Classifying tumor heterogeneity of human esophageal cancer biopsies by dynamic contrast OCT with deep learning

Zhen Hua; Shadia Jalal; John Turek; David D. Nolte

doi:10.1117/12.2647602

8 March 2023 Classifying tumor heterogeneity of human esophageal cancer biopsies by dynamic contrast OCT with deep learning

Zhen Hua, Shadia Jalal, John Turek, David D. Nolte

Author Affiliations +

Proceedings Volume 12367, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXVII; 123670L (2023) https://doi.org/10.1117/12.2647602
Event: SPIE BiOS, 2023, San Francisco, California, United States

Abstract

Tumor heterogeneity is one of the greatest obstacles standing in the way of successful cancer therapy. Cancer in a single patient is not a single disease, but is a host of related diseases, all of which need to respond to a single treatment regimen. We have completed the first human clinical trial in esophageal cancer using dynamic-contrast OCT (DC-OCT) based on full-frame digital holography to assess the spatial heterogeneity of biopsy response to platinum-based chemotherapy. A deep twin neural network successfully identified biopsy sub-phenotypes in the dynamic tissue response that enabled accurate prediction of patient treatment success.

Conference Presentation

Citation Download Citation

Zhen Hua, Shadia Jalal, John Turek, and David D. Nolte "Classifying tumor heterogeneity of human esophageal cancer biopsies by dynamic contrast OCT with deep learning", Proc. SPIE 12367, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXVII, 123670L (8 March 2023); https://doi.org/10.1117/12.2647602

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