Chemical imaging and machine learning for sub‐classification of oesophageal tissue histology. Issue 4 (29th September 2021)

• Record Type: Journal Article
• Authors: Keogan, Abigail; Nguyen, Thi Nguyet Que; Phelan, James J.; O'Farrell, Naoimh; Lynam‐Lennon, Niamh; Doyle, Brendan; O'Toole, Dermot; Reynolds, John V.; O'Sullivan, Jacintha; Meade, Aidan D.
• Is Part Of: Translational biophotonics. Volume 3:Issue 4(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-09-29
• Subjects: Barrett's Oesophagus -- FTIR spectroscopy -- oesophageal adenocarcinoma -- PLSDA; Imaging systems in medicine -- Periodicals; Biosensors -- Optical properties -- Periodicals; Photonics -- Periodicals; Imaging systems in medicine; Photonics; Optics and Photonics; Translational Medical Research; Periodicals; Periodical; 621.365
• DOI: 10.1002/tbio.202100004
• Languages: English
• ISSNs: 2627-1850

Abstract

Abstract: Fourier Transform Infrared (FTIR) based chemical imaging is a powerful, non‐destructive and label‐free biophotonic technique, which spatially acquires bio‐molecularly relevant information in histopathology. Cancer detection with objective chemical imaging techniques is relatively well established, though detection of pre‐cancer stages within a continuum from normal tissue to cancer remains challenging. Here machine learning with chemical imaging was used to provide an objective classification pipeline for oesophageal tissues pathologically classified as normal, oesophagitis, dysplasia, Barrett's disease and cancer. Spectral images were segmented using a k ‐means cluster validity indices approach and clustered spectra were classified using partial least squares discriminant analysis. Classification performances approached a receiver operator characteristic area‐under‐the‐curve (ROC‐AUC) of 0.90 for binary classification tasks (eg, normal vs Barrett's). Isolated histopathological substructures were identified which delivered a ROC‐AUC in of ~0.69 in classifying into each of the five‐classes. This work may provide the means to assist pathologist diagnoses of intermediate pre‐cancer stages. Abstract : Cancer detection with objective chemical imaging techniques is relatively well established, though detection of pre‐cancer stages within a continuum from normal tissue to cancer remains challenging. Here, machine learning with chemical imaging was used to provide an objective classification pipeline for oesophageal tissues pathologically classified as normal, oesophagitis, dysplasia, Barrett's disease and cancer. Classification performances approached a receiver operator characteristic area‐under‐the‐curve of 0.90 for binary classification tasks (eg, normal vs Barrett's). This work may provide the means to assist pathologist diagnoses of intermediate pre‐cancer stages.