AIMC Topic: Prostatic Neoplasms

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Prostate Cancer Risk Stratification via Nondestructive 3D Pathology with Deep Learning-Assisted Gland Analysis.

Cancer research
Prostate cancer treatment planning is largely dependent upon examination of core-needle biopsies. The microscopic architecture of the prostate glands forms the basis for prognostic grading by pathologists. Interpretation of these convoluted three-dim...

Does prior transurethral resection of prostate affect the functional and oncological outcomes of robot-assisted radical prostatectomy? A matched-pair analysis.

Journal of robotic surgery
Robot-assisted radical prostatectomy (RARP) is challenging in men with prior history of transurethral resection of the prostate (TURP). Few studies analyze this peculiar group of patients, and hence we sought to investigate the outcome of RARP in pos...

Magnetic Resonance Imaging Image Feature Analysis Algorithm under Convolutional Neural Network in the Diagnosis and Risk Stratification of Prostate Cancer.

Journal of healthcare engineering
This work aimed to explore the accuracy of magnetic resonance imaging (MRI) images based on the convolutional neural network (CNN) algorithm in the diagnosis of prostate cancer patients and tumor risk grading. A total of 89 patients with prostate can...

Concurrently bendable and rotatable continuum tubular robot for omnidirectional multi-core transurethral prostate biopsy.

Medical & biological engineering & computing
A transurethral prostate biopsy device is proposed in this paper, which can shoot a biopsy needle at different angles to take samples from multiple locations within the prostate. Firstly, the traditional prostate biopsy methods, including transrectal...

Deep learning-assisted prostate cancer detection on bi-parametric MRI: minimum training data size requirements and effect of prior knowledge.

European radiology
OBJECTIVES: To assess Prostate Imaging Reporting and Data System (PI-RADS)-trained deep learning (DL) algorithm performance and to investigate the effect of data size and prior knowledge on the detection of clinically significant prostate cancer (csP...

Development and validation of the 3D U-Net algorithm for segmentation of pelvic lymph nodes on diffusion-weighted images.

BMC medical imaging
BACKGROUND: The 3D U-Net model has been proved to perform well in the automatic organ segmentation. The aim of this study is to evaluate the feasibility of the 3D U-Net algorithm for the automated detection and segmentation of lymph nodes (LNs) on pe...