RESEARCH GRANTS

Title:
CANDLE POLISEE: Early Cancer Detection in the Liver of High-Risk Filipinos Through Evidence-Based Enhancement of the PhilHealth NHDR and Healthcare Packages

Background:
Liver cirrhosis and hepatocellular carcinoma (HCC) are major causes of mortality in the Philippines, with chronic hepatitis B affecting an estimated 3.3 million Filipino adults. Early liver disease is often asymptomatic, and current public health programs lack sufficient screening and monitoring tools within primary care services. Integrating simple biochemical markers and strengthening health data systems could enable earlier detection and better monitoring of individuals at risk for liver cancer.

Objective:
To develop a policy-supported patient pathway for early detection of hepatocellular carcinoma among high-risk Filipinos by integrating screening tools, clinical registries, and healthcare navigation systems within the PhilHealth YAKAP program and the National Health Data Repository (NHDR).

Target Output:
A policy brief and implementation framework recommending the inclusion of key laboratory tests (AST, ALT, and platelet count) and liver cancer screening tools in the PhilHealth YAKAP package, alongside the development of interoperable registries and clinical pathways to improve early detection and management of chronic liver disease and hepatocellular carcinoma in the Philippines.

Title:
Validation of Machine Learning–Based Models for the Diagnosis of Liver Cirrhosis in Filipino Patients

Background:
Liver cirrhosis is a major cause of liver-related morbidity and mortality and significantly increases the risk of hepatocellular carcinoma. Diagnosis traditionally relies on liver biopsy, which is invasive and costly, while existing non-invasive tests have variable accuracy and limited validation in Filipino populations. Advances in artificial intelligence (AI) and machine learning offer opportunities to improve non-invasive detection using routinely collected clinical and laboratory data.

Objective:
To evaluate the diagnostic performance of machine learning–based models in detecting liver cirrhosis and compare their accuracy with existing non-invasive scoring systems and histopathology-confirmed diagnoses among Filipino patients.

Target Output:
A validated AI-driven diagnostic model for liver cirrhosis based on Philippine clinical data, along with scientific publications and conference presentations that support the development of accessible, non-invasive tools for early liver disease detection.

Title:
Benefit of Adding Ultrasound to Mammography Among BI-RADS 0 Patients: A Nested Cohort Observational Study in the SCRAP Cancer MobiBus Program

Background:
Breast cancer is the most common cancer worldwide and many patients in the Philippines are diagnosed at advanced stages. Mammography is the standard screening tool for early detection; however, its sensitivity can be reduced in women with dense breast tissue, which is common among Asian populations. In cases where mammography results are categorized as BI-RADS 0 (incomplete assessment), additional imaging such as ultrasound may help identify cancers that are not visible on mammography alone.

Objective:
To compare the diagnostic performance—specifically the sensitivity, specificity, and accuracy—of mammography alone versus mammography combined with ultrasound in women with BI-RADS 0 results who are participating in a community-based breast cancer screening program.

Target Output:
Evidence on the clinical value of adding ultrasound to mammography in breast cancer screening programs, which may guide policymakers and inform future clinical practice guidelines and cost-effectiveness analyses for public health screening initiatives in the Philippines.