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

Pei Nie; Xia Zhao; Jinlong Ma; Yicong Wang; Ben Li; Xiaoli Li; Qiyuan Li; Yanmei Wang; Yuchao Xu; Zhengjun Dai; Jie Wu; Ning Wang; Guangjie Yang; Dapeng Hao; Tengbo Yu

doi:10.1016/j.ejrad.2024.111719

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

Eur J Radiol. 2024 Sep 17:181:111719. doi: 10.1016/j.ejrad.2024.111719. Online ahead of print.

Authors

Pei Nie¹, Xia Zhao², Jinlong Ma³, Yicong Wang⁴, Ben Li⁵, Xiaoli Li¹, Qiyuan Li¹, Yanmei Wang⁶, Yuchao Xu⁷, Zhengjun Dai⁸, Jie Wu⁹, Ning Wang¹⁰, Guangjie Yang¹¹, Dapeng Hao¹², Tengbo Yu¹³

Affiliations

¹ Department of Radiology, the Affiliated Hospital of Qingdao University, No. 16, Jiangsu Road, Qingdao 266003, Shandong, China.
² Department of Radiology, the Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China.
³ Department of Rehabilitative Medicine, Qingdao Municipal Hospital, Qingdao, Shandong, China.
⁴ Department of Nuclear Medicine, Affiliated Hospital of Jining Medical University, Jining, Shandong, China.
⁵ Department of Nuclear Medicine, the Affiliated Hospital of Qingdao University, No. 59, Haier Road, Qingdao 266061, Shandong, China.
⁶ GE Healthcare, Shanghai, China.
⁷ School of Nuclear Science and Technology, University of South China, Hengyang, Hunan, China.
⁸ Scientific Research Department, Huiying Medical Technology Co., Ltd, Beijing, China.
⁹ Department of Pathology, the Affiliated Hospital of Qingdao University, Qingdao, Shandong, China.
¹⁰ Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China.
¹¹ 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.
¹² Department of Radiology, the Affiliated Hospital of Qingdao University, No. 16, Jiangsu Road, Qingdao 266003, Shandong, China. Electronic address: haodp2021@qdu.edu.cn.
¹³ Department of Orthopedic Surgery, Qingdao Municipal Hospital, No. 5, Donghai Middle Road, Qingdao 266071, Shandong, China. Electronic address: qdfyytb@126.com.

PMID: 39305748
DOI: 10.1016/j.ejrad.2024.111719

Abstract

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.

Keywords: Chondrosarcoma; Deep learning; Grade; Radiomics; Tomography, X-ray computed.