AIMC Topic: Prostate

Clear Filters Showing 61 to 70 of 614 articles

Outcomes of robot-assisted radical prostatectomy in men after trans-urethral resection of the prostate: a matched-pair analysis.

Journal of robotic surgery
Prior history of transurethral resection of the prostate (TURP) can complicate Robot-assisted radical prostatectomy (RARP). Very few studies analyse the outcomes of RARP in men with a prior history of TURP. We analysed the oncological and functional ...

Hybrid-supervised deep learning for domain transfer 3D protoacoustic image reconstruction.

Physics in medicine and biology
. Protoacoustic imaging showed great promise in providing real-time 3D dose verification of proton therapy. However, the limited acquisition angle in protoacoustic imaging induces severe artifacts, which impairs its accuracy for dose verification. In...

Comparison of model-based versus deep learning-based image reconstruction for thin-slice T2-weighted spin-echo prostate MRI.

Abdominal radiology (New York)
PURPOSE: To compare a previous model-based image reconstruction (MBIR) with a newly developed deep learning (DL)-based image reconstruction for providing improved signal-to-noise ratio (SNR) in high through-plane resolution (1 mm) T2-weighted spin-ec...

Automated prostate gland segmentation in challenging clinical cases: comparison of three artificial intelligence methods.

Abdominal radiology (New York)
OBJECTIVE: Automated methods for prostate segmentation on MRI are typically developed under ideal scanning and anatomical conditions. This study evaluates three different prostate segmentation AI algorithms in a challenging population of patients wit...

DiagSet: a dataset for prostate cancer histopathological image classification.

Scientific reports
Cancer diseases constitute one of the most significant societal challenges. In this paper, we introduce a novel histopathological dataset for prostate cancer detection. The proposed dataset, consisting of over 2.6 million tissue patches extracted fro...

Can we predict pathology without surgery? Weighing the added value of multiparametric MRI and whole prostate radiomics in integrative machine learning models.

European radiology
OBJECTIVE: To test the ability of high-performance machine learning (ML) models employing clinical, radiological, and radiomic variables to improve non-invasive prediction of the pathological status of prostate cancer (PCa) in a large, single-institu...

RAPHIA: A deep learning pipeline for the registration of MRI and whole-mount histopathology images of the prostate.

Computers in biology and medicine
Image registration can map the ground truth extent of prostate cancer from histopathology images onto MRI, facilitating the development of machine learning methods for early prostate cancer detection. Here, we present RAdiology PatHology Image Alignm...

Critical evaluation of artificial intelligence as a digital twin of pathologists for prostate cancer pathology.

Scientific reports
Prostate cancer pathology plays a crucial role in clinical management but is time-consuming. Artificial intelligence (AI) shows promise in detecting prostate cancer and grading patterns. We tested an AI-based digital twin of a pathologist, vPatho, on...

Development of a microultrasound-based nomogram to predict extra-prostatic extension in patients with prostate cancer undergoing robot-assisted radical prostatectomy.

Urologic oncology
OBJECTIVES: To develop a microultrasound-based nomogram including clinicopathological parameters and microultrasound findings to predict the presence of extra-prostatic extension and guide the grade of nerve-sparing.

Deep-learning-based joint rigid and deformable contour propagation for magnetic resonance imaging-guided prostate radiotherapy.

Medical physics
BACKGROUND: Deep learning-based unsupervised image registration has recently been proposed, promising fast registration. However, it has yet to be adopted in the online adaptive magnetic resonance imaging-guided radiotherapy (MRgRT) workflow.