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- Title
Deep learning based tumor-stroma ratio scoring in colon cancer correlates with microscopic assessment.
- Authors
Smit, Marloes A.; Ciompi, Francesco; Bokhorst, John-Melle; van Pelt, Gabi W.; Geessink, Oscar G. F.; Putter, Hein; Tollenaar, Rob A. E. M.; van Krieken, J. Han J. M.; Mesker, Wilma E.; van der Laak, Jeroen A. W. M.
- Abstract
Background: The amount of stroma within the primary tumor is a prognostic parameter for colon cancer patients. This phenomenon can be assessed using the tumor–stroma ratio (TSR), which classifies tumors in stroma-low (≤50% stroma) and stroma-high (>50% stroma). Although the reproducibility for TSR determination is good, improvement might be expected from automation. The aim of this study was to investigate whether the scoring of the TSR in a semi-and fully automated method using deep learning algorithms is feasible. Methods: A series of 75 colon cancer slides were selected from a trial series of the UNITED study. For the standard determination of the TSR, 3 observers scored the histological slides. Next, the slides were digitized, color normalized, and the stroma percentages were scored using semi- and fully automated deep learning algorithms. Correlations were determined using intraclass correlation coefficients (ICCs) and Spearman rank correlations. Results: 37 (49%) cases were classified as stroma-low and 38 (51%) as stroma-high by visual estimation. A high level of concordance between the 3 observers was reached, with ICCs of 0.91, 0.89, and 0.94 (all P < .001). Between visual and semi-automated assessment the ICC was 0.78 (95% CI 0.23-0.91, P-value 0.005), with a Spearman correlation of 0.88 (P < .001). Spearman correlation coefficients above 0.70 (N=3) were observed for visual estimation versus the fully automated scoring procedures. Conclusion: Good correlations were observed between standard visual TSR determination and semi- and fully automated TSR scores. At this point, visual examination has the highest observer agreement, but semi-automated scoring could be helpful to support pathologists.
- Subjects
COLON cancer; MACHINE learning; DEEP learning; INTRACLASS correlation; RANK correlation (Statistics); ARTIFICIAL intelligence
- Publication
Journal of Pathology Informatics, 2023, Vol 14, p1
- ISSN
2229-5089
- Publication type
Article
- DOI
10.1016/j.jpi.2023.100191