See the published version of this preprint at BMC Cancer.
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Research article
zhendong li
Department of Hepatobiliary Surgery, The First Affiliated Hospital of Jinan University
Corresponding Author
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: The objective of this study was to develop a practical nomogram for predicting the cancer-specific survival (CSS) of patients with small-intestine adenocarcinoma.
Methods: Patients diagnosed with small-intestine adenocarcinoma between 2010 and 2015 were selected for inclusion in this study from the Surveillance, Epidemiology, and End Results (SEER) database. The selected patients were randomly divided into the training and validation cohorts at a ratio of 7:3. The predictors of CSS were identified by applying both forward and backward stepwise selection methods in a Cox regression model. The performance of the nomogram was measured by the concordance index (C-index), the area under receiver operating characteristic curve (AUC), calibration plots, the net reclassification improvement (NRI), the integrated discrimination improvement (IDI), and decision-curve analysis (DCA).
Results: Multivariate Cox regression indicated that factors including age at diagnosis, sex, marital status, insurance status, histology grade, SEER stage, surgery status, T stage, and N stage were independent covariates associated with CSS. These factors were used to construct a predictive model, which was built and virtualized by a nomogram. The C-index of the constructed nomogram was 0.850. The AUC values indicated that the established nomogram displayed better discrimination performance than did the seventh edition of the American Joint Committee on Cancer TNM staging system in predicting CSS. The IDI and NRI also showed that the nomogram exhibited superior performance in both the training and validation cohorts. Furthermore, the calibrated nomogram predicted survival rates that closely corresponded to actual survival rates, while the DCA demonstrated the considerable clinical usefulness of the nomogram.
Conclusion: We have constructed a nomogram for predicting the CSS of small-intestine adenocarcinoma patients. This prognostic model may improve the ability of clinicians to predict survival in individual patients and provide them with treatment recommendations.
Keywords
small-intestine adenocarcinoma, nomogram, cancer-specific survival.
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View the published article at BMC Cancer.
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Posted 12 May, 2020
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Editor assigned
On 27 Apr, 2020
Editor invited
On 26 Apr, 2020
Submission checks complete
On 13 Jan, 2020
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Review #2 received
Received 21 Apr, 2020
Editorial decision: Major revision
On 21 Apr, 2020
Reviewer #2 agreed
On 18 Apr, 2020
Review #1 received
Received 10 Apr, 2020
Reviewers invited
Invitations sent on 06 Apr, 2020
Reviewer #1 agreed
On 06 Apr, 2020
Editor assigned
On 05 Apr, 2020
Submission checks complete
On 04 Apr, 2020
Editor invited
On 04 Apr, 2020
No comments provided
Review #2 received
Received 22 Feb, 2020
Editorial decision: Major revision
On 22 Feb, 2020
Review #3 received
Received 16 Feb, 2020
Review #1 received
Received 16 Feb, 2020
Reviewer #4 agreed
On 03 Feb, 2020
Reviewer #3 agreed
On 02 Feb, 2020
Reviewer #2 agreed
On 31 Jan, 2020
Reviewer #1 agreed
On 25 Jan, 2020
Editor assigned
On 22 Jan, 2020
Reviewers invited
Invitations sent on 22 Jan, 2020
Submission checks complete
On 14 Jan, 2020
Editor invited
On 14 Jan, 2020
First submitted
On 06 Jan, 2020
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See the published version of this preprint at BMC Cancer.
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.
Learn more about In Review
Research article
zhendong li
Department of Hepatobiliary Surgery, The First Affiliated Hospital of Jinan University
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: Published
View the published article at BMC Cancer.
Version 3
Posted 12 May, 2020
No community comments so far
Editor assigned
On 27 Apr, 2020
Editor invited
On 26 Apr, 2020
Submission checks complete
On 13 Jan, 2020
No comments provided
Review #2 received
Received 21 Apr, 2020
Editorial decision: Major revision
On 21 Apr, 2020
Reviewer #2 agreed
On 18 Apr, 2020
Review #1 received
Received 10 Apr, 2020
Reviewers invited
Invitations sent on 06 Apr, 2020
Reviewer #1 agreed
On 06 Apr, 2020
Editor assigned
On 05 Apr, 2020
Submission checks complete
On 04 Apr, 2020
Editor invited
On 04 Apr, 2020
No comments provided
Review #2 received
Received 22 Feb, 2020
Editorial decision: Major revision
On 22 Feb, 2020
Review #3 received
Received 16 Feb, 2020
Review #1 received
Received 16 Feb, 2020
Reviewer #4 agreed
On 03 Feb, 2020
Reviewer #3 agreed
On 02 Feb, 2020
Reviewer #2 agreed
On 31 Jan, 2020
Reviewer #1 agreed
On 25 Jan, 2020
Editor assigned
On 22 Jan, 2020
Reviewers invited
Invitations sent on 22 Jan, 2020
Submission checks complete
On 14 Jan, 2020
Editor invited
On 14 Jan, 2020
First submitted
On 06 Jan, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at BMC Cancer.
Background: The objective of this study was to develop a practical nomogram for predicting the cancer-specific survival (CSS) of patients with small-intestine adenocarcinoma.
Methods: Patients diagnosed with small-intestine adenocarcinoma between 2010 and 2015 were selected for inclusion in this study from the Surveillance, Epidemiology, and End Results (SEER) database. The selected patients were randomly divided into the training and validation cohorts at a ratio of 7:3. The predictors of CSS were identified by applying both forward and backward stepwise selection methods in a Cox regression model. The performance of the nomogram was measured by the concordance index (C-index), the area under receiver operating characteristic curve (AUC), calibration plots, the net reclassification improvement (NRI), the integrated discrimination improvement (IDI), and decision-curve analysis (DCA).
Results: Multivariate Cox regression indicated that factors including age at diagnosis, sex, marital status, insurance status, histology grade, SEER stage, surgery status, T stage, and N stage were independent covariates associated with CSS. These factors were used to construct a predictive model, which was built and virtualized by a nomogram. The C-index of the constructed nomogram was 0.850. The AUC values indicated that the established nomogram displayed better discrimination performance than did the seventh edition of the American Joint Committee on Cancer TNM staging system in predicting CSS. The IDI and NRI also showed that the nomogram exhibited superior performance in both the training and validation cohorts. Furthermore, the calibrated nomogram predicted survival rates that closely corresponded to actual survival rates, while the DCA demonstrated the considerable clinical usefulness of the nomogram.
Conclusion: We have constructed a nomogram for predicting the CSS of small-intestine adenocarcinoma patients. This prognostic model may improve the ability of clinicians to predict survival in individual patients and provide them with treatment recommendations.
Figure 1
Figure 2
Figure 3
Figure 4
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