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Li W, Dong S, Wang B, Wang H, Xu C, Zhang K, Li W, Hu Z, Li X, Liu Q, Wu R, Yin C. The Construction and Development of a Clinical Prediction Model to Assess Lymph Node Metastases in Osteosarcoma. Front Public Health 2022; 9:813625. [PMID: 35071175 PMCID: PMC8770939 DOI: 10.3389/fpubh.2021.813625] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Accepted: 12/06/2021] [Indexed: 12/23/2022] Open
Abstract
Background: This study aimed to construct a clinical prediction model for osteosarcoma patients to evaluate the influence factors for the occurrence of lymph node metastasis (LNM). Methods: In our retrospective study, a total of 1,256 patients diagnosed with chondrosarcoma were enrolled from the SEER (Surveillance, Epidemiology, and End Results) database (training cohort, n = 1,144) and multicenter dataset (validation cohort, n = 112). Both the univariate and multivariable logistic regression analysis were performed to identify the potential risk factors of LNM in osteosarcoma patients. According to the results of multivariable logistic regression analysis, A nomogram were established and the predictive ability was assessed by calibration plots, receiver operating characteristics (ROCs) curve, and decision curve analysis (DCA). Moreover, Kaplan-Meier plot of overall survival (OS) was plot and a web calculator visualized the nomogram. Results: Five independent risk factors [chemotherapy, surgery, lung metastases, lymphatic metastases (M-stage) and tumor size (T-stage)] were identified by multivariable logistic regression analysis. What's more, calibration plots displayed great power both in training and validation group. DCA presented great clinical utility. ROCs curve provided the predictive ability in the training cohort (AUC = 0.805) and the validation cohort (AUC = 0.808). Moreover, patients in LNN group had significantly better survival than that in LNP group both in training and validation group. Conclusion: In this study, we constructed and developed a nomogram with risk factors, which performed well in predicting risk factors of LNM in osteosarcoma patients. It may give a guide for surgeons and oncologists to optimize individual treatment and make a better clinical decision.
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Affiliation(s)
- Wenle Li
- Department of Orthopedics, Xianyang Central Hospital, Xianyang, China.,Clinical Medical Research Center, Xianyang Central Hospital, Xianyang, China
| | - Shengtao Dong
- Department of Spine Surgery, Second Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Bing Wang
- Clinical Medical Research Center, Xianyang Central Hospital, Xianyang, China
| | - Haosheng Wang
- Department of Orthopaedics, The Second Hospital of Jilin University, Changchun, China
| | - Chan Xu
- Clinical Medical Research Center, Xianyang Central Hospital, Xianyang, China
| | - Kai Zhang
- Department of Orthopedics, Xianyang Central Hospital, Xianyang, China.,Clinical Medical Research Center, Xianyang Central Hospital, Xianyang, China
| | - Wanying Li
- Clinical Medical Research Center, Xianyang Central Hospital, Xianyang, China
| | - Zhaohui Hu
- Department of Spine Surgery, Liuzhou People's Hospital, Liuzhou, China
| | - Xiaoping Li
- Shulan International Medical College, Zhejiang Shuren University, Hangzhou, China
| | - Qiang Liu
- Department of Orthopedics, Xianyang Central Hospital, Xianyang, China
| | - Rilige Wu
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
| | - Chengliang Yin
- Faculty of Medicine, Macau University of Science and Technology, Macau, Macao SAR, China
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Chaudhari AJ, Raynor WY, Gholamrezanezhad A, Werner TJ, Rajapakse CS, Alavi A. Total-Body PET Imaging of Musculoskeletal Disorders. PET Clin 2021; 16:99-117. [PMID: 33218607 PMCID: PMC7684980 DOI: 10.1016/j.cpet.2020.09.012] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Imaging of musculoskeletal disorders, including arthritis, infection, osteoporosis, sarcopenia, and malignancies, is often limited when using conventional modalities such as radiography, computed tomography (CT), and MR imaging. As a result of recent advances in Positron Emission Tomography (PET) instrumentation, total-body PET/CT offers a longer axial field-of-view, higher geometric sensitivity, and higher spatial resolution compared with standard PET systems. This article discusses the potential applications of total-body PET/CT imaging in the assessment of musculoskeletal disorders.
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Affiliation(s)
- Abhijit J Chaudhari
- Department of Radiology, University of California Davis, 4860 Y Street, Sacramento, CA 95825, USA.
| | - William Y Raynor
- Department of Radiology, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, USA; Drexel University College of Medicine, 2900 West Queen Lane, Philadelphia, PA 19129, USA
| | - Ali Gholamrezanezhad
- Keck School of Medicine, University of Southern California, 1520 San Pablo Street, Los Angeles, CA 90033, USA
| | - Thomas J Werner
- Department of Radiology, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, USA
| | - Chamith S Rajapakse
- Department of Radiology, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, USA
| | - Abass Alavi
- Department of Radiology, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, USA
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