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- Title
Improved accuracy in colorectal cancer tissue decomposition through refinement of established deep learning solutions.
- Authors
Prezja, Fabi; Äyrämö, Sami; Pölönen, Ilkka; Ojala, Timo; Lahtinen, Suvi; Ruusuvuori, Pekka; Kuopio, Teijo
- Abstract
Hematoxylin and eosin-stained biopsy slides are regularly available for colorectal cancer patients. These slides are often not used to define objective biomarkers for patient stratification and treatment selection. Standard biomarkers often pertain to costly and slow genetic tests. However, recent work has shown that relevant biomarkers can be extracted from these images using convolutional neural networks (CNNs). The CNN-based biomarkers predicted colorectal cancer patient outcomes comparably to gold standards. Extracting CNN-biomarkers is fast, automatic, and of minimal cost. CNN-based biomarkers rely on the ability of CNNs to recognize distinct tissue types from microscope whole slide images. The quality of these biomarkers (coined 'Deep Stroma') depends on the accuracy of CNNs in decomposing all relevant tissue classes. Improving tissue decomposition accuracy is essential for improving the prognostic potential of CNN-biomarkers. In this study, we implemented a novel training strategy to refine an established CNN model, which then surpassed all previous solutions. We obtained a 95.6% average accuracy in the external test set and 99.5% in the internal test set. Our approach reduced errors in biomarker-relevant classes, such as Lymphocytes, and was the first to include interpretability methods. These methods were used to better apprehend our model's limitations and capabilities.
- Subjects
DEEP learning; COLORECTAL cancer; CONVOLUTIONAL neural networks; CANCER prognosis; TISSUES
- Publication
Scientific Reports, 2023, Vol 13, Issue 1, p1
- ISSN
2045-2322
- Publication type
Article
- DOI
10.1038/s41598-023-42357-x