Deep learning-based artificial intelligence for prostate cancer detection at biparametric MRI.

Purpose: To present fully automated DL-based prostate cancer detection system for prostate MRI.

Methods: MRI scans from two institutions, were used for algorithm training, validation, testing. MRI-visible lesions were contoured by an experienced radiologist.

A Cascaded Deep Learning-Based Artificial Intelligence Algorithm for Automated Lesion Detection and Classification on Biparametric Prostate Magnetic Resonance Imaging.

Rationale And Objectives: Prostate MRI improves detection of clinically significant prostate cancer; however, its diagnostic performance has wide variation. Artificial intelligence (AI) has the potential to assist radiologists in the detection and classification of prostatic lesions. Herein, we aimed to develop and test a cascaded deep learning detection and classification system trained on biparametric prostate MRI using PI-RADS for assisting radiologists during prostate MRI read out.

Data Augmentation and Transfer Learning to Improve Generalizability of an Automated Prostate Segmentation Model.

Deep learning applications in radiology often suffer from overfitting, limiting generalization to external centers. The objective of this study was to develop a high-quality prostate segmentation model capable of maintaining a high degree of performance across multiple independent datasets using transfer learning and data augmentation. A retrospective cohort of 648 patients who underwent prostate MRI between February 2015 and November 2018 at a single center was used for training and validation.

Deep-Learning-Based Artificial Intelligence for PI-RADS Classification to Assist Multiparametric Prostate MRI Interpretation: A Development Study.

Background: The Prostate Imaging Reporting and Data System (PI-RADS) provides guidelines for risk stratification of lesions detected on multiparametric MRI (mpMRI) of the prostate but suffers from high intra/interreader variability.

Purpose: To develop an artificial intelligence (AI) solution for PI-RADS classification and compare its performance with an expert radiologist using targeted biopsy results.

Study Type: Retrospective study including data from our institution and the publicly available ProstateX dataset.

Quantitative Characterization of the Prostatic Urethra Using MRI: Implications for Lower Urinary Tract Symptoms in Patients with Benign Prostatic Hyperplasia.

Introduction: The aim of this study was to perform a quantitative assessment of the prostate anatomy with a focus on the relation of prostatic urethral anatomic variation to urinary symptoms.

Methods: This retrospective study involved patients undergoing magnetic resonance imaging for prostate cancer who were also assessed for lower urinary tract symptoms. Volumetric segmentations were utilized to derive the in vivo prostatic urethral length and urethral trajectory in coronal and sagittal planes using a piece-wise cubic spline function to derive the angle of the urethra within the prostate.