients. Inter-reader variability in prostate contour delineations was estimated using standard metrics (Dice similarity coefficient [DSC], Hausdorff distance and volume-based metrics).

3.0 T prostate MRI: Visual assessment of 2D and 3D T2-weighted imaging sequences using PI-QUAL score.

Purpose: Comparing 2D and 3D T2 weighted sequences in terms of image quality in 3.0 T MRI with readers of varied experiences, using PI-QUAL inspired criteria.

Methods: 91 male patients with suspected prostate cancer (PCa) underwent diagnostic prostate MRI on a 3.

Automatic segmentation of prostate zonal anatomy on MRI: a systematic review of the literature.

Objectives: Accurate zonal segmentation of prostate boundaries on MRI is a critical prerequisite for automated prostate cancer detection based on PI-RADS. Many articles have been published describing deep learning methods offering great promise for fast and accurate segmentation of prostate zonal anatomy. The objective of this review was to provide a detailed analysis and comparison of applicability and efficiency of the published methods for automatic segmentation of prostate zonal anatomy by systematically reviewing the current literature.

Robust Bayesian fusion of continuous segmentation maps.

The fusion of probability maps is required when trying to analyse a collection of image labels or probability maps produced by several segmentation algorithms or human raters. The challenge is to weight the combination of maps correctly, in order to reflect the agreement among raters, the presence of outliers and the spatial uncertainty in the consensus. In this paper, we address several shortcomings of prior work in continuous label fusion.

Automatic zonal segmentation of the prostate from 2D and 3D T2-weighted MRI and evaluation for clinical use.

An accurate zonal segmentation of the prostate is required for prostate cancer (PCa) management with MRI. The aim of this work is to present UFNet, a deep learning-based method for automatic zonal segmentation of the prostate from T2-weighted (T2w) MRI. It takes into account the image anisotropy, includes both spatial and channelwise attention mechanisms and uses loss functions to enforce prostate partition.

Prostate volume prediction on MRI: tools, accuracy and variability.

Objective: A reliable estimation of prostate volume (PV) is essential to prostate cancer management. The objective of our multi-rater study was to compare intra- and inter-rater variability of PV from manual planimetry and ellipsoid formulas.

Methods: Forty treatment-naive patients who underwent prostate MRI were selected from a local database.

Challenge of prostate MRI segmentation on T2-weighted images: inter-observer variability and impact of prostate morphology.

Background: Accurate prostate zonal segmentation on magnetic resonance images (MRI) is a critical prerequisite for automated prostate cancer detection. We aimed to assess the variability of manual prostate zonal segmentation by radiologists on T2-weighted (T2W) images, and to study factors that may influence it.

Methods: Seven radiologists of varying levels of experience segmented the whole prostate gland (WG) and the transition zone (TZ) on 40 axial T2W prostate MRI images (3D T2W images for all patients, and both 3D and 2D images for a subgroup of 12 patients).