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Research Article
wei niu
Guangdong Provincial Hospital of Traditional Chinese Medicine
Corresponding Author
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: Osteosarcoma is one of the most common malignant tumors of the bone with poor prognosis. The present study was aimed to establish and validate nomogram to predict the risk of metastasis for individual osteosarcoma patients.
Methods: 2195 osteosarcoma patients were retrospectively collected from Surveillance, Epidemiology, and End Results (SEER) database. The least absolute shrinkage and selection operator regression model was applied to promote feature selection for the metastasis signature model. Univariate and multivariate logistic analysis were used to discern independent predictive factors. These predictive factors were included in the nomogram to evaluate metastasis probability in osteosarcoma patients. Nomogram was validated internally and externally.
Results: We randomly assigned 2195 osteosarcoma patients into the training (n = 1464) and validation (n = 731) subgroups. Age, sex, tumor grade, tumor size and use of surgery were identified as the predictive factors for the risk of metastasis (all p<0.05) and further incorporated to develop the nomogram. Predictive accuracy (AUC) for metastasis prediction were 0.7427067 in the training subgroup and 0.6798374 in the external validation subgroup, respectively. And the decision curve analysis also proved the value of the model.
Conclusions: We have constructed new high-performance nomogram to assist clinicians in identifying osteosarcoma patients with high risk of metastasis.
Keywords
Osteosarcoma, Metastasis, Nomogram, SEER Program
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This is a preprint. It has not completed peer review.
Research Article
wei niu
Guangdong Provincial Hospital of Traditional Chinese Medicine
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
History
CURRENT STATUS: Posted
Version 1
Posted 10 Mar, 2021
No community comments so far
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: Osteosarcoma is one of the most common malignant tumors of the bone with poor prognosis. The present study was aimed to establish and validate nomogram to predict the risk of metastasis for individual osteosarcoma patients.
Methods: 2195 osteosarcoma patients were retrospectively collected from Surveillance, Epidemiology, and End Results (SEER) database. The least absolute shrinkage and selection operator regression model was applied to promote feature selection for the metastasis signature model. Univariate and multivariate logistic analysis were used to discern independent predictive factors. These predictive factors were included in the nomogram to evaluate metastasis probability in osteosarcoma patients. Nomogram was validated internally and externally.
Results: We randomly assigned 2195 osteosarcoma patients into the training (n = 1464) and validation (n = 731) subgroups. Age, sex, tumor grade, tumor size and use of surgery were identified as the predictive factors for the risk of metastasis (all p<0.05) and further incorporated to develop the nomogram. Predictive accuracy (AUC) for metastasis prediction were 0.7427067 in the training subgroup and 0.6798374 in the external validation subgroup, respectively. And the decision curve analysis also proved the value of the model.
Conclusions: We have constructed new high-performance nomogram to assist clinicians in identifying osteosarcoma patients with high risk of metastasis.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
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