This preprint is under consideration at World Journal of Surgical Oncology. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
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Research Article
wei niu
Guangdong Provincial Hospital of Traditional Chinese Medicine
Corresponding Author
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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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CURRENT STATUS: Under Review
Version 1
Posted 10 Mar, 2021
No community comments so far
Review #1 received
Received 08 Apr, 2021
Reviewer #2 agreed
On 25 Mar, 2021
Reviewer #1 agreed
On 15 Mar, 2021
Review #? received
Received 15 Mar, 2021
Reviewers invited
Invitations sent on 04 Mar, 2021
Editor assigned
On 03 Mar, 2021
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On 03 Mar, 2021
Editor invited
On 03 Mar, 2021
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On 02 Mar, 2021
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This preprint is under consideration at World Journal of Surgical Oncology. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In 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
Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 10 Mar, 2021
No community comments so far
Review #1 received
Received 08 Apr, 2021
Reviewer #2 agreed
On 25 Mar, 2021
Reviewer #1 agreed
On 15 Mar, 2021
Review #? received
Received 15 Mar, 2021
Reviewers invited
Invitations sent on 04 Mar, 2021
Editor assigned
On 03 Mar, 2021
Submission checks complete
On 03 Mar, 2021
Editor invited
On 03 Mar, 2021
First submitted
On 02 Mar, 2021
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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