ArticleJournal of neuro-oncology2019
Nomograms for predicting the overall and cause-specific survival in patients with malignant peripheral nerve sheath tumor: a population-based study.
Article in Journal of neuro-oncology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 4 of them syntheses that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
23 citing papers in PubMed, 4 syntheses or guidelines pooled it, 45 citations in OpenAlex.
- Overinterpretation of findings in machine learning prediction model studies in oncology: a systematic review.Journal of clinical epidemiology · 2023Pooled it
- Methodological conduct of prognostic prediction models developed using machine learning in oncology: a systematic review.BMC medical research methodology · 2022Pooled it
- Artificial intelligence applied to musculoskeletal oncology: a systematic review.Skeletal radiology · 2022Pooled it
- Prognosis and risk factors for malignant peripheral nerve sheath tumor: a systematic review and meta-analysis.World journal of surgical oncology · 2020Pooled it
- Article
- Clinical benefits of immune checkpoint inhibitors for malignant peripheral nerve sheath tumors withFrontiers in immunology · 2025Article
- Phase II Study of Pexidartinib Plus Sirolimus in Unresectable Malignant Peripheral Nerve Sheath Tumors Identifies M2 Macrophage Activation.JCO oncology advances · 2025Article
- Multidimensional Immunotyping of Human NF1-Associated Peripheral Nerve Sheath Tumors Uncovers Tumor-Associated Macrophages as Key Drivers of Immune Evasion in the Tumor Microenvironment.Clinical cancer research : an official journal of the American Association for Cancer Research · 2024Article
- Current state of spinal nerve sheath tumor management and future advances.Neuro-oncology advances · 2024Review
- Machine Learning Predict Survivals of Spinal and Pelvic Ewing's Sarcoma with the SEER Database.Global spine journal · 2024Article
- Artificial Intelligence and Machine Learning in Neuroregeneration: A Systematic Review.Cureus · 2024Review
- Development and validation of a machine learning-based model to predict postoperative overall survival in patients with soft tissue sarcoma: a retrospective cohort study.American journal of cancer research · 2024Article
- The Clinical Characteristics, Risk Classification System, and Web-Based Nomogram for Primary Spinal Ewing Sarcoma: A Large Population-Based Cohort Study.Global spine journal · 2023Article
- Review
- Malignant Peripheral Nerve Sheath Tumors: Latest Concepts in Disease Pathogenesis and Clinical Management.Cancers · 2023Review
- Current Applications of Machine Learning in Spine: From Clinical View.Global spine journal · 2022Review
- Spinal cord compression from cutaneous malignant peripheral nerve sheath tumor metastasis in a patient with neurofibromatosis Type 1.Surgical neurology international · 2022Article
- Nomograms predicting local and distant recurrence and disease-specific mortality for R0/R1 soft tissue sarcomas of the extremities.Frontiers in oncology · 2022Article
- Systemic Options for Malignant Peripheral Nerve Sheath Tumors.Current treatment options in oncology · 2021Review
- The Clinical Characteristics and Prediction Nomograms for Primary Spine Malignancies.Frontiers in oncology · 2021Article
Corrections and comments
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Authors and funding
10 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundMalignant peripheral nerve sheath tumor (MPNST) is extremely rare in soft tissue sarcoma, with a high rate of recurrence and metastasis. Due to its rarity, the epidemiological features and prognostic factors are still uncertain. Moreover, nomograms for patients with MPNST have not been constructed and validated until now. PATIENTS AND
methodsPatients diagnosed with MPNST between 1973 and 2014 were selected from the Surveillance, Epidemiology, and End Results (SEER) database. Survival analysis, machine learning and Lasso regression were used to identify the prognostic factors for overall survival (OS) and cause-specific survival (CSS). Significant prognostic factors were integrated to construct nomograms and then the nomograms were validated externally with a separate cohort from our own institution.
resultsA total of 689 patients were included in the training set and 42 patients in the validation set. Multivariate analysis suggested that age, histology, historic stage and chemotherapy were independent prognostic factors for OS and primary site, surgery, historic stage and chemotherapy for CSS. The nomograms based on multivariate models were developed and validated for predicting 3- and 5-year OS and CSS, with a C-index of 0.686 and 0.707, respectively. In the external validation set, the C-index was 0.700 for OS and 0.722 for CSS.
conclusionICD-O-3 histology, historic stage and chemotherapy were independent prognostic factors for OS and primary site, surgery, historic stage and chemotherapy for CSS. The constructed nomograms could provide individual prediction for MPNST patients and assist oncologists in making accurate survival evaluation.
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