Imperial College London
Alternate

Professor Daniel Elson

Faculty of Medicine, Department of Surgery & Cancer

Professor of Surgical Imaging
 
 
 
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Contact

 

+44 (0)20 7594 1700daniel.elson Website CV

 
 
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Location

 

415 Bessemer BuildingBessemer BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Nazarian:2022,
author = {Nazarian, S and Gkouzionis, I and Kawka, M and Jamroziak, M and Lloyd, J and Darzi, A and Patel, N and Elson, DS and Peters, CJ},
journal = {JAMA Surgery},
title = {Real-time tracking and classification of tumour and non-tumour tissue in upper gastrointestinal cancers using diffuse reflectance spectroscopy for resection margin assessment},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Importance:Cancers of the upper gastrointestinal tract remain a major contributor to the global cancer burden. The accurate mapping of tumour margins is of particular importance for curative cancer resection and improvement in overall survival. Current mapping techniques preclude a full resection margin assessment in real-time.Objective:We aimed to use diffuse reflectance spectroscopy on gastric and oesophageal cancer specimens to differentiate tissue types and provide real-time feedback to the operator.Design:This was a prospective ex vivo validation study. Patients undergoing oesophageal or gastric cancer resection were prospectively recruited into the study between July 2020 and July 2021 at Hammersmith Hospital in London, United Kingdom.Setting:This was a single-centre study based at a tertiary hospital.Participants:Tissue specimens were included for patients undergoing elective surgery for either oesophageal carcinoma (adenocarcinoma or squamous cell carcinoma) or gastric adenocarcinoma.Exposure:A hand-held diffuse reflectance spectroscopy probe and tracking system was used on freshly resected ex vivo tissue to obtain spectral data. Binary classification, following histopathological validation, was performed using four supervised machine learning classifiers. Main Outcomes and Measures:Data were divided into training and testing sets using a stratified 5-fold cross-validation method. Machine learning classifiers were evaluated in terms of sensitivity, specificity, overall accuracy, and the area under the curve.Results:A total of 14,097 mean spectra for normal and cancerous tissue were collected from 37 patients. The machine learning classifier achieved an overall normal versus cancer diagnostic accuracy of 93.86±0.66 for stomach tissue and 96.22±0.50 for oesophageal tissue, and sensitivity and specificity of 91.31% and 95.13% for stomach and 94.60% and 97.28% for oesophagus, respectively. Real-time tissue tracking and classification was achieved a
AU  - Nazarian,S
AU  - Gkouzionis,I
AU  - Kawka,M
AU  - Jamroziak,M
AU  - Lloyd,J
AU  - Darzi,A
AU  - Patel,N
AU  - Elson,DS
AU  - Peters,CJ
PY  - 2022///
SN  - 2168-6254
TI  - Real-time tracking and classification of tumour and non-tumour tissue in upper gastrointestinal cancers using diffuse reflectance spectroscopy for resection margin assessment
T2  - JAMA Surgery
ER  -
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