ArticleFrontiers in oncology2021
The Clinical Characteristics and Prediction Nomograms for Primary Spine Malignancies.
Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed, 14 citations in OpenAlex.
- Radiogenomic method combining DNA methylation profiles and magnetic resonance imaging radiomics predicts patient prognosis in skull base chordoma.Clinical epigenetics · 2025Article
- Establishment of a nomogram for potential prediction of lung metastasis in patients with primary limb bone tumors: a study based on the SEER database.Translational cancer research · 2024Article
- A Novel Clinical Tool to Predict Cancer-specific Survival in Postoperative Patients With Primary Spinal and Pelvic Sarcomas: A Large Population-Based Retrospective Cohort Study.Global spine journal · 2024Article
- Construction of a Tool to Predict Overall Survival of Patients With Primary Spinal Tumors After Surgical Resection: A Real-World Analysis Based on the Surveillance, Epidemiology, and End Results Database.Global spine journal · 2023Article
- Development and validation of nomograms predicting overall and cancer-specific survival for non-metastatic primary malignant bone tumor of spine patients.Scientific reports · 2023Article
- A Simple-to-Use Nomogram for Predicting Postoperative Early Death Risk in Elderly Patients with Spinal Tumors: A Population-Based Study.Journal of oncology · 2023Article
- Clinical characteristics, prognostic factors, and predictive model for elderly primary spinal tumor patients who are difficult to tolerate surgery or refuse surgery.Frontiers in oncology · 2022Article
- Individualized assessment of risk and overall survival in patients newly diagnosed with primary osseous spinal neoplasms with synchronous distant metastasis.Frontiers in public health · 2022Article
- Construction, Validation, and Visualization of Two Web-Based Nomograms for Predicting Overall Survival and Cancer-Specific Survival in Elderly Patients with Primary Osseous Spinal Neoplasms.Journal of oncology · 2022Article
- Identifying the Best Candidate for Primary Tumor Resection in Patients With Advanced Osteosarcoma.Cancer control : journal of the Moffitt Cancer CenterArticle
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPrimary spine malignancies (PSMs) are relatively rare in bone tumors. Due to their rarity, the clinical characteristics and prognostic factors are still ambiguous. In this study, we aim to identify the clinical features and proposed prediction nomograms for patients with PSMs.
methodsPatients diagnosed with PSMs including chordoma, osteosarcoma, chondrosarcoma, Ewing sarcoma, and malignant giant cell tumor of bone (GCTB) between 1975 and 2016 were selected from the Surveillance, Epidemiology, and End Results (SEER) database. The patient and tumor characteristics were described based on clinical information. The significant prognostic factors of overall survival (OS) and cancer-specific survival (CSS) were identified by the univariate and multivariate Cox analysis. Then, the nomograms for OS and CSS were established based on the selected predictors and their accuracy was explored by the Cox-Snell residual plot, area under the curve (AUC) of receiver operator characteristic (ROC) and calibration curve.
resultsThe clinical information of 1,096 patients with PSMs was selected from the SEER database between 1975 and 2016. A total of 395 patients were identified with full survival and treatment data between 2004 and 2016. Chordoma is the commonest tumor with 400 cases, along 172 cases with osteosarcoma, 240 cases with chondrosarcoma, 262 cases with Ewing sarcoma and 22 cases with malignant GCTB. The univariate and multivariate analyses revealed that older age (Age > 60), distant metastasis, chemotherapy, and Surgery were independent predictors for OS and/or CSS. Based on these results, the nomograms were established with a better applicability (AUC for CSS: 0.784; AUC for OS: 0.780).
conclusionsThis study provides the statistics evidence for the clinical characteristics and predictors for patients with PSMs based on a large size population. Additionally, precise prediction nomograms were also established with a well-applicability.
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