Real time colon histopathology by infrared spectroscopic imaging

Abstract
Colorectal cancer (CRC) is one of the leading causes of death in the US. Active screening and early intervention
in risky cancers can lead to good outcomes; however, a bottleneck in rapidly delivering appropriate patient care
is the long time period for histologic assessment and lack of precision in predicting disease severity.
Morphological assessments prevalent in histology are useful but resource intensive and not predictive enough.
Molecular techniques to complement traditional pathology are emerging but often require much more effort and
time, without being especially compatible with histologic assessments. Here, we seek to develop a technology
that measures the chemical content of tissues, does not require reagents, is entirely compatible with clinical
workflows and leverages modern artificial intelligence (AI) techniques to provide real-time histologic assessment.
The foundation of our approach is a new design for an infrared spectroscopic imaging system that is faster than
any reported, offers a higher spatial and spectral quality and uses a solid immersion lens with a fixed focus at
the sealed surface of the lens to enable use by a minimally trained person. In conjunction with the instrument,
we develop AI algorithms that measure the chemical content of tissue and use it to provide (a) conventional
pathology images without the use of dyes (“stainless staining”), and (b) histologic assessment based on
molecular data, which can provide complementary composition, disease and risk of lethal cancer images akin to
conventional pathology. The instrument will be usable by laboratory technicians, without the need to prepare thin
sections from excised tissue and will provide information in minutes. Using preliminary data from human patients
on over 850 tissue microarray (TMA) samples from 8 TMAs and 30 surgical resections, we validate the use of
technology in providing complete histologic and disease grade assessment. Statistical methods will be used to
assess the results rigorously and quantitative milestones guide the entire approach. We then translate the results
to fresh tissue chunks, providing histology minutes after tissue is extracted from the body. Finally, we use the
detailed tumor and microenvironment information available from the tissue to segment patients into a “high risk”
and “low risk” group. The availability of rapid histologic assessment can help prevent delays in providing care,
provide intraoperative assessment, and add more information to morphologic assessments following screening,
enabling a wide use in CRC and other cancer pathologies.

Grant Number: 5R01CA260830-05
NIH Institute/Center: NIH
Principal Investigator: Rohit Bhargava