AI Medical Compendium Topic:
Prostatic Neoplasms

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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...

Fully Automated Deep Learning Model to Detect Clinically Significant Prostate Cancer at MRI.

Radiology
Background Multiparametric MRI can help identify clinically significant prostate cancer (csPCa) (Gleason score ≥7) but is limited by reader experience and interobserver variability. In contrast, deep learning (DL) produces deterministic outputs. Purp...

[Research Progress of Artificial Intelligence in Prostate Cancer Diagnosis Application].

Zhongguo yi liao qi xie za zhi = Chinese journal of medical instrumentation
With the continuous advancement of artificial intelligence in the field of prostate cancer research, numerous studies have shown that AI performance can rival that of physicians. This review examines the recent applications and developments of AI in ...

THGNCDA: circRNA-disease association prediction based on triple heterogeneous graph network.

Briefings in functional genomics
Circular RNAs (circRNAs) are a class of noncoding RNA molecules featuring a closed circular structure. They have been proved to play a significant role in the reduction of many diseases. Besides, many researches in clinical diagnosis and treatment of...

Deep Learning Classification of Prostate Cancer on Confidently Labeled Micro-Ultrasound Images.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Micro-ultrasound is a high-resolution ultrasound technology that has recently been introduced as an inexpensive alternative to MRI for prostate cancer identification. However, it is difficult to correlate micro-ultrasound imaging with MRI and ground ...