NEW FEATURE ANNOUNCEMENT: VinBrain Officially Launches LI-RADS Classification Feature on DrAid™ CT Liver Cancer D&T

Alongside the existing groundbreaking features such as automatic segmentation and classification of 4 types of liver lesions on CT images of DrAid™ CT Liver Cancer D&T, VinBrain officially unveils the LI-RADS classification feature starting from June 2024. DrAid™ CT Liver Cancer D&T is the result of collaborative research efforts between experts from the United States and Vietnam, including skilled doctors from the University Medical Center (UMC) in Ho Chi Minh City, contributing to medical labeling and professional advisory services. 

In medical imaging diagnostics, variability in interpreting liver lesions and inconsistency in diagnoses pose significant challenges. Moreover, ensuring uniformity in treatment evaluation, especially among doctors with varying levels of expertise, is becoming increasingly difficult. So, what can artificial intelligence (AI) do to address these issues and better support doctors and medical experts? 

1. What is the AI feature for lesion classification following LI-RADS standards?
   The Liver Imaging Reporting and Data System (LI-RADS) is a standardized classification method developed by the American College of Radiology. LI-RADS aids in the interpretation of liver lesions on medical images, with each lesion assigned a LI-RADS category indicating its relative risk for hepatocellular carcinoma (HCC).

The categories range from LR-1 (definitely benign), LR-2 (probably benign), LR-M (malignant but not HCC), to LR-5 (definitely HCC). (Source: https://www.acr.org/-/media/ACR/Files/RADS/LI-RADS/LI-RADS-2018-Core.pdf )

DrAid™ CT Liver Cancer utilizes AI technology to analyze liver CT scans, integrating advanced LI-RADS standards to assist doctors in diagnosing liver cancer more accurately and effectively. Beyond classification, DrAid™ CT Liver Cancer provides interpretations and other critical criteria based on AI algorithms, including:

•  Non-rim arterial phase hyperenhancement (APHE): signifying enhancement in the arterial phase greater than background liver.
• Non peripheral "washout": a visual assessment of relative hypo-intensity of the lesion compared with background liver on the portal venous and delayed phases.
• Enhancing "capsule": the peripheral rim of smooth hyperenhancement seen in the portal venous phase or delayed phase.
• Size: determination of tumor dimensions for accurate characterization.
• Tumor size growth: size increase of a tumor by at least 50% within 6 months.

Interface displaying AI results with LI-RADS criteria interpretation.

2. AI model standardizes and streamlines diagnosis and treatment evaluation process for liver cancer
The launch of LI-RADS feature on DrAid™ CT Liver Cancer brings significant benefits to clinical practitioners, hepatologists, gastroenterologists, oncologists, and radiologists.

Benefits for Doctors:

• Standardize diagnosis: LI-RADS on DrAid™ helps unify the language for describing and classifying liver tumors, reducing diagnostic errors and ensuring consistency in treatment evaluation, which is particularly important for high-risk patient groups.
• Improve treatment efficiency: Accurate diagnosis based on the LI-RADS standard on DrAid™ assists doctors in selecting appropriate treatment methods, closely monitoring treatment effectiveness, and accurately assessing disease response and progression.

Benefits for Patients:

• Get early diagnosis and accurate treatment.
• Receive appropriate and timely treatment according to disease progression.
• Assess accurately post-treatment outcomes and recurrence prognosis.

DrAid™ CT Liver Cancer contributes to doctors' workflows, serving as an effective tool in diagnosis and treatment.