1
|
Petäinen L, Väyrynen JP, Ruusuvuori P, Pölönen I, Äyrämö S, Kuopio T. Domain-specific transfer learning in the automated scoring of tumor-stroma ratio from histopathological images of colorectal cancer. PLoS One 2023; 18:e0286270. [PMID: 37235626 DOI: 10.1371/journal.pone.0286270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Accepted: 05/11/2023] [Indexed: 05/28/2023] Open
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.
Collapse
Affiliation(s)
- Liisa Petäinen
- Faculty of Information Technology, University of Jyväskylä, Jyväskylä, Finland
| | - Juha P Väyrynen
- Cancer and Translational Medicine Research Unit, Medical Research Center, Oulu University Hospital, and University of Oulu, Oulu, Finland
| | - Pekka Ruusuvuori
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Cancer Research Unit, Institute of Biomedicine, University of Turku, Turku, Finland
- FICAN West Cancer Centre, Turku University Hospital, Turku, Finland
| | - Ilkka Pölönen
- Faculty of Information Technology, University of Jyväskylä, Jyväskylä, Finland
| | - Sami Äyrämö
- Faculty of Information Technology, University of Jyväskylä, Jyväskylä, Finland
| | - Teijo Kuopio
- Department of Education and Research, Hospital Nova of Central Finland, Jyväskylä, Finland
- Department of Biological and Environmental Science, University of Jyväskylä, Jyväskylä, Finland
- Department of Pathology, Hospital Nova of Central Finland, Jyväskylä, Finland
| |
Collapse
|