1. Fully Automatic Quantitative Measurement of 18F-FDG PET/CT in Thymic Epithelial Tumors Using a Convolutional Neural Network.
- Author
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Han S, Oh JS, Kim YI, Seo SY, Lee GD, Park MJ, Choi S, Kim HR, Kim YH, Kim DK, Park SI, and Ryu JS
- Subjects
- Fluorodeoxyglucose F18 metabolism, Glycolysis, Humans, Neural Networks, Computer, Positron Emission Tomography Computed Tomography, Prognosis, Retrospective Studies, Tumor Burden, Neoplasms, Glandular and Epithelial diagnostic imaging, Thymoma, Thymus Neoplasms diagnostic imaging
- Abstract
Objectives: The aim of this study was to develop a deep learning (DL)-based segmentation algorithm for automatic measurement of metabolic parameters of 18F-FDG PET/CT in thymic epithelial tumors (TETs), comparable performance to manual volumes of interest., Patients and Methods: A total of 186 consecutive patients with resectable TETs and preoperative 18F-FDG PET/CT were retrospectively enrolled (145 thymomas, 41 thymic carcinomas). A quasi-3D U-net architecture was trained to resemble ground-truth volumes of interest. Segmentation performance was assessed using the Dice similarity coefficient. Agreements between manual and DL-based automated extraction of SUVmax, metabolic tumor volume (MTV), total lesion glycolysis (TLG), and 63 radiomics features were evaluated via concordance correlation coefficients (CCCs) and linear regression slopes. Diagnostic and prognostic values were compared in terms of area under the receiver operating characteristics curve (AUC) for thymic carcinoma and hazards ratios (HRs) for freedom from recurrence., Results: The mean Dice similarity coefficient was 0.83 ± 0.34. Automatically measured SUVmax (slope, 0.97; CCC, 0.92), MTV (slope, 0.94; CCC, 0.96), and TLG (slope, 0.96; CCC, 0.96) were in good agreement with manual measurements. The mean CCC and slopes were 0.88 ± 0.06 and 0.89 ± 0.05, respectively, for the radiomics parameters. Automatically measured SUVmax, MTV, and TLG showed good diagnostic accuracy for thymic carcinoma (AUCs: SUVmax, 0.95; MTV, 0.85; TLG, 0.87) and significant prognostic value (HRs: SUVmax, 1.31 [95% confidence interval, 1.16-1.48]; MTV, 2.11 [1.09-4.06]; TLG, 1.90 [1.12-3.23]). No significant differences in the AUCs or HRs were found between automatic and manual measurements for any of the metabolic parameters., Conclusions: Our DL-based model provides comparable segmentation performance and metabolic parameter values to manual measurements in TETs., Competing Interests: Conflicts of interest and sources of funding: This work was supported by the National Research Foundation of Korea grant funded by the Korean government (Ministry of Science and ICT; numbers NRF-2020M2D9A1094074 and 2021R1A2C3009056), and by a grant from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute, funded by the Ministry of Health & Welfare, Republic of Korea (HI18C2383). The funders had no role in the conceptualization or design of the study; in the collection, analysis, and interpretation of the data; in the writing of the manuscript; or in the decision to submit the manuscript for publication. The authors declare no competing interests., (Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.)
- Published
- 2022
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