AIMC Topic: Prostatic Neoplasms

Clear Filters Showing 831 to 840 of 1447 articles

Digital application developed to evaluate functional results following robot-assisted radical prostatectomy: App for prostate cancer.

Computer methods and programs in biomedicine
INTRODUCTION: Mobile applications ("apps") developed for smartphones and tablets are increasingly used in healthcare, allowing remote patient support or promoting self-health care. Prostate cancer (PC) screening allows for early-stage PC diagnosis, r...

Deep adaptive registration of multi-modal prostate images.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Artificial intelligence, especially the deep learning paradigm, has posed a considerable impact on cancer imaging and interpretation. For instance, fusing transrectal ultrasound (TRUS) and magnetic resonance (MR) images to guide prostate cancer biops...

The Use of Cumulative Sum Analysis to Derive Institutional and Surgeon-Specific Learning Curves for Robot-Assisted Radical Prostatectomy.

Journal of endourology
The cumulative sum (CUSUM) approach has been adopted to evaluate surgical competence in various contexts. The CUSUM method comprises sequential monitoring of cumulative differences from a target level in performance quality over time, allowing the d...

Deep learning using preoperative magnetic resonance imaging information to predict early recovery of urinary continence after robot-assisted radical prostatectomy.

International journal of urology : official journal of the Japanese Urological Association
OBJECTIVES: To investigate whether a deep learning model from magnetic resonance imaging information is an accurate method to predict the risk of urinary incontinence after robot-assisted radical prostatectomy.

Clinical impact of psoas muscle volume on the development of inguinal hernia after robot-assisted radical prostatectomy.

Surgical endoscopy
BACKGROUND: Sarcopenia, a syndrome characterized by the loss of skeletal muscle mass, has attracted attention in the field of oncology, as it reflects poor nutritional status. The present study aimed to determine the risk factors for postoperative in...

A deep learning framework for prostate localization in cone beam CT-guided radiotherapy.

Medical physics
PURPOSE: To develop a deep learning-based model for prostate planning target volume (PTV) localization on cone beam computed tomography (CBCT) to improve the workflow of CBCT-guided patient setup.

Test-retest repeatability of a deep learning architecture in detecting and segmenting clinically significant prostate cancer on apparent diffusion coefficient (ADC) maps.

European radiology
OBJECTIVES: To evaluate short-term test-retest repeatability of a deep learning architecture (U-Net) in slice- and lesion-level detection and segmentation of clinically significant prostate cancer (csPCa: Gleason grade group > 1) using diffusion-weig...

MAGPEL: an autoMated pipeline for inferring vAriant-driven Gene PanEls from the full-length biomedical literature.

Scientific reports
In spite of the efforts in developing and maintaining accurate variant databases, a large number of disease-associated variants are still hidden in the biomedical literature. Curation of the biomedical literature in an effort to extract this informat...

A deep learning method for real-time intraoperative US image segmentation in prostate brachytherapy.

International journal of computer assisted radiology and surgery
PURPOSE: This paper addresses the detection of the clinical target volume (CTV) in transrectal ultrasound (TRUS) image-guided intraoperative for permanent prostate brachytherapy. Developing a robust and automatic method to detect the CTV on intraoper...