Can the preoperative CT-based deep learning radiomics model predict histologic grade and prognosis of chondrosarcoma?

Autor: Nie P; Department of Radiology, the Affiliated Hospital of Qingdao University, No. 16, Jiangsu Road, Qingdao 266003, Shandong, China., Zhao X; Department of Radiology, the Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China., Ma J; Department of Rehabilitative Medicine, Qingdao Municipal Hospital, Qingdao, Shandong, China., Wang Y; Department of Nuclear Medicine, Affiliated Hospital of Jining Medical University, Jining, Shandong, China., Li B; Department of Nuclear Medicine, the Affiliated Hospital of Qingdao University, No. 59, Haier Road, Qingdao 266061, Shandong, China., Li X; Department of Radiology, the Affiliated Hospital of Qingdao University, No. 16, Jiangsu Road, Qingdao 266003, Shandong, China., Li Q; Department of Radiology, the Affiliated Hospital of Qingdao University, No. 16, Jiangsu Road, Qingdao 266003, Shandong, China., Wang Y; GE Healthcare, Shanghai, China., Xu Y; School of Nuclear Science and Technology, University of South China, Hengyang, Hunan, China., Dai Z; Scientific Research Department, Huiying Medical Technology Co., Ltd, Beijing, China., Wu J; Department of Pathology, the Affiliated Hospital of Qingdao University, Qingdao, Shandong, China., Wang N; Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China., Yang G; Department of Nuclear Medicine, the Affiliated Hospital of Qingdao University, No. 59, Haier Road, Qingdao 266061, Shandong, China. Electronic address: ygj_2815@qdu.edu.cn., Hao D; Department of Radiology, the Affiliated Hospital of Qingdao University, No. 16, Jiangsu Road, Qingdao 266003, Shandong, China. Electronic address: haodp2021@qdu.edu.cn., Yu T; Department of Orthopedic Surgery, Qingdao Municipal Hospital, No. 5, Donghai Middle Road, Qingdao 266071, Shandong, China. Electronic address: qdfyytb@126.com.
Jazyk: angličtina
Zdroj: European journal of radiology [Eur J Radiol] 2024 Dec; Vol. 181, pp. 111719. Date of Electronic Publication: 2024 Sep 17.
DOI: 10.1016/j.ejrad.2024.111719
Abstrakt: Background and Purpose: Computed tomography (CT) and biopsy may be insufficient for preoperative evaluation of the grade and outcome of patients with chondrosarcoma. The aim of this study was to develop and validate a CT-based deep learning radiomics model (DLRM) for predicting histologic grade and prognosis in chondrosarcoma (CS).
Methods: A multicenter 211 (training cohort/ test cohort, 127/84) CS patients were enrolled. Radiomics signature (RS), deep learning signature (DLS), and DLRM incorporating radiomics and deep learning features were developed for predicting the grade. Kaplan-Meier survival analysis was used to assess the association of the model-predicted grade with recurrence-free survival (RFS). Model performance was evaluated with the area under the receiver operating characteristic curve (AUC) and the Harrell's concordance index (C-index).
Results: The DLRM (AUC, 0.879; 95 % confidence interval [CI], 0.802-0.956) outperformed (z = 2.773, P=0.006) the RS (AUC, 0.715;95 % CI, 0.606-0.825) in predicting grade in the test cohort. RFS showed significant differences (log-rank test, P<0.05) between low-grade and high-grade patients stratified by DLRM. The DLRM achieved a higher C-index (0.805; 95 % CI, 0.694-0.916) than the RS (0.692, 95 % CI, 0.540-0.844) did in predicting RFS for CS patients in the test cohort.
Conclusion: The DLRM can accurately predict the histologic grade and prognosis in CS.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024 Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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