Domain-specific transfer learning in the automated scoring of
tumor-stroma ratio from histopathological images of colorectal cancer
- URL: http://arxiv.org/abs/2212.14652v1
- Date: Fri, 30 Dec 2022 12:27:27 GMT
- Title: Domain-specific transfer learning in the automated scoring of
tumor-stroma ratio from histopathological images of colorectal cancer
- Authors: Liisa Pet\"ainen, Juha P. V\"ayrynen, Pekka Ruusuvuori, Ilkka
P\"ol\"onen, Sami \"Ayr\"am\"o and Teijo Kuopio
- Abstract summary: Tumor-stroma ratio (TSR) is a prognostic factor for many types of solid tumors.
The method is based on convolutional neural networks which were trained to classify colorectal cancer tissue.
- Score: 1.2264932946286657
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Tumor-stroma ratio (TSR) is a prognostic factor for many types of solid
tumors. In this study, we propose a method for automated estimation of TSR from
histopathological images of colorectal cancer. The method is based on
convolutional neural networks which were trained to classify colorectal cancer
tissue in hematoxylin-eosin stained samples into three classes: stroma, tumor
and other. The models were trained using a data set that consists of 1343 whole
slide images. Three different training setups were applied with a transfer
learning approach using domain-specific data i.e. an external colorectal cancer
histopathological data set. The three most accurate models were chosen as a
classifier, TSR values were predicted and the results were compared to a visual
TSR estimation made by a pathologist. The results suggest that classification
accuracy does not improve when domain-specific data are used in the
pre-training of the convolutional neural network models in the task at hand.
Classification accuracy for stroma, tumor and other reached 96.1$\%$ on an
independent test set. Among the three classes the best model gained the highest
accuracy (99.3$\%$) for class tumor. When TSR was predicted with the best
model, the correlation between the predicted values and values estimated by an
experienced pathologist was 0.57. Further research is needed to study
associations between computationally predicted TSR values and other
clinicopathological factors of colorectal cancer and the overall survival of
the patients.
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