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Peng Liao
First Affiliated Hospital of Nanchang University
Corresponding Author
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Background: Current the number of examined LNs are controversial in predicting the survival of ESCC. We aimed to develop an alternative LN-classification-based nomogram to individualize ESCC prognosis.
Methods: Using the data of patients diagnosed with ESCC from SEER database between 2004 and 2015, we determined the cut-off values for the number of LNs examined via the K-adaptive partitioning (KAPS) algorithm. A nomogram predicting the survival of ESCC was performed, internally and externally validated, and evaluated by calibration plot, C-index, and decision curve analysis, and compared to the 7th TNM stage.
Results: Totally, we included 3629 patients with detailed information. The optimal cut-off for examined LN number was 8. The C-index for the nomogram was higher than the 7th TNM staging (internal: 0.708; 95%CI, 0.678-0.753 vs 0.601; 95%CI, 0.573-0.656, P<0.001; external: 0.687; 95%CI, 0.601-0.734 vs 0.605; 95%CI, 0.563-0.659, P<0.001). Additionally, the nomogram showed good agreement between internal and external validation. DCA analysis showed no matter in the internal cohort or external cohort, the nomogram showed a greater benefit across the period of follow-up compared to 7th TNM stage.
Conclusion: We found examining LNs that was more than 8 benefited for prognosis of patients. Based on these, a nomogram with greater benefit for predicting survival of EC patients than TNM staging was constructed.
Keywords
Esophageal squamous cell cancer, SEER, Lymph node metastasis, Survival, Validation study
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A new preprint design is coming!
We have improved readability, clarity, accessibility, and opportunities for engagement.
This is a preprint, 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.
Research
Peng Liao
First Affiliated Hospital of Nanchang University
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 24 Mar, 2020
No community comments so far
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Oncology
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: Current the number of examined LNs are controversial in predicting the survival of ESCC. We aimed to develop an alternative LN-classification-based nomogram to individualize ESCC prognosis.
Methods: Using the data of patients diagnosed with ESCC from SEER database between 2004 and 2015, we determined the cut-off values for the number of LNs examined via the K-adaptive partitioning (KAPS) algorithm. A nomogram predicting the survival of ESCC was performed, internally and externally validated, and evaluated by calibration plot, C-index, and decision curve analysis, and compared to the 7th TNM stage.
Results: Totally, we included 3629 patients with detailed information. The optimal cut-off for examined LN number was 8. The C-index for the nomogram was higher than the 7th TNM staging (internal: 0.708; 95%CI, 0.678-0.753 vs 0.601; 95%CI, 0.573-0.656, P<0.001; external: 0.687; 95%CI, 0.601-0.734 vs 0.605; 95%CI, 0.563-0.659, P<0.001). Additionally, the nomogram showed good agreement between internal and external validation. DCA analysis showed no matter in the internal cohort or external cohort, the nomogram showed a greater benefit across the period of follow-up compared to 7th TNM stage.
Conclusion: We found examining LNs that was more than 8 benefited for prognosis of patients. Based on these, a nomogram with greater benefit for predicting survival of EC patients than TNM staging was constructed.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
This is a list of supplementary files associated with this preprint. Click to download.
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