A convenient clinical nomogram for predicting the cancer-specific survival of individual patients with small-intestine adenocarcinoma
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.
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Posted 12 May, 2020
On 27 Apr, 2020
On 26 Apr, 2020
On 13 Jan, 2020
Received 21 Apr, 2020
On 21 Apr, 2020
On 18 Apr, 2020
Received 10 Apr, 2020
Invitations sent on 06 Apr, 2020
On 06 Apr, 2020
On 05 Apr, 2020
On 04 Apr, 2020
On 04 Apr, 2020
Received 22 Feb, 2020
On 22 Feb, 2020
Received 16 Feb, 2020
Received 16 Feb, 2020
On 03 Feb, 2020
On 02 Feb, 2020
On 31 Jan, 2020
On 25 Jan, 2020
On 22 Jan, 2020
Invitations sent on 22 Jan, 2020
On 14 Jan, 2020
On 14 Jan, 2020
On 06 Jan, 2020
A convenient clinical nomogram for predicting the cancer-specific survival of individual patients with small-intestine adenocarcinoma
Posted 12 May, 2020
On 27 Apr, 2020
On 26 Apr, 2020
On 13 Jan, 2020
Received 21 Apr, 2020
On 21 Apr, 2020
On 18 Apr, 2020
Received 10 Apr, 2020
Invitations sent on 06 Apr, 2020
On 06 Apr, 2020
On 05 Apr, 2020
On 04 Apr, 2020
On 04 Apr, 2020
Received 22 Feb, 2020
On 22 Feb, 2020
Received 16 Feb, 2020
Received 16 Feb, 2020
On 03 Feb, 2020
On 02 Feb, 2020
On 31 Jan, 2020
On 25 Jan, 2020
On 22 Jan, 2020
Invitations sent on 22 Jan, 2020
On 14 Jan, 2020
On 14 Jan, 2020
On 06 Jan, 2020
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