Our approach leverages patient data over time, including clinical records, symptoms, blood tests, imaging, and tissue samples, to track disease progression and enhance early detection and diagnosis. (Adapted from Zhuang et al and created in https://BioRender.com)
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Overview
Overview
In today’s healthcare landscape, the ability to collect vast longitudinal observational data must be paired with the power to continuously learn from that data and personalize clinical decisions for every individual. At the Hsu Lab, we develop and validate computational tools that transform complex biomedical data into clinically meaningful insights.
Our Approach
We harness artificial intelligence (AI) and machine learning (ML) to mine and analyze clinical records, diagnostic imaging, and molecular data, integrating genomic and environmental information to enable evidence-based patient management and precision health.
Research Focus
Our work centers on three key areas:
Building tools to process, align, and harmonize clinical and imaging data, helping clinicians uncover patterns across diverse data types.
Designing and validating algorithms that combine genetics, imaging, and clinical records to deliver accurate health predictions.
Deploying these tools in real-world hospitals and clinics to study their effect on physician workflows and, most importantly, patient care.
Our Mission
We transform complex multimodal datasets into actionable insights, empowering clinicians and individuals to make informed decisions and lead healthier lives.
Focus Areas
Focus Areas
Image harmonization to improve the reliability of quantitative image analysis
Multimodal characterization of indeterminate pulmonary nodules
Longitudinal, multimodal analysis
Implementation of AI to enable same-day diagnostic follow-up of abnormal screening mammograms
Utilization of AI to increase adherence to cancer screening recommendations
Active Funding
Active Funding
National Cancer Institute
Boston University-UCLA Biomarker Characterization Center (U2C)
PI: Marc Lenberg, MPIs: Steven Dubinett, Jennifer Beane, William Hsu
EFIRM Liquid Biopsy Research Laboratory: Early Lung Cancer Assessment (U01)
PI: David Wong, MPIs: Denise Aberle, William Hsu, Kostyantyn Krysan, Charles Strom, Fang Wei
National Institute for Biomedical Imaging and Bioengineering
Computational Toolkit for Normalizing the Impact of CT Acquisition and Reconstruction on Quantitative Image Features (R01)
PI: William Hsu, MPI: Michael McNitt-Gray
An AI/ML-ready Dataset for Investigating the Effect of Variations in CT Acquisition and Reconstruction (Supplement)
PI: William Hsu, MPI: Michael McNitt-Gray
Medical Imaging Informatics Training Grant (T32)
PI: Alex Bui, MPI: Ashley Prosper, William Hsu
Agency for Healthcare Research and Quality
Using artificial intelligence to support efficient same-day diagnostic imaging in breast cancer screening (R21/R33)
PI: William Hsu, MPI: Anne Hoyt
V Foundation
Integrating Cell-Free DNA Methylome and CT Imaging to Determine the Malignancy of Lung Nodules
PI: Xianghong Jasmine Zhou, Co-PI: William Hsu
Early Diagnostics Inc
Cloud-based Liquid-biopsy and Radiomics Platform for the Cancer Research Data Commons (subcontract to NCI SBIR Phase II)
Subcontract PI: William Hsu
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