Fast and label-free automated detection of microsatellite status in early colon cancer using artificial intelligence integrated infrared imaging.
Artificial intelligence
Colon cancer
Convolutional neural networks
Deep learning
Infrared imaging
Label-free
Microsatellite instability
Journal
European journal of cancer (Oxford, England : 1990)
ISSN: 1879-0852
Titre abrégé: Eur J Cancer
Pays: England
ID NLM: 9005373
Informations de publication
Date de publication:
03 2023
03 2023
Historique:
received:
04
10
2022
revised:
14
12
2022
accepted:
23
12
2022
pubmed:
12
2
2023
medline:
3
3
2023
entrez:
11
2
2023
Statut:
ppublish
Résumé
Microsatellite instability (MSI) due to mismatch repair (MMR) defects accounts for 15-20% of colon cancers (CC). MSI testing is currently standard of care in CC with immunohistochemistry of the four MMR proteins representing the gold standard. Instead, label-free quantum cascade laser (QCL) based infrared (IR) imaging combined with artificial intelligence (AI) may classify MSI/microsatellite stability (MSS) in unstained tissue sections user-independently and tissue preserving. Paraffin-embedded unstained tissue sections of early CC from patients participating in the multicentre AIO ColoPredict Plus (CPP) 2.0 registry were analysed after dividing into three groups (training, test, and validation). IR images of tissue sections using QCL-IR microscopes were classified by AI (convolutional neural networks [CNN]) using a two-step approach. The first CNN (modified U-Net) detected areas of cancer while the second CNN (VGG-Net) classified MSI/MSS. End-points were area under receiver operating characteristic (AUROC) and area under precision recall curve (AUPRC). The cancer detection in the first step was based on 629 patients (train n = 273, test n = 138, and validation n = 218). Resulting classification AUROC was 1.0 for the validation dataset. The second step classifying MSI/MSS was performed on 547 patients (train n = 331, test n = 69, and validation n = 147) reaching AUROC and AUPRC of 0.9 and 0.74, respectively, for the validation cohort. Our novel label-free digital pathology approach accurately and rapidly classifies MSI vs. MSS. The tissue sections analysed were not processed leaving the sample unmodified for subsequent analyses. Our approach demonstrates an AI-based decision support tool potentially driving improved patient stratification and precision oncology in the future.
Identifiants
pubmed: 36773401
pii: S0959-8049(23)00002-3
doi: 10.1016/j.ejca.2022.12.026
pii:
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
122-131Informations de copyright
Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.