AIMC Topic: Prostatic Neoplasms

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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 ...

Deep Learning Prostate MRI Segmentation Accuracy and Robustness: A Systematic Review.

Radiology. Artificial intelligence
Purpose To investigate the accuracy and robustness of prostate segmentation using deep learning across various training data sizes, MRI vendors, prostate zones, and testing methods relative to fellowship-trained diagnostic radiologists. Materials and...

The expanding role of artificial intelligence in the histopathological diagnosis in urological oncology: a literature review.

Folia medica
The ongoing growth of artificial intelligence (AI) involves virtually every aspect of oncologic care in medicine. Although AI is in its infancy, it has shown great promise in the diagnosis of oncologic urological conditions. This paper aims to explor...

Mathematical Model-Driven Deep Learning Enables Personalized Adaptive Therapy.

Cancer research
UNLABELLED: Standard-of-care treatment regimens have long been designed for maximal cell killing, yet these strategies often fail when applied to metastatic cancers due to the emergence of drug resistance. Adaptive treatment strategies have been deve...

Artificial intelligence-based algorithms for the diagnosis of prostate cancer: A systematic review.

American journal of clinical pathology
OBJECTIVES: The high incidence of prostate cancer causes prostatic samples to significantly affect pathology laboratories workflow and turnaround times (TATs). Whole-slide imaging (WSI) and artificial intelligence (AI) have both gained approval for p...

Applications of Artificial Intelligence in Prostate Cancer Care: A Path to Enhanced Efficiency and Outcomes.

American Society of Clinical Oncology educational book. American Society of Clinical Oncology. Annual Meeting
The landscape of prostate cancer care has rapidly evolved. We have transitioned from the use of conventional imaging, radical surgeries, and single-agent androgen deprivation therapy to an era of advanced imaging, precision diagnostics, genomics, and...

Prostate Cancer Risk Stratification by Digital Histopathology and Deep Learning.

JCO clinical cancer informatics
PURPOSE: Prostate cancer (PCa) represents a highly heterogeneous disease that requires tools to assess oncologic risk and guide patient management and treatment planning. Current models are based on various clinical and pathologic parameters includin...