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




Events

ATTENTION: Due to the COVID-19 PANDEMIC, many events are being rescheduled for a later date, converted into virtual venues, or altogether cancelled. Please check with the event organizer or website prior to planning for any forthcoming event.

Machine Learning Model Uses MRI Data to Identify Candidates for Liver Transplant

By MedImaging International staff writers
Posted on 22 Aug 2022
Print article
Image: Machine learning models can predict hepatocellular carcinoma treatment response (Photo courtesy of Pexels)
Image: Machine learning models can predict hepatocellular carcinoma treatment response (Photo courtesy of Pexels)

Post-treatment recurrence is an unpredictable complication after liver transplant for hepatocellular carcinoma (HCC) that is associated with poor survival. Biomarkers are needed to estimate recurrence risk before organ allocation. A new study has found that machine learning (ML) models applied to presently underutilized imaging features could help construct more reliable criteria for organ allocation and liver transplant eligibility.

In the proof-of-concept study, researchers at Yale University School of Medicine (New Haven, CT, USA) evaluated the use of ML to predict recurrence from pretreatment laboratory, clinical, and MRI data in patients with early-stage HCC initially eligible for liver transplant. The study included 120 patients (88 men, 32 women; median age, 60 years) diagnosed with early-stage HCC between June 2005 and March 2018, who were initially eligible for liver transplant and underwent treatment by transplant, resection, or thermal ablation. Patients underwent pretreatment MRI and post-treatment imaging surveillance, and imaging features were extracted from post-contrast phases of pretreatment MRI examinations using a pre-trained convolutional neural network (VGG-16). Pretreatment clinical characteristics (including laboratory data) and extracted imaging features were integrated to develop three ML models - clinical, imaging, combined - for recurrence prediction within 1-6 years post-treatment.

Ultimately, all three models predicted post-treatment recurrence for early-stage HCC from pretreatment clinical (AUC 0.60–0.78, across all six time frames), MRI (AUC 0.71–0.85), and both data combined (AUC 0.62–0.86). Using imaging data as the sole model input yielded higher predictive performance than clinical data alone; however, combining both data types did not significantly improve performance over use of imaging data alone.

“The findings suggest that machine learning-based models can predict recurrence before therapy allocation in patients with early-stage HCC initially eligible for liver transplant,” wrote corresponding author Julius Chapiro from the department of radiology and biomedical imaging at Yale University School of Medicine.

Related Links:

Yale University School of Medicine

New
Gold Supplier
Conductive Gel
Tensive
New
Digital X-Ray Flat Panel Detector
2121DXV
New
DICOM Cloud Solution
ORCA Archive
New
SPECT/CT Scanner
AnyScan SC

Print article
Sun Nuclear -    Mirion
FIME - Informa

Channels

Radiography

view channel
Image: BiOI ruby-like crystals can improve medical imaging safety by lowering intensities of harmful X-rays (Photo courtesy of University of Cambridge)

Sustainable Solar Cell Material Could Revolutionize Medical Imaging

The use of X-rays for internal body imaging has dramatically changed non-invasive medical diagnostics. Yet, the high dose of X-rays required for these imaging techniques, due to the poor performance of... Read more

Ultrasound

view channel
Image: A novel, skull-implantable ultrasound device can open the blood-brain barrier to deliver chemotherapy (Photo courtesy of Northwestern Medicine)

Skull-Implantable Ultrasound Device Enables Powerful Chemotherapy Drug to Reach Brain Tumors

A significant challenge in treating lethal brain cancer known as glioblastoma has been the inability of powerful chemotherapy to penetrate the blood-brain barrier to target the aggressive brain tumor.... Read more

Nuclear Medicine

view channel
Image: The components of the dosimeter can fit into an 18mm by 7mm capsule (Photo courtesy of University of NUS)

Novel Ingestible Capsule X-Ray Dosimeter Enables Real-Time Radiotherapy Monitoring

Gastric cancer ranks among the most prevalent cancers worldwide. Precision is vital in modern radiotherapy, as it aims to target tumor cells while minimizing damage to healthy tissue. However, challenges... 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 global AI-enabled medical imaging solutions market is expected to reach USD 18.36 billion in 2032 (Photo courtesy of Freepik)

Global AI-Enabled Medical Imaging Solutions Market Driven by Need for Early Disease Detection

The AI-enabled medical imaging solutions market is currently in its developmental stages, following the significant role of AI-based tools in combating the COVID-19 pandemic. The pandemic saw an upswing... Read more
Copyright © 2000-2023 Globetech Media. All rights reserved.