Computer-Assisted Diagnosis of Lymph Node Metastases in Colorectal Cancers Using Transfer Learning With an Ensemble Model.
Details
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State: Public
Version: Final published version
License: CC BY-NC-ND 4.0
State: Public
Version: Final published version
License: CC BY-NC-ND 4.0
Serval ID
serval:BIB_3160CEB75ED8
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Computer-Assisted Diagnosis of Lymph Node Metastases in Colorectal Cancers Using Transfer Learning With an Ensemble Model.
Journal
Modern pathology
ISSN
1530-0285 (Electronic)
ISSN-L
0893-3952
Publication state
Published
Issued date
05/2023
Peer-reviewed
Oui
Volume
36
Number
5
Pages
100118
Language
english
Notes
Publication types: Journal Article ; Research Support, Non-U.S. Gov't
Publication Status: ppublish
Publication Status: ppublish
Abstract
Screening of lymph node metastases in colorectal cancer (CRC) can be a cumbersome task, but it is amenable to artificial intelligence (AI)-assisted diagnostic solution. Here, we propose a deep learning-based workflow for the evaluation of CRC lymph node metastases from digitized hematoxylin and eosin-stained sections. A segmentation model was trained on 100 whole-slide images (WSIs). It achieved a Matthews correlation coefficient of 0.86 (±0.154) and an acceptable Hausdorff distance of 135.59 μm (±72.14 μm), indicating a high congruence with the ground truth. For metastasis detection, 2 models (Xception and Vision Transformer) were independently trained first on a patch-based breast cancer lymph node data set and were then fine-tuned using the CRC data set. After fine-tuning, the ensemble model showed significant improvements in the F1 score (0.797-0.949; P <.00001) and the area under the receiver operating characteristic curve (0.959-0.978; P <.00001). Four independent cohorts (3 internal and 1 external) of CRC lymph nodes were used for validation in cascading segmentation and metastasis detection models. Our approach showed excellent performance, with high sensitivity (0.995, 1.0) and specificity (0.967, 1.0) in 2 validation cohorts of adenocarcinoma cases (n = 3836 slides) when comparing slide-level labels with the ground truth (pathologist reports). Similarly, an acceptable performance was achieved in a validation cohort (n = 172 slides) with mucinous and signet-ring cell histology (sensitivity, 0.872; specificity, 0.936). The patch-based classification confidence was aggregated to overlay the potential metastatic regions within each lymph node slide for visualization. We also applied our method to a consecutive case series of lymph nodes obtained over the past 6 months at our institution (n = 217 slides). The overlays of prediction within lymph node regions matched 100% when compared with a microscope evaluation by an expert pathologist. Our results provide the basis for a computer-assisted diagnostic tool for easy and efficient lymph node screening in patients with CRC.
Keywords
Humans, Lymphatic Metastasis/pathology, Artificial Intelligence, Diagnosis, Computer-Assisted, Lymph Nodes/pathology, Machine Learning, Colorectal Neoplasms/diagnosis, Colorectal Neoplasms/pathology, colorectal cancer, ensemble model, histopathology, lymph nodes, metastasis detection, transfer learning
Pubmed
Web of science
Open Access
Yes
Create date
28/02/2023 15:39
Last modification date
21/11/2023 8:13