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
Sign In
Advertise with Us
GLOBETECH PUBLISHING LLC

Download Mobile App




Machine Learning Aids Diagnosis and Prognosis of Prostate Cancer Using MRI

By MedImaging International staff writers
Posted on 27 Mar 2023
Print article
Image: New research harnesses the power of machine learning in prostate cancer imaging (Photo courtesy of UMiami Health System)
Image: New research harnesses the power of machine learning in prostate cancer imaging (Photo courtesy of UMiami Health System)

Conventional magnetic resonance imaging (MRI) is a reliable tool for prognosis, diagnosis, active surveillance, and reducing the need for biopsy procedures in lower-risk prostate cancer patients. The integration of open-source data with machine learning models has created new opportunities to study disease progression and regression, particularly in the medical field. However, effectively incorporating machine learning in patient care poses several challenges, such as optimizing machine learning approaches for specific cancers, ensuring adequate specificity of training data for a particular medical condition, and more. In this context, generative adversarial networks (GANs) are being explored as a potential solution for generating high-quality synthetic data that accurately represent the clinical variability of a condition and can be applied to a range of imaging technologies, including PET, CT, MRI, ultrasound, and X-ray imaging in the brain, abdomen, and chest. However, while there has been some success, the use of GAN models is currently limited when it comes to accurately depicting the heterogeneity of complex diseases like prostate cancer.

A team of translational researchers at the University of Miami Health System (Miami, FL, USA) is leading the way in improving GAN tools for integration with diagnostic and prognostic tools in prostate cancer research. By requiring less data and patient follow-ups, GAN has the potential to revolutionize machine learning models and reduce healthcare costs and patient discomfort associated with repeat follow-ups. The goal is to use GAN's machine learning capabilities to generate digital images that learn from previous MRI images and clinical parameters, and predict disease progression or regression patterns.

The research team conducted a study using prostate MRIs and digital pathology from multiple sources as training data to develop a GAN model. With deep learning, they trained the model to segment the prostate boundary on both MRI and histology slices, which provide microscopic tissue structures. Experts with different levels of experience evaluated the generated images against traditional MRI images of the prostate. The study demonstrated that the prostate cancer MRIs produced using the model were of high quality. Deep learning segmentation helped remove images with significant distortion, indicating that this GAN machine learning model for prostate cancer imaging has promising implications for complex patient cases.

“Timely diagnosis and assessment of prognosis are challenges for prostate cancer, and this results in many deaths and increases [risk of disease progression],” said Himanshu Arora, Ph.D., assistant professor at Sylvester and the Desai Sethi Urology Institute at the Miller School of Medicine. “We cannot replace the human eye when it comes to medical decision-making. Still, the improvement in technologies could potentially assist radiation oncologists in making timely decisions.”

“Technically, the technology developed here is the first start to building more sophisticated models of ‘data augmentation’ where new digital images can be used in further analysis. This is an early phase of our study, but the outcomes are extremely promising,” Dr. Arora added.

Related Links:
University of Miami Health System

Gold Member
Solid State Kv/Dose Multi-Sensor
AGMS-DM+
Dose Calibration Electrometer
PC Electrometer
New
PACS Workstation
CHILI Web Viewer
Imaging Table
Stille imagiQ2

Print article
Radcal

Channels

Radiography

view channel
Image: The study supports annual screening beginning at age 40 as the best way to diagnose cancer early (Photo courtesy of 123RF)

Annual Mammography Beginning At 40 Cuts Breast Cancer Mortality By 42%

Breast cancer remains a leading cause of cancer-related deaths among women in the United States. Although studies have shown that regular mammography screenings can cut breast cancer fatalities by 40%,... Read more

Ultrasound

view channel
Mindray`s comprehensive range of ultrasound machines include the Resona I9 (photo courtesy of Mindray)

Non-Invasive Ultrasound Technique Helps Identify Life-Changing Complications after Neck Surgery

Nasopharyngoscopy is an intrusive diagnostic medical procedure that involves the examination of the internal structures of the nose and throat (nasopharynx) using an endoscope inserted through the patient’s nose.... Read more

Nuclear Medicine

view channel
Image: The PET imaging technique can noninvasively detect active inflammation before clinical symptoms arise (Photo courtesy of 123RF)

New PET Tracer Detects Inflammatory Arthritis Before Symptoms Appear

Rheumatoid arthritis, the most common form of inflammatory arthritis, affects 18 million people globally. It is a complex autoimmune disease marked by chronic inflammation, leading to cartilage and bone... Read more

General/Advanced Imaging

view channel
Image: The new AI-enabled CT 5300 aims to bring confident diagnosis to more patients at low cost (Photo courtesy of Royal Philips)

AI-Enabled CT System Provides More Accurate and Reliable Imaging Results

Computed Tomography (CT) plays a critical role in diagnosing cardiac diseases. Recent research advocates a "CT first" approach for patients with chest pain and undiagnosed coronary artery disease, thus... 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

Industry News

view channel
Image: The acquisition will expand IBA’s medical imaging quality assurance offering (Photo courtesy of Radcal)

IBA Acquires Radcal to Expand Medical Imaging Quality Assurance Offering

Ion Beam Applications S.A. (IBA, Louvain-La-Neuve, Belgium), the global leader in particle accelerator technology and a world-leading provider of dosimetry and quality assurance (QA) solutions, has entered... Read more
Copyright © 2000-2024 Globetech Media. All rights reserved.