AIMC Topic: Prostatic Neoplasms

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Clinical characteristics and oncological outcomes in negative multiparametric MRI patients undergoing robot-assisted radical prostatectomy.

The Prostate
BACKGROUND: Efforts are ongoing to try and find ways to reduce the number of unnecessary prostate biopsies without missing clinically significant prostate cancers (csPCa). The utility of multiparametric magnetic resonance imaging (mpMRI) in detecting...

Artificial intelligence for advance requesting of immunohistochemistry in diagnostically uncertain prostate biopsies.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc
The use of immunohistochemistry in the reporting of prostate biopsies is an important adjunct when the diagnosis is not definite on haematoxylin and eosin (H&E) morphology alone. The process is however inherently inefficient with delays while waiting...

A deep learning-based framework for segmenting invisible clinical target volumes with estimated uncertainties for post-operative prostate cancer radiotherapy.

Medical image analysis
In post-operative radiotherapy for prostate cancer, precisely contouring the clinical target volume (CTV) to be irradiated is challenging, because the cancerous prostate gland has been surgically removed, so the CTV encompasses the microscopic spread...

Discovery of primary prostate cancer biomarkers using cross cancer learning.

Scientific reports
Prostate cancer (PCa), the second leading cause of cancer death in American men, is a relatively slow-growing malignancy with multiple early treatment options. Yet, a significant number of low-risk PCa patients are over-diagnosed and over-treated wit...

ESUR/ESUI position paper: developing artificial intelligence for precision diagnosis of prostate cancer using magnetic resonance imaging.

European radiology
Artificial intelligence developments are essential to the successful deployment of community-wide, MRI-driven prostate cancer diagnosis. AI systems should ensure that the main benefits of biopsy avoidance are delivered while maintaining consistent hi...

Combining weakly and strongly supervised learning improves strong supervision in Gleason pattern classification.

BMC medical imaging
BACKGROUND: One challenge to train deep convolutional neural network (CNNs) models with whole slide images (WSIs) is providing the required large number of costly, manually annotated image regions. Strategies to alleviate the scarcity of annotated da...

Extraperitoneal Laparoscopic Versus Transperitoneal Robot-Assisted Laparoscopic Approaches for Extended Pelvic Lymph Node Dissection During Radical Prostatectomy.

Journal of laparoendoscopic & advanced surgical techniques. Part A
We aim to directly compare the feasibility and safety of extended pelvic lymph node dissection (PLND) during transperitoneal robotic-assisted radical prostatectomy (Tp-RARP) and extraperitoneal laparoscopic radical prostatectomy (Ep-LRP). We retros...

Digital Frozen Sections with Fluorescence Confocal Microscopy During Robot-assisted Radical Prostatectomy: Surgical Technique.

European urology
BACKGROUND: Robot-assisted radical prostatectomy (RARP) involves a tradeoff between oncological control and functional outcomes. Intraoperative control of surgical margins (SMs) may help in ensuring the safety of the dissection. Fluorescence confocal...

Development of a transperineal prostate biopsy robot guided by MRI-TRUS image.

The international journal of medical robotics + computer assisted surgery : MRCAS
BACKGROUND: In the transrectal ultrasound (TRUS)-guided transperineal prostate biopsy, doctors determine the biopsy target by observing the prostate region in TRUS images. However, ultrasound images with low imaging quality make doctors easy to be in...

Deep learning approach to predict lymph node metastasis directly from primary tumour histology in prostate cancer.

BJU international
OBJECTIVE: To develop a new digital biomarker based on the analysis of primary tumour tissue by a convolutional neural network (CNN) to predict lymph node metastasis (LNM) in a cohort matched for already established risk factors.