| Autor: |
Justin D. Krogue, Shekoofeh Azizi, Fraser Tan, Isabelle Flament-Auvigne, Trissia Brown, Markus Plass, Robert Reihs, Heimo Müller, Kurt Zatloukal, Pema Richeson, Greg S. Corrado, Lily H. Peng, Craig H. Mermel, Yun Liu, Po-Hsuan Cameron Chen, Saurabh Gombar, Thomas Montine, Jeanne Shen, David F. Steiner, Ellery Wulczyn |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Communications Medicine, Vol 3, Iss 1, Pp 1-9 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2730-664X |
| DOI: |
10.1038/s43856-023-00282-0 |
| Popis: |
Abstract Background Presence of lymph node metastasis (LNM) influences prognosis and clinical decision-making in colorectal cancer. However, detection of LNM is variable and depends on a number of external factors. Deep learning has shown success in computational pathology, but has struggled to boost performance when combined with known predictors. Methods Machine-learned features are created by clustering deep learning embeddings of small patches of tumor in colorectal cancer via k-means, and then selecting the top clusters that add predictive value to a logistic regression model when combined with known baseline clinicopathological variables. We then analyze performance of logistic regression models trained with and without these machine-learned features in combination with the baseline variables. Results The machine-learned extracted features provide independent signal for the presence of LNM (AUROC: 0.638, 95% CI: [0.590, 0.683]). Furthermore, the machine-learned features add predictive value to the set of 6 clinicopathologic variables in an external validation set (likelihood ratio test, p |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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