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Radiomic signature: A novel magnetic resonance imaging-based prognostic biomarker in patients with skull base chordoma

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机构: [a]School of Electronics and Information, Xi’an Polytechnic University, School of Life Science and Technology, Xidian University, Xi’an [b]Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi’an [c]CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing [d]Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China [e]China National Clinical Research Center for Neurological Diseases, Beijing, China [f]Department of Neuropathology, Beijing Neurosurgical Institute, Beijing, China [g]School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China [h]Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine, Beihang University, Beijing, China
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关键词: Biomarkers Magnetic resonance imaging Prognosis Progression-free survival Radiomics Skull base chordoma

摘要:
Background and purpose: We used radiomic analysis to establish a radiomic signature based on anatomical magnetic resonance imaging (MRI) sequences and explore its effectiveness as a novel prognostic biomarker for skull base chordoma (SBC). Materials and methods: In this retrospective study, radiomic analysis was performed using preoperative axial T1 FLAIR, T2-weighted, and enhanced T1 FLAIR from a single hospital. The primary clinical endpoint was progression-free survival. A total of 1860 3-D radiomic features were extracted from manually segmented region of interest. Pearson correlation coefficient was used for feature dimensional reduction and a ridge regression-based Cox proportional hazards model was used to determine a radiomic signature. Afterwards, radiomic signature and nine other potential prognostic factors, including age, gender, histological subtype, dural invasion, blood supply, adjuvant radiotherapy, extent of resection, preoperative KPS, and postoperative KPS were analyzed to build a radiomic nomogram and a clinical model. Finally, we compared the nomogram with each prognostic factor/model by DeLong's test. Results: A total of 148 SBC patients were enrolled, including 64 with disease progression. The median follow-up time was 52 months (range 4–122 months). The Harrell's concordance index of the radiomic signature was 0.745 (95% CI, 0.709–0.781) for the validation cohort, and its discrimination accuracy in predicting progression risk at 5 years in the same cohort was 82.4% (95% CI, 72.6–89.7%). Conclusions: The radiomics is a low-cost, non-invasive method to predict SBC prognosis preoperatively. Radiomic signature is a potential prognostic biomarker that may allow the individualized evaluation of patients with SBC. © 2019 Elsevier B.V.

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出版当年[2018]版:
大类 | 2 区 医学
小类 | 2 区 肿瘤学 2 区 核医学
最新[2023]版:
大类 | 1 区 医学
小类 | 2 区 肿瘤学 2 区 核医学
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出版当年[2017]版:
Q1 ONCOLOGY Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
最新[2023]版:
Q1 ONCOLOGY Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

影响因子: 最新[2023版] 最新五年平均 出版当年[2017版] 出版当年五年平均 出版前一年[2016版] 出版后一年[2018版]

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第一作者机构: [a]School of Electronics and Information, Xi’an Polytechnic University, School of Life Science and Technology, Xidian University, Xi’an [b]Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi’an [c]CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing [h]Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine, Beihang University, Beijing, China
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通讯作者:
通讯机构: [b]Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi’an [c]CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing [d]Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China [e]China National Clinical Research Center for Neurological Diseases, Beijing, China [g]School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China [h]Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine, Beihang University, Beijing, China [*1]Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, 119 West Road of South Fourth Ring, Fengtai District, Beijing 100050, China [*2]CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
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