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.
Original language | English (US) |
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Title of host publication | Medical Imaging 2017 |
Subtitle of host publication | Computer-Aided Diagnosis |
Publisher | SPIE |
Volume | 10134 |
ISBN (Electronic) | 9781510607132 |
DOIs | |
State | Published - 2017 |
Event | Medical Imaging 2017: Computer-Aided Diagnosis - Orlando, United States Duration: Feb 13 2017 → Feb 16 2017 |
Other
Other | Medical Imaging 2017: Computer-Aided Diagnosis |
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Country | United States |
City | Orlando |
Period | 2/13/17 → 2/16/17 |
Keywords
- Dictionary learning
- Distant failure
- NSCLC
- SABR
- Sparse representation
ASJC Scopus subject areas
- Electronic, Optical and Magnetic Materials
- Atomic and Molecular Physics, and Optics
- Biomaterials
- Radiology Nuclear Medicine and imaging