SBME Research Seminar - Dr. Hannah Carter
Immune Checkpoint Blockade (ICB) has revolutionized cancer treatment, however mechanisms determining patient response remain poorly understood. We used machine learning to predict ICB response from germline and somatic biomarkers and studied feature usage by the learned model to uncover putative mechanisms driving superior outcomes. Patients with higher T follicular helper infiltrates were robust to defects in the class-I Major Histocompatibility Complex (MHC-I). Further investigation uncovered different ICB responses in MHC-I versus MHC-II neoantigen reliant tumors across patients. Despite similar response rates, MHC-II reliant responses were associated with significantly longer durable clinical benefit (Discovery: Median OS=63.6 vs. 34.5 months P=0.0074; Validation: Median OS=37.5 vs. 33.1 months, P=0.040). Characteristics of the tumor immune microenvironment reflected MHC neoantigen reliance, and analysis of immune checkpoints revealed LAG3 as a potential target in MHC-II but not MHC-I reliant responses. This study highlights the value of interpretable machine learning models in elucidating the biological basis of therapy responses.
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Propels: AI in Medical Imaging: from Innovation to Adoption
February 4 @ 12:00 pm - 1:30 pm PST
Propels: AI in Medical Imaging: from Innovation to Adoption
This workshop dives into the lifecycle of AI in medical imaging, focusing on technology development, key use cases, the pathway to market and clinical adoption. Participants will gain an understanding of how AI-powered imaging solutions are designed, built, and optimized for real-world applications, along with insights into regulatory frameworks critical for market entry. Led by Clarius Mobile Health, the session will cover strategies for bringing AI innovations to clinical settings, emphasizing collaborative roles that enhance, rather than replace, the expertise of healthcare professionals. Join us to learn how AI is shaping the future of medical imaging from concept to impactful clinical integration.
Location:
SBME 1005 (Design Studio 2)