Prediction of early colorectal cancer metastasis by machine learning using digital slide images.
Colorectal cancer
Lymph node metastasis
Random forest
Supervised machine learning
Journal
Computer methods and programs in biomedicine
ISSN: 1872-7565
Titre abrégé: Comput Methods Programs Biomed
Pays: Ireland
ID NLM: 8506513
Informations de publication
Date de publication:
Sep 2019
Sep 2019
Historique:
received:
10
02
2019
revised:
08
06
2019
accepted:
21
06
2019
entrez:
17
8
2019
pubmed:
17
8
2019
medline:
23
2
2020
Statut:
ppublish
Résumé
Prediction of lymph node metastasis (LNM) for early colorectal cancer (CRC) is critical for determining treatment strategies after endoscopic resection. Some histologic parameters for predicting LNM have been established, but evaluator error and inter-observer disagreement are unsolved issues. Here we describe an LNM prediction algorithm for submucosal invasive (T1) CRC based on machine learning. We conducted a retrospective single-institution study of 397 T1 CRCs. Several morphologic parameters were extracted from whole slide images of cytokeratin immunohistochemistry using Image J. A random forest algorithm for a training dataset (n = 277) was executed and used to predict LNM for the test dataset (n = 120). The results were compared with conventional histologic evaluation of hematoxylin-eosin staining. Machine learning showed better LNM predictive ability than the conventional method on some datasets. Cross validation revealed no significant difference between the methods. Machine learning resulted in fewer false-negative cases than the conventional method. Machine learning on whole slide images is a potential alternative for determining treatment strategies for T1 CRC.
Sections du résumé
BACKGROUND AND OBJECTIVES
OBJECTIVE
Prediction of lymph node metastasis (LNM) for early colorectal cancer (CRC) is critical for determining treatment strategies after endoscopic resection. Some histologic parameters for predicting LNM have been established, but evaluator error and inter-observer disagreement are unsolved issues. Here we describe an LNM prediction algorithm for submucosal invasive (T1) CRC based on machine learning.
METHODS
METHODS
We conducted a retrospective single-institution study of 397 T1 CRCs. Several morphologic parameters were extracted from whole slide images of cytokeratin immunohistochemistry using Image J. A random forest algorithm for a training dataset (n = 277) was executed and used to predict LNM for the test dataset (n = 120). The results were compared with conventional histologic evaluation of hematoxylin-eosin staining.
RESULTS
RESULTS
Machine learning showed better LNM predictive ability than the conventional method on some datasets. Cross validation revealed no significant difference between the methods. Machine learning resulted in fewer false-negative cases than the conventional method.
CONCLUSIONS
CONCLUSIONS
Machine learning on whole slide images is a potential alternative for determining treatment strategies for T1 CRC.
Identifiants
pubmed: 31416544
pii: S0169-2607(19)30197-X
doi: 10.1016/j.cmpb.2019.06.022
pii:
doi:
Substances chimiques
Keratins
68238-35-7
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
155-161Informations de copyright
Copyright © 2019 Elsevier B.V. All rights reserved.