Histological subtypes, i.e. adenocarcinoma (ADN) and squamous cell carcinoma (SCC), identified from a single biopsy occasionally differ from those from actual surgical resections in NSCLC. For increasing the classification accuracy, we aim to develop an automated approach for classifying histological subtypes of NSCLC using Gaussian, linear and polynomial support vector machines (SVMs) with radiomic features. Classification models of Gaussian, linear and polynomial SVMs constructed with radiomic features achieved the areas under the curves of 0.7542, 0.7522 and 0.7531, respectively. Histological subtypes of NSCLC could be classified into ADN and SCC using a Gaussian SVM with radiomic features.

Date Published: 27 March 2019
PDF: 4 pages
Proc. SPIE 11050, International Forum on Medical Imaging in Asia 2019, 110500P (27 March 2019); doi: 10.1117/12.2521511

Published in SPIE Proceedings Vol. 11050:
International Forum on Medical Imaging in Asia 2019
Feng Lin; Hiroshi Fujita; Jong Hyo Kim, Editor(s)