Improving performance and generalizability in radiogenomics: a pilot study for prediction of IDH1/2 mutation status in gliomas with multicentric data
Santinha, J.
; Matos, C.
;
Figueiredo, M. A. T.
; Papanikolaou, N.
Journal of Medical Imaging Vol. 8, Nº 03, pp. 031905-1 - 031905-23, April, 2021.
ISSN (print): 2329-4302
ISSN (online): 2329-4310
Scimago Journal Ranking: 0,88 (in 2021)
Digital Object Identifier: 10.1117/1.JMI.8.3.031905
Abstract
Radiogenomics offers a potential virtual and noninvasive biopsy. However, radiogenomics models often suffer from generalizability issues, which cause a performance degradation on unseen data. In MRI, differences in the sequence parameters, manufacturers, and scanners make this generalizability issue worse. Such image acquisition information may be used to define different environments and select robust and invariant radiomic features associated with the clinical outcome that should be included in radiomics/radiogenomics models.
