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

ABSTRACT: 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 radiom

SUBMITTER: Vandaele R 

PROVIDER: S-EPMC9868648 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature