We have located links that may give you full text access.
A Sparse Representation-based Radiomics for Outcome Prediction of Higher Grade Gliomas.
Medical Physics 2018 November 13
PURPOSE: Accurately predicting outcome (i.e., overall survival (OS) time) for higher grade glioma (HGG) has great clinical value and would provide optimized guidelines for treatment planning. Radiomics focuses on revealing underlying pathophysiological information in biomedical images for disease analysis and demonstrates promising prognostic clinical performance. In this paper, we propose a novel sparse representation-based radiomics framework to predict if HGG patients would have long or short OS time.
METHODS: First, taking advantages of the scale invariant feature transform (SIFT) feature in image characterizing, we developed a sparse representation-based method to convert a local SIFT descriptor into a global tumor feature. Next, because preserving sample structure is beneficial for feature selection, we proposed a locality preserving projection and sparse representation-combined feature selection method to select more discriminative features for tumor classification. Finally, we employed a multi-feature collaborative sparse representation classification to combine the information of multi-modal images to classify OS time.
RESULTS: Three experiments were performed on the two datasets provided by different institutions. That are training and independent testing on dataset 1 (135 subjects), on dataset 2 (86 subjects) and on the combination of dataset1 and dataset 2, respectively. Experimental results demonstrated that the proposed method achieved encouraging prediction performance, exhibiting a testing accuracy of 93.33% on dataset 1 (one modality), 92.31% on dataset 2 (two modalities) and 87.93% on the combined dataset (one modality).
CONCLUSIONS: The sparse representation theory provides reasonable solutions to feature extraction, feature selection and classification for radiomics. This study provides a promising tool to enhance the prediction performance of HGG patient's outcome. This article is protected by copyright. All rights reserved.
METHODS: First, taking advantages of the scale invariant feature transform (SIFT) feature in image characterizing, we developed a sparse representation-based method to convert a local SIFT descriptor into a global tumor feature. Next, because preserving sample structure is beneficial for feature selection, we proposed a locality preserving projection and sparse representation-combined feature selection method to select more discriminative features for tumor classification. Finally, we employed a multi-feature collaborative sparse representation classification to combine the information of multi-modal images to classify OS time.
RESULTS: Three experiments were performed on the two datasets provided by different institutions. That are training and independent testing on dataset 1 (135 subjects), on dataset 2 (86 subjects) and on the combination of dataset1 and dataset 2, respectively. Experimental results demonstrated that the proposed method achieved encouraging prediction performance, exhibiting a testing accuracy of 93.33% on dataset 1 (one modality), 92.31% on dataset 2 (two modalities) and 87.93% on the combined dataset (one modality).
CONCLUSIONS: The sparse representation theory provides reasonable solutions to feature extraction, feature selection and classification for radiomics. This study provides a promising tool to enhance the prediction performance of HGG patient's outcome. This article is protected by copyright. All rights reserved.
Full text links
Related Resources
Trending Papers
Challenges in Septic Shock: From New Hemodynamics to Blood Purification Therapies.Journal of Personalized Medicine 2024 Februrary 4
Molecular Targets of Novel Therapeutics for Diabetic Kidney Disease: A New Era of Nephroprotection.International Journal of Molecular Sciences 2024 April 4
The 'Ten Commandments' for the 2023 European Society of Cardiology guidelines for the management of endocarditis.European Heart Journal 2024 April 18
A Guide to the Use of Vasopressors and Inotropes for Patients in Shock.Journal of Intensive Care Medicine 2024 April 14
Diagnosis and Management of Cardiac Sarcoidosis: A Scientific Statement From the American Heart Association.Circulation 2024 April 19
Essential thrombocythaemia: A contemporary approach with new drugs on the horizon.British Journal of Haematology 2024 April 9
Get seemless 1-tap access through your institution/university
For the best experience, use the Read mobile app
All material on this website is protected by copyright, Copyright © 1994-2024 by WebMD LLC.
This website also contains material copyrighted by 3rd parties.
By using this service, you agree to our terms of use and privacy policy.
Your Privacy Choices
You can now claim free CME credits for this literature searchClaim now
Get seemless 1-tap access through your institution/university
For the best experience, use the Read mobile app