Small Bowel Adenocarcinoma

et al. 2020

Preprint

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.

Section: Discussionsupporting

confidence: 88%

Section: Introductionmentioning

confidence: 99%

A convenient clinical nomogram for predicting the cancer-specific survival of individual patients with small-intestine adenocarcinoma

et al. 2020

Preprint

“…Surgery remains the most commonly used treatment method for small-intestine adenocarcinoma [27,28], and it has been shown that palliative chemotherapy and radiation can also play a role in improving survival outcomes in these patients [4,29,30]. However, our research did not find this association, the main reason being that the current data on radiotherapy and chemotherapy in the SEER database have potential bias because many factors that influence the course of treatment are not captured in the registry data.…”

Section: Discussioncontrasting

confidence: 65%

“…Malignancies in the small intestine are relatively rare in clinical practice, accounting for less than 5% of all malignant tumors of the digestive tract [2,3]. Adenocarcinoma, neuroendocrine tumors, stromal tumors, and lymphoma are the four main histological types of small-intestine malignancies, of which small-intestine adenocarcinoma is the most-common type, accounting for 30-40% of these tumors [4,5]. The rarity of small-intestine adenocarcinoma restricts studies of its treatment and prognosis.…”

Section: Introductionmentioning

confidence: 99%

A convenient clinical nomogram for predicting the cancer-specific survival of individual patients with small-intestine adenocarcinoma

et al. 2020

Preprint

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 intothe 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 0.858. 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.

“…most-common type, accounting for 30% to 40% of these tumors [1,2]. The rarity of small-intestine adenocarcinoma restricts studies of its treatment and prognosis.…”

mentioning

confidence: 99%

A convenient clinical nomogram for predicting the cancer-specific survival of individual patients with small-intestine adenocarcinoma

et al. 2020

Preprint

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.