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NIH grant 1R01CA305903-01: Assessment of sound waves from liver vessels via artificial intelligence to detect liver cancer
Institute: {'code': 'CA', 'abbreviation': 'NCI', 'name': 'National Cancer Institute', 'admin_org_id': '12500008', 'admin_funding_url': ''}. PI: Jose Daniel Debes. Award: $441138.
Start: 2026-09-18T00:00:00.
Hepatocellular carcinoma (HCC) is the second leading cause of cancer-related death worldwide. Hepatitis B
and C are the most common causes of HCC worldwide. The majority of deaths related to HCC occur in
resource-limited settings such as South America where viral hepatitis is frequent. HCC mortality occurs
primarily due to late detection, which precludes potentially curative interventions. Currently, individuals with
liver disease are advised to undergo liver ultrasonography every 6 months with the goal of “visually” identifying
a tumor small enough that might be amenable to cure. This visual screening approach is not optimal as
ultrasound is operator dependent. Moreover, the sensitivity of ultrasound in screening for early HCC is
approximately 50%, which is far from optimal. Therefore, there is a need to find easy-to-apply HCC screening
methods which are effective, low-cost, scalable to a population, and require no advanced expertise.
Artificial Intelligence (AI) is widely studied in its potential to diagnose cancer, usually attempting to enhance the
detection accuracy of a mass that is already detectable by the human eye. The liver is mainly vascularized by
the portal vein. As a primary liver cancer (HCC) develops small hepatic arteries grow within the tumor,
providing arterial blood flow. Thus, the normal liver received blood from the portal vein while liver cancer (HCC)
does so from the hepatic artery, effectively dividing liver and tumor into two vascular systems and changing
blood flow of the main liver vessels. These changes are not perceptible to the human eye. We hypothesize that
assessment of sound waves of the main liver vessels through Doppler ultrasonography, which can be detected
with mobile-phone ultrasonography, and analyzed via AI will detect changes that can differentiate livers with
HCC from those with no HCC. Our preliminary data show that a simple Doppler ultrasound assessment of the
portal vein with our AI reading can detect the presence of HCC with a 90% sensitivity – an almost 2-fold
improvement over ultrasound alone. Our aims are to #1 assess the role of Doppler ultrasonography sound
waves analyzed via AI in differentiating livers with cancer versus controls, refining the algorithm in our
6000 ultrasound database; #2 determine the efficacy of Doppler ultrasonography in detecting HCC in
resource-limited settings in a separate validation cohort, training and supervising local staff in Porto
Alegre, Brazil to evaluate the algorithm in an existing 2000 ultrasound database and; #3 prospectively
validate the real-life role of mobile-phone Doppler ultrasound analyzed via AI in screening for HCC in
resource-limited settings, through our ongoing ESCALON HCC clinics in Brazil and Ecuador.
If successful, our project has the potential to revolutionize the way we detect liver cancer. Moreover, this
methodology could eventually be modified and applied to the screening of other cancers around the globe.document fetched 2026-09-27 02:45