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Prediction of Chemotherapy Response of Osteosarcoma Using Baseline 18F-FDG Textural Features Machine Learning Approaches with PCA
Joint Authors
Jeong, Su Young
Kim, Wook
Byun, Byung Hyun
Kong, Chang-Bae
Song, Won Seok
Lim, Ilhan
Lim, Sang Moo
Woo, Sang-Keun
Source
Contrast Media & Molecular Imaging
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-07-24
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Abstract EN
Purpose.
Patients with high-grade osteosarcoma undergo several chemotherapy cycles before surgical intervention.
Response to chemotherapy, however, is affected by intratumor heterogeneity.
In this study, we assessed the ability of a machine learning approach using baseline 18F-fluorodeoxyglucose (18F-FDG) positron emitted tomography (PET) textural features to predict response to chemotherapy in osteosarcoma patients.
Materials and Methods.
This study included 70 osteosarcoma patients who received neoadjuvant chemotherapy.
Quantitative characteristics of the tumors were evaluated by standard uptake value (SUV), total lesion glycolysis (TLG), and metabolic tumor volume (MTV).
Tumor heterogeneity was evaluated using textural analysis of 18F-FDG PET scan images.
Assessments were performed at baseline and after chemotherapy using 18F-FDG PET; 18F-FDG textural features were evaluated using the Chang-Gung Image Texture Analysis toolbox.
To predict the chemotherapy response, several features were chosen using the principal component analysis (PCA) feature selection method.
Machine learning was performed using linear support vector machine (SVM), random forest, and gradient boost methods.
The ability to predict chemotherapy response was evaluated using the area under the receiver operating characteristic curve (AUC).
Results.
AUCs of the baseline 18F-FDG features SUVmax, TLG, MTV, 1st entropy, and gray level co-occurrence matrix entropy were 0.553, 0538, 0.536, 0.538, and 0.543, respectively.
However, AUCs of the machine learning features linear SVM, random forest, and gradient boost were 0.72, 0.78, and 0.82, respectively.
Conclusion.
We found that a machine learning approach based on 18F-FDG textural features could predict the chemotherapy response using baseline PET images.
This early prediction of the chemotherapy response may aid in determining treatment plans for osteosarcoma patients.
American Psychological Association (APA)
Jeong, Su Young& Kim, Wook& Byun, Byung Hyun& Kong, Chang-Bae& Song, Won Seok& Lim, Ilhan…[et al.]. 2019. Prediction of Chemotherapy Response of Osteosarcoma Using Baseline 18F-FDG Textural Features Machine Learning Approaches with PCA. Contrast Media & Molecular Imaging،Vol. 2019, no. 2019, pp.1-7.
https://search.emarefa.net/detail/BIM-1130224
Modern Language Association (MLA)
Jeong, Su Young…[et al.]. Prediction of Chemotherapy Response of Osteosarcoma Using Baseline 18F-FDG Textural Features Machine Learning Approaches with PCA. Contrast Media & Molecular Imaging No. 2019 (2019), pp.1-7.
https://search.emarefa.net/detail/BIM-1130224
American Medical Association (AMA)
Jeong, Su Young& Kim, Wook& Byun, Byung Hyun& Kong, Chang-Bae& Song, Won Seok& Lim, Ilhan…[et al.]. Prediction of Chemotherapy Response of Osteosarcoma Using Baseline 18F-FDG Textural Features Machine Learning Approaches with PCA. Contrast Media & Molecular Imaging. 2019. Vol. 2019, no. 2019, pp.1-7.
https://search.emarefa.net/detail/BIM-1130224
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1130224