Open AccessColon Cancer Grading Using Infrared Spectroscopic Imaging-Based Deep Learning
Author(s) -
Saumya Tiwari,
Kianoush Falahkheirkhah,
Georgina Cheng,
Rohit Bhargava
Publication year - 2022
Publication title -
applied spectroscopy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.415
H-Index - 110
eISSN - 1943-3530
pISSN - 0003-7028
DOI - 10.1177/00037028221076170
Subject(s) - grading (engineering) , pathology , biopsy , colorectal cancer , h&e stain , medicine , radiology , biomedical engineering , cancer , biology , staining , ecology
Tumor grade assessment is critical to the treatment of cancers. A pathologist typically evaluates grade by examining morphologic organization in tissue using hematoxylin and eosin (H&E) stained tissue sections. Fourier transform infrared spectroscopic (FT-IR) imaging provides an alternate view of tissue in which spatially specific molecular information from unstained tissue can be utilized. Here, we examine the potential of IR imaging for grading colon cancer in biopsy samples. We used a 148-patient cohort to develop a deep learning classifier to estimate the tumor grade using IR absorption. We demonstrate that FT-IR imaging can be a viable tool to determine colorectal cancer grades, which we validated on an independent cohort of surgical resections. This work demonstrates that harnessing molecular information from FT-IR imaging and coupling it with morphometry is a potential path to develop clinically relevant grade prediction models.