AI Medical Compendium Topic:
Prostatic Neoplasms

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What factors affect the operative time of robot-assisted laparoscopic radical prostatectomy?

Surgical endoscopy
BACKGROUND: Robot-assisted radical prostatectomy (RARP) has gained prominence since the da Vinci surgical system was introduced in 2000. RARP has now become a standard procedure for treating cases with localized prostate cancer. However, no study has...

A Deep Learning Framework Identifies Pathogenic Noncoding Somatic Mutations from Personal Prostate Cancer Genomes.

Cancer research
Our understanding of noncoding mutations in cancer genomes has been derived primarily from mutational recurrence analysis by aggregating clinical samples on a large scale. These cohort-based approaches cannot directly identify individual pathogenic n...

Automatic IMRT planning via static field fluence prediction (AIP-SFFP): a deep learning algorithm for real-time prostate treatment planning.

Physics in medicine and biology
The purpose of this work was to develop a deep learning (DL) based algorithm, Automatic intensity-modulated radiotherapy (IMRT) Planning via Static Field Fluence Prediction (AIP-SFFP), for automated prostate IMRT planning with real-time planning effi...

Subsphincteric Anastomosis During Laparoscopic Robot-Assisted Radical Prostatectomy and Its Positive Impact on Continence Recovery.

Journal of endourology
To assess the interest of a new sphincter preserving anastomosis technique for continence recovery after robot-assisted laparoscopic radical prostatectomy (RALP). : We performed a monocentric single-operator study on 187 consecutive RALP. Patients w...

High throughput assessment of biomarkers in tissue microarrays using artificial intelligence: PTEN loss as a proof-of-principle in multi-center prostate cancer cohorts.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc
Phosphatase and tensin homolog (PTEN) loss is associated with adverse outcomes in prostate cancer and has clinical potential as a prognostic biomarker. The objective of this work was to develop an artificial intelligence (AI) system for automated det...

T1 and T2 MR fingerprinting measurements of prostate cancer and prostatitis correlate with deep learning-derived estimates of epithelium, lumen, and stromal composition on corresponding whole mount histopathology.

European radiology
OBJECTIVES: To explore the associations between T1 and T2 magnetic resonance fingerprinting (MRF) measurements and corresponding tissue compartment ratios (TCRs) on whole mount histopathology of prostate cancer (PCa) and prostatitis.

Time-range based sequential mining for survival prediction in prostate cancer.

Journal of biomedical informatics
BACKGROUND AND OBJECTIVE: Metastatic prostate cancer has a higher mortality rate than localized cancers. There is a need to investigate the survival outcome of metastatic prostate cancers separately. Also, the treatments undertaken by the patients af...

Cone-beam computed tomography-based radiomics in prostate cancer: a mono-institutional study.

Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al]
PURPOSE: The purpose of the reported study was to investigate the value of cone-beam computed tomography (CBCT)-based radiomics for risk stratification and prediction of biochemical relapse in prostate cancer.

Impact of rescanning and normalization on convolutional neural network performance in multi-center, whole-slide classification of prostate cancer.

Scientific reports
Algorithms can improve the objectivity and efficiency of histopathologic slide analysis. In this paper, we investigated the impact of scanning systems (scanners) and cycle-GAN-based normalization on algorithm performance, by comparing different deep ...