  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, 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 technique, called optical photothermal
infrared (O-PTIR) imaging, provides a 10X enhancement in spatial resolution
relative to prior instruments. It enables sub-cellular spectroscopic
investigation of tissue at biochemically important fingerprint wavelengths. We
demonstrate that 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
statistically robust validation from 74 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 from
up to 235 wavenumbers. We also propose two quantitative biomarkers based on the
relative quantities of epithelium and stroma that exhibits 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.
