NDLI: A textural feature based tumor therapy response prediction model for longitudinal evaluation with PET imaging

Content Provider	IEEE Xplore Digital Library
Author	George, J. Claes, P. Vunckx, K. Tejpar, S. Deroose, C.M. Nuyts, J. Loeckx, D. Suetens, P.
Copyright Year	2012
Description	Author affiliation: Nuclear Medicine, KU Leuven, Belgium (Vunckx, K.; Deroose, C.M.; Nuyts, J.) \|\| Gastroenterology, KU Leuven, Belgium (Tejpar, S.) \|\| ESAT/PSI/MIC, KU Leuven, Belgium (George, J.; Claes, P.; Loeckx, D.; Suetens, P.)
Abstract	Early therapy response prediction, employing biomarkers such as $^{18}F-fluorodeoxyglucose$ (FDG) followed with positron emission tomography (PET), is an actively researched topic. Traditionally, only the first order intensity based feature estimates are used for the response evaluations. In this work, we focus on the predictive power of lesion texture along with traditional features in follow up studies. Both standard and textural features are extracted after delineating the lesions with state-of-the-art methods. We propose subspace learning to reduce the influence of delineation parameters and to represent each patient as a Grassmann manifold spanned by the extracted feature subspace. We also propose parallel analysis (PA) to find out the optimal subspace dimensionality. Weighted projection distance between longitudinal subspaces is checked for concordance with the progression outcome using time dependent receiver operating characteristics (ROC). The preliminary clinical results suggest that higher order lesion textures have an added value in response evaluations.
Starting Page	1048
Ending Page	1051
File Size	147012
Page Count	4
File Format	PDF
ISBN	9781457718571
ISSN	19457928
e-ISBN	9781457718588
DOI	10.1109/ISBI.2012.6235738
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-05-02
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Lesions Feature extraction Positron emission tomography Image segmentation Medical treatment Predictive models concordance measure PET tumor delineation textural features subspace learning Grassmann manifold principal angles parallel analysis survival analysis time dependent ROC
Content Type	Text
Resource Type	Article

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