Quantitative analysis of high-plex immunofluorescence microscopy images to investigate the breast cancer tumor microenvironment

Madeleine S. Torcasso; Frederick M. Howard; Yuanyuan Zha; Junting Ai; Marcus R. Clark; Maryellen L. Giger

doi:10.1117/12.3027025

29 May 2024 Quantitative analysis of high-plex immunofluorescence microscopy images to investigate the breast cancer tumor microenvironment

Madeleine S. Torcasso, Frederick M. Howard, Yuanyuan Zha, Junting Ai, Marcus R. Clark, Maryellen L. Giger

Author Affiliations +

Proceedings Volume 13174, 17th International Workshop on Breast Imaging (IWBI 2024); 1317413 (2024) https://doi.org/10.1117/12.3027025
Event: 17th International Workshop on Breast Imaging (IWBI 2024), 2024, Chicago, IL, United States

Abstract

Breast cancer (BC) remains the deadliest cancer for women worldwide. Neoadjuvant immunotherapies have demonstrated improved responses for some patients. Unfortunately, no robust method exists for predicting which patients will respond to immunotherapy. Imaging of diagnostic BC biopsies has revealed that the spatial distribution of tumor infiltrating lymphocytes (TILs) and other immune cells within and around the tumor can help stratify BC patients into responders and non-responders. However, clinical microscopy cannot differentiate between subtypes of TILs; numerous markers are needed to capture the heterogeneity of cancer cells and immune cells in the TME. Highly multiplexed fluorescence microscopy, or high-plex IF, has emerged as a workhorse in data collection for spatial proteomics. We present a pilot study of the TME of BC patients treated with neoadjuvant immunotherapy. Specifically in this abstract, we discuss computer vision methods for analyzing the cellular constituents probed in these complex and rich images. We discuss image stitching and channel registration for high-plex modalities, deep learning algorithms for cell detection and segmentation, and pseudo-spectral angle mapping (pSAM) for cell classification. We present strategies for accurate quantification of these images, facilitating investigations into immune activity in breast tumors with high phenotypic accuracy.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Madeleine S. Torcasso, Frederick M. Howard, Yuanyuan Zha, Junting Ai, Marcus R. Clark, and Maryellen L. Giger "Quantitative analysis of high-plex immunofluorescence microscopy images to investigate the breast cancer tumor microenvironment", Proc. SPIE 13174, 17th International Workshop on Breast Imaging (IWBI 2024), 1317413 (29 May 2024); https://doi.org/10.1117/12.3027025

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
7 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Tumors

Image segmentation

Biopsy

Breast cancer

Microscopy

Cancer

Fluorescence microscopy

Show All Keywords

Keywords/Phrases

Search In:

Publication Years