Topological data analysis of thoracic radiographic images shows improved radiomics-based lung tumor histology prediction.

Autor:	Vandaele R; Department of Applied Mathematics, Computer Science and Statistics, Ghent University, 9000 Ghent, Belgium.; Data Mining and Modeling for Biomedicine, VIB Inflammation Research Center, 9052 Ghent, Belgium.; IDLab, Department of Electronics and Information Systems, Ghent University, Gent, Belgium., Mukherjee P; Stanford Center for Biomedical Informatics Research (BMIR), Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA., Selby HM; Stanford Center for Biomedical Informatics Research (BMIR), Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA.; Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA 94305, USA., Shah RP; Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA.; Department of Radiology, Stanford University, Stanford, CA, USA., Gevaert O; Stanford Center for Biomedical Informatics Research (BMIR), Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA.; Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA 94305, USA.
Jazyk:	angličtina
Zdroj:	Patterns (New York, N.Y.) [Patterns (N Y)] 2022 Dec 12; Vol. 4 (1), pp. 100657. Date of Electronic Publication: 2022 Dec 12 (Print Publication: 2023).
DOI:	10.1016/j.patter.2022.100657
Abstrakt:	Topological data analysis provides tools to capture wide-scale structural shape information in data. Its main method, persistent homology, has found successful applications to various machine-learning problems. Despite its recent gain in popularity, much of its potential for medical image analysis remains undiscovered. We explore the prominent learning problems on thoracic radiographic images of lung tumors for which persistent homology improves radiomic-based learning. It turns out that our topological features well capture complementary information important for benign versus malignant and adenocarcinoma versus squamous cell carcinoma tumor prediction while contributing less consistently to small cell versus non-small cell-an interesting result in its own right. Furthermore, while radiomic features are better for predicting malignancy scores assigned by expert radiologists through visual inspection, we find that topological features are better for predicting more accurate histology assessed through long-term radiology review, biopsy, surgical resection, progression, or response. Competing Interests: The authors declare no competing interests. (© 2022 The Author(s).)
Databáze:	MEDLINE
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