Leveraging Mid-infrared Spectroscopic Imaging and Deep Learning for Tissue Subtype Classification in Ovarian Cancer

Authors

Chalapathi Charan Gajjela
Matthew Brun
Rupali Mankar
Sara Corvigno
Noah Kennedy
Yanping Zhong
Jinsong Liu
Anil K Sood
David Mayerich
Sebastian Berisha
Rohith Reddy

Language

English

Publication Date

6-12-2023

Journal

Analyst

DOI

10.1039/d2an01035f

PMID

37218522

PMCID

PMC12616426

PubMedCentral® Posted Date

11-15-2025

PubMedCentral® Full Text Version

Author MSS

Abstract

Mid-infrared spectroscopic imaging (MIRSI) is an emerging class of label-free techniques being leveraged for digital histopathology. Modern histopathologic identification of ovarian cancer involves tissue staining followed by morphological pattern recognition. This process is time-consuming and subjective and requires extensive expertise. This paper presents the first label-free, quantitative, and automated histological recognition of ovarian tissue subtypes using a new MIRSI technique. This optical photothermal infrared (O-PTIR) imaging technique provides a 10× enhancement in spatial resolution relative to prior instruments. It enables sub-cellular spectroscopic investigation of tissue at biochemically important fingerprint wavelengths. We demonstrate that the enhanced resolution of sub-cellular features, combined with spectroscopic information, enables reliable classification of ovarian cell subtypes achieving a classification accuracy of 0.98. Moreover, we present a statistically robust analysis from 78 patient samples with over 60 million data points. We show that sub-cellular resolution from five wavenumbers is sufficient to outperform state-of-the-art diffraction-limited techniques with up to 235 wavenumbers. We also propose two quantitative biomarkers based on the relative quantities of epithelia and stroma that exhibit efficacy in early cancer diagnosis. This paper demonstrates that combining deep learning with intrinsic biochemical MIRSI measurements enables quantitative evaluation of cancerous tissue, improving the rigor and reproducibility of histopathology.

Keywords

Humans, Female, Reproducibility of Results, Deep Learning, Spectrophotometry, Infrared, Diagnostic Imaging, Ovarian Neoplasms

Published Open-Access

yes

Recommended Citation

Gajjela, Chalapathi Charan; Brun, Matthew; Mankar, Rupali; et al., "Leveraging Mid-infrared Spectroscopic Imaging and Deep Learning for Tissue Subtype Classification in Ovarian Cancer" (2023). Faculty, Staff and Student Publications. 6189.
https://digitalcommons.library.tmc.edu/uthgsbs_docs/6189