AIMC Topic: Prostatic Neoplasms

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Application of deep learning for semantic segmentation in robotic prostatectomy: Comparison of convolutional neural networks and visual transformers.

Investigative and clinical urology
PURPOSE: Semantic segmentation is a fundamental part of the surgical application of deep learning. Traditionally, segmentation in vision tasks has been performed using convolutional neural networks (CNNs), but the transformer architecture has recentl...

External Validation of a Previously Developed Deep Learning-based Prostate Lesion Detection Algorithm on Paired External and In-House Biparametric MRI Scans.

Radiology. Imaging cancer
Purpose To evaluate the performance of an artificial intelligence (AI) model in detecting overall and clinically significant prostate cancer (csPCa)-positive lesions on paired external and in-house biparametric MRI (bpMRI) scans and assess performanc...

The uropathologist of the future: getting ready with intelligence for the prostate cancer tsunami.

Pathologica
According to the recently published paper by the Lancet Commission on prostate cancer (PCa) , the projections of new cases of PCa will rise from 1.4 million in 2020 to 2.9 million by 2040. Such a rise cannot be prevented by public health intervention...

Deep Learning Features Can Improve Radiomics-Based Prostate Cancer Aggressiveness Prediction.

JCO clinical cancer informatics
PURPOSE: Emerging evidence suggests that the use of artificial intelligence can assist in the timely detection and optimization of therapeutic approach in patients with prostate cancer. The conventional perspective on radiomics encompassing segmentat...

Deep Learning-based Unsupervised Domain Adaptation via a Unified Model for Prostate Lesion Detection Using Multisite Biparametric MRI Datasets.

Radiology. Artificial intelligence
Purpose To determine whether the unsupervised domain adaptation (UDA) method with generated images improves the performance of a supervised learning (SL) model for prostate cancer (PCa) detection using multisite biparametric (bp) MRI datasets. Materi...

Assessing the Performance of Deep Learning for Automated Gleason Grading in Prostate Cancer.

Studies in health technology and informatics
Prostate cancer is a dominant health concern calling for advanced diagnostic tools. Utilizing digital pathology and artificial intelligence, this study explores the potential of 11 deep neural network architectures for automated Gleason grading in pr...

Machine learning enables pan-cancer identification of mutational hotspots at persistent CTCF binding sites.

Nucleic acids research
CCCTC-binding factor (CTCF) is an insulator protein that binds to a highly conserved DNA motif and facilitates regulation of three-dimensional (3D) nuclear architecture and transcription. CTCF binding sites (CTCF-BSs) reside in non-coding DNA and are...

Utilization of machine learning methods for prediction of acute and late rectal toxicity due to curative prostate radiotherapy.

Radiation protection dosimetry
OBJECTIVE: Rectal toxicity is one of the primary dose-limiting side effects of prostate cancer radiotherapy, and consequential impairment on quality of life in these patients with long survival is an important problem. In this study, we aimed to eval...

Advancements in artificial intelligence for robotic-assisted radical prostatectomy in men suffering from prostate cancer: results from a scoping review.

Chinese clinical oncology
BACKGROUND: Robotic-assisted radical prostatectomy (RARP) is currently a first-line treatment option for men with localized prostate cancer (PCa), at least 10 years of life expectancy, and candidate for curative treatment. We performed a scoping revi...