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Stability of radiomic features of liver lesions from manual delineation in CT scans
Author(s): Jan Hendrik Moltz
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Paper Abstract

We investigate the stability of radiomic features under variations in manual delineation of liver tumors. The analysis is based on 13 CT scans with ten expert segmentations of a lesion per patient. We computed 110 firstorder, shape, and texture features using the open-source software pyradiomics and created a ranking by intra-class correlation (ICC), discarding highly correlated features. Half of the 27 remaining features have very good stability (ICC > 0.9), with features relating to size, simple texture and average intensity performing best. Elongation and kurtosis are by far the least stable features (ICC < 0.65) and should be avoided.

Paper Details

Date Published: 13 March 2019
PDF: 7 pages
Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 109501W (13 March 2019); doi: 10.1117/12.2512406
Show Author Affiliations
Jan Hendrik Moltz, Fraunhofer Institute for Digital Medicine MEVIS (Germany)

Published in SPIE Proceedings Vol. 10950:
Medical Imaging 2019: Computer-Aided Diagnosis
Kensaku Mori; Horst K. Hahn, Editor(s)

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