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

Download Mobile App

AI Technique Automatically Traces Tumors in Large MRI Datasets to Guide Real-time Glioblastoma Treatment

By MedImaging International staff writers
Posted on 16 Nov 2023
Print article
Image: The researchers are using MRI-guided radiation therapy that pairs daily MRIs with radiation treatment (Photo courtesy of Sylvester)
Image: The researchers are using MRI-guided radiation therapy that pairs daily MRIs with radiation treatment (Photo courtesy of Sylvester)

Treating glioblastoma, a prevalent and aggressive brain cancer, involves the use of radiation therapy guided by CT imaging. While this method is effective in targeting radiation, it doesn't provide real-time information about the tumor's response to the treatment. This gap means that clinicians are unable to determine whether a patient's cancer is responding to the treatment or progressing until follow-up images are taken, sometimes months later. Given the rapid progression of glioblastoma, such delays can have critical consequences.

To address this challenge, a team of researchers at Sylvester Comprehensive Cancer Center, part of the University of Miami Miller School of Medicine, (Coral Gables, FL, USA) is utilizing a technique called MRI-guided radiation therapy. This method integrates daily MRI scans with radiation treatments. MRI technology illuminates the brain tumor, assisting in guiding the radiation beams. Importantly, the detailed images produced by MRIs also offer the possibility of near real-time monitoring of the tumor's response or progression. However, this advanced approach generates a substantial volume of data. For the 36 patients with glioblastoma in their study, each of the 31 time points included between four and six distinct images. To efficiently analyze this wealth of information, the team has employed artificial intelligence.

The AI-based solution developed by the researchers automatically delineates glioblastoma tumors and the resection cavities—spaces remaining after surgical removal of tumors—within these extensive MRI datasets. This automated tracing of tumors and cavities allows for tracking of tumor growth or reduction throughout the treatment. The algorithm, an adaptation from previous work in cervical cancer, can swiftly calculate the precise volume of the tumor and track changes over time. This AI method also offers a significant reduction in time compared to manual analysis, which can take over 20 hours per patient. The AI can process the same data in approximately 90 minutes.

Looking forward, the team plans to enhance the machine learning approach to include additional data from the MRI images. A key focus is identifying pseudo-progression, a condition where the tumor appears to grow due to treatment-induced swelling but ultimately recedes. This distinction between actual tumor growth and pseudo-progression is a crucial but challenging aspect of the research. The researchers are designing a study to evaluate tumor progression in glioblastoma patients undergoing MRI-guided radiation therapy on a weekly basis. They aim to adjust treatments in real-time based on the response of the tumors or changes in their size, utilizing the new machine learning method to facilitate swift treatment modifications.

“You can monitor so many different qualities of the tumor with MRI. That’s an untapped frontier,” said Adrian Breto, a doctoral student and programmer. “We haven’t gone yet to the center of the earth as far as what MRI can tell us about the patient’s disease and quality of life. That’s what we’re trying to do, squeeze as much information as we can out of these images for the benefit of the patient.”

Related Links:
Miller School of Medicine 

Gold Supplier
Conductive Gel
Gold Supplier
Ultrasound System
Bladder Scanner
Mobile Radiographic System

Print article



view channel
Image: Intelligent NR provides high-quality diagnostic images containing significantly less grainy noise (Photo courtesy of Canon)

AI-Driven DR System Produces Higher Quality Images While Limiting Radiation Doses in Pediatric Patients

Ionizing radiation is a fundamental element in producing diagnostic X-rays, yet it's widely acknowledged for its cancer risk potential. Digital projection radiography, a vital imaging modality, accounts... Read more


view channel
Image: The new ultrasound patch can measure how full the bladder is (Photo courtesy of MIT)

Ultrasound Patch Designed to Monitor Bladder and Kidney Health Could Enable Earlier Cancer Diagnosis

Bladder dysfunction and related health issues affect millions worldwide. Monitoring bladder volume is crucial for assessing kidney health. Traditionally, this requires a visit to a medical facility and... Read more

Nuclear Medicine

view channel
Image: A novel PET radiotracer facilitates early, noninvasive detection of IBD (Photo courtesy of Karmanos)

New PET Radiotracer Aids Early, Noninvasive Detection of Inflammatory Bowel Disease

Inflammatory bowel disease (IBD), which includes Crohn’s disease and ulcerative colitis, is an inflammatory condition of the gastrointestinal tract affecting roughly seven million individuals globally.... Read more

General/Advanced Imaging

view channel
Image: Artificial intelligence predicts therapy responses for ovarian cancer (Photo courtesy of 123RF)

AI Model Combines Blood Test and CT Scan Analysis to Predict Therapy Responses in Ovarian Cancer Patients

Ovarian cancer annually impacts thousands of women, with many diagnoses occurring at advanced stages due to subtle early symptoms. High-grade serous ovarian carcinoma, which accounts for 70-80% of ovarian... 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: Attendees can discover innovative products and technology in the RSNA 2023 Technical Exhibits (Photo courtesy of RSNA)

RSNA 2023 Technical Exhibits to Offer Innovations in AI, 3D Printing and More

The 109th Scientific Assembly and Annual Meeting of the Radiological Society of North America (RSNA, Oak Brook, IL, USA) to be held in Chicago, Nov. 26 to 30 is all set to offer a vast array of medical... Read more
Copyright © 2000-2023 Globetech Media. All rights reserved.