期刊论文

Hao, DP;Yang, GJ;Yu, TB

英文

European Journal of Radiology

0720-048X

2024

181

111719

10.1016/j.ejrad.2024.111719

CRediT authorship contribution statement Pei Nie: Writing – original draft, Investigation, acquisition, Formal analysis, Data curation. Xia Zhao: Writing – original draft, Investigation, Formal analysis, Data curation. Jinlong Ma: Validation, Investigation, Formal analysis, Data curation. Yicong Wang: Validation, Investigation, Formal analysis, Data curation. Ben Li: Formal analysis, Data curation. Xiaoli Li: Formal analysis, Data curation. Qiyuan Li: Formal analysis, Data curation. Yanmei Wang: Software, Methodology. Yuchao Acknowledgements This study was funded by the Taishan Scholar Foundation of Shandong Province (tsqn202408392), the National Natural Science Foundation of China (82172035, 82472067), and the Postdoctoral Science Foundation of China (2021M701811). The funding source had no role in study design, the collection, analysis and interpretation of data, the writing of the report; and in the decision to submit the article for publication.

本校为其他机构

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). 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 ...