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Proceedings Paper

Distant failure prediction for early stage NSCLC by analyzing PET with sparse representation
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Paper Abstract

Positron emission tomography (PET) imaging has been widely explored for treatment outcome prediction. Radiomicsdriven methods provide a new insight to quantitatively explore underlying information from PET images. However, it is still a challenging problem to automatically extract clinically meaningful features for prognosis. In this work, we develop a PET-guided distant failure predictive model for early stage non-small cell lung cancer (NSCLC) patients after stereotactic ablative radiotherapy (SABR) by using sparse representation. The proposed method does not need precalculated features and can learn intrinsically distinctive features contributing to classification of patients with distant failure. The proposed framework includes two main parts: 1) intra-tumor heterogeneity description; and 2) dictionary pair learning based sparse representation. Tumor heterogeneity is initially captured through anisotropic kernel and represented as a set of concatenated vectors, which forms the sample gallery. Then, given a test tumor image, its identity (i.e., distant failure or not) is classified by applying the dictionary pair learning based sparse representation. We evaluate the proposed approach on 48 NSCLC patients treated by SABR at our institute. Experimental results show that the proposed approach can achieve an area under the characteristic curve (AUC) of 0.70 with a sensitivity of 69.87% and a specificity of 69.51% using a five-fold cross validation.

Paper Details

Date Published: 3 March 2017
PDF: 7 pages
Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 101343W (3 March 2017); doi: 10.1117/12.2254686
Show Author Affiliations
Hongxia Hao, The Univ. of Texas Southwestern Medical Ctr. (United States)
Xidian Univ. (China)
Zhiguo Zhou, The Univ. of Texas Southwestern Medical Ctr. (United States)
Jing Wang, The Univ. of Texas Southwestern Medical Ctr. (United States)

Published in SPIE Proceedings Vol. 10134:
Medical Imaging 2017: Computer-Aided Diagnosis
Samuel G. Armato III; Nicholas A. Petrick, Editor(s)

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