Deep learning based tumor–stroma ratio scoring in colon cancer correlates with microscopic assessment
| العنوان: | Deep learning based tumor–stroma ratio scoring in colon cancer correlates with microscopic assessment |
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| المؤلفون: | 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, 1967 |
| المصدر: | Journal of Pathology Informatics. 14 |
| الوصف: | 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. © 2023 The Authors |
| وصف الملف: | electronic |
| URL الوصول: | https://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-200781 https://doi.org/10.1016/j.jpi.2023.100191 https://liu.diva-portal.org/smash/get/diva2:1835988/FULLTEXT01.pdf |
| قاعدة البيانات: | SwePub |
| تدمد: | 22295089 21533539 |
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| DOI: | 10.1016/j.jpi.2023.100191 |