Colon Cancer Grading Using Infrared Spectroscopic Imaging-Based Deep Learning

Saumya Tiwari, Kianoush Falahkheirkhah, Georgina Cheng, Rohit Bhargava

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)475-484
Number of pages10
JournalApplied Spectroscopy
Issue number4
StatePublished - Apr 2022


  • FT-IR
  • Fourier transform infrared spectroscopic imaging
  • automated grading
  • colon grade
  • colorectal cancer
  • deep learning
  • digital pathology
  • machine learning

ASJC Scopus subject areas

  • Instrumentation
  • Spectroscopy


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