MEMP PhD Thesis Defense (12:00pm): Christian Landeros

Machine-Guided Biopsy Analysis in Oncology: Facilitating Diagnostic Access and Biomedical Discovery Through Deep Learning

Oncology researchers have long understood the critical role of disease heterogeneity in shaping patient outcomes and treatment responses. Patient datasets now encompass a wide range of imaging, molecular, and genetic information in an effort to provide more personalized and targeted care. In this thesis, we outline three case studies examining the role deep learning can play in interpreting and utilizing complex biomedical data. In the first case, we employ neural networks to replace computationally intensive image processing steps in a point-of-care HPV diagnostic device, facilitating its deployment to resource-limited settings. In the second, we develop a comprehensive computational pipeline for cyclic fluorescent microscopy. We identify key immune cell subpopulations in head and neck cancer biopsies to better understand the tumor microenvironment’s influence on disease progression and treatment response. Our third study tackles the analysis of much larger gigapixel-sized digital histology images from breast cancer biopsies. We establish a two-step approach that i) uses self-supervised learning to encode small-scale histological details into robust representations and ii) applies a transformer model to these representations to evaluate larger-scale histological patterns. Our model successfully distinguished patients with high-risk genetic profiles from histology alone and provided visualization tools to highlight slide regions most closely associated with a high risk of cancer recurrence. In doing so, we set the stage for deep learning to serve as an alternative to expensive genetic testing as well as a tool for illuminating previously unidentified markers of recurrence risk. These studies underscore deep learning’s versatility in oncology, streamlining the integration of complex datasets into clinical workflows and broadening access to state-of-the-art personalized care.

Thesis Supervisor:
Hakho Lee, PhD
Professor of Radiology, HMS; Director of the Biomedical Engineering Program at the Center for Systems Biology, MGH

Thesis Committee Chair:
Collin M. Stultz, MD, PhD
Nina T. and Robert H. Rubin Professor, Electrical Engineering & Computer Science and Institute for Medical Engineering & Science; Co-Director, Harvard-MIT Health Sciences & Technology; Associate Director, Institute for Medical Engineering and Science, MIT

Thesis Reader:
Amy Ly, MD
Associate Professor of Pathology, HMS; Assistant Pathologist, MGH

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Zoom invitation – 

Christian Landeros is inviting you to a scheduled Zoom meeting.

Topic: Christian Landeros MEMP PhD Thesis Defense
Time: Monday, April 29, 2024, 12:00 PM Eastern Time (US and Canada)

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