We use cookies to understand how you use our site and to improve your experience. This includes personalizing content and advertising. To learn more, click here. By continuing to use our site, you accept our use of cookies. Cookie Policy.

Features Partner Sites Information LinkXpress hp
Sign In
Advertise with Us
GLOBETECH PUBLISHING LLC

Download Mobile App




Artificial Neural Network Improves Prostate Cancer Detection

By MedImaging International staff writers
Posted on 29 Apr 2019
Print article
Image: New research hints that AI may soon make radiologists redundant (Photo courtesy of 123rf.com).
Image: New research hints that AI may soon make radiologists redundant (Photo courtesy of 123rf.com).
A new artificial intelligence (AI) system identifies and predicts the aggressiveness of prostate cancer (PC) at the same level of accuracy as experienced radiologists.

Developed at the University of California, Los Angeles (UCLA; USA), FocalNet is a convolutional neural network (CNN) that uses an algorithm with more than a million trainable variables. The CNN was trained using multi-parametric MRI (mp-MRI) scans of 417 men with PC prior to robotic-assisted laparoscopic prostatectomy (RALP). In order to learn how to classify the aggressiveness of the tumor using the Gleason score (GS), the results were compared to the actual pathology specimen. They then compared the AI system's results with readings by UCLA radiologists who had more than 10 years of experience.

The results revealed that in the free-response receiver operating characteristics (FROC) analysis for lesion detection, FocalNet showed 89.7% and 87.9% sensitivity for index lesions and clinically significant lesions, respectively. With the comparison to the prospective performance of radiologists using current diagnostic guidelines, FocalNet demonstrated a detection sensitivity for clinically significant lesions (80.5%) comparable to that of radiologists with at least 10 years of experience (83.9%). The study was presented at the IEEE International Symposium on Biomedical Imaging (ISBI), held during April 2019 in Venice (Italy).

“Multi-parametric MRI is considered the best non-invasive imaging modality for diagnosing prostate cancer. However, mp-MRI for PC diagnosis is currently limited by the qualitative or semi-quantitative interpretation criteria, leading to inter-reader variability and a suboptimal ability to assess lesion aggressiveness,” concluded senior author Kyunghyun Sung, of the UCLA department of radiology, and colleagues. “CNNs are a powerful method to automatically learn the discriminative features for various tasks, including cancer detection.”

CNN’s use a cascade of many layers of nonlinear processing units for feature extraction and conversion, with each successive layer using the output from the previous layer as input to form a hierarchical representation.

Related Links:
University of California, Los Angeles

Ultrasound Table
Women’s Ultrasound EA Table
Diagnostic Ultrasound System
MS1700C
Silver Member
X-Ray QA Meter
T3 AD Pro
New
MRI Infusion Workstation
BeneFusion MRI Station

Print article

Channels

MRI

view channel
Image: Comparison showing 3T and 7T scans for the same participant (Photo courtesy of P Simon Jones/University of Cambridge)

Ultra-Powerful MRI Scans Enable Life-Changing Surgery in Treatment-Resistant Epileptic Patients

Approximately 360,000 individuals in the UK suffer from focal epilepsy, a condition in which seizures spread from one part of the brain. Around a third of these patients experience persistent seizures... Read more

Ultrasound

view channel
Image: The new type of Sonogenetic EchoBack-CAR T cell (Photo courtesy of Longwei Liu/USC)

Smart Ultrasound-Activated Immune Cells Destroy Cancer Cells for Extended Periods

Chimeric antigen receptor (CAR) T-cell therapy has emerged as a highly promising cancer treatment, especially for bloodborne cancers like leukemia. This highly personalized therapy involves extracting... Read more

Imaging IT

view channel
Image: The new Medical Imaging Suite makes healthcare imaging data more accessible, interoperable and useful (Photo courtesy of Google Cloud)

New Google Cloud Medical Imaging Suite Makes Imaging Healthcare Data More Accessible

Medical imaging is a critical tool used to diagnose patients, and there are billions of medical images scanned globally each year. Imaging data accounts for about 90% of all healthcare data1 and, until... Read more
Copyright © 2000-2025 Globetech Media. All rights reserved.