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

Development and validation of a nomogram for predicting cancer-specific survival in small-bowel adenocarcinoma patients using the SEER database

BACKGROUND

Small bowel adenocarcinoma (SBA) is a rare gastrointestinal malignancy forwhich survival is hampered by late diagnosis, complex responses to treatment, and poor prognosis. Accurate prognostic tools are crucial for optimizing treatment strategies and improving patient outcomes. This study aimed to develop and validate a nomogram based on the Surveillance, Epidemiology, and End Results (SEER) database to predict cancer-specific survival (CSS) in patients with SBA and compare it to traditional American Joint Committee on Cancer (AJCC) staging.

METHODS

We analyzed data from 2,064 patients diagnosed with SBA between 2010 and 2020 from the SEER database. Patients were randomly assigned to training and validation cohorts (7:3 ratio). Kaplan‒Meier survival analysis, Cox multivariate regression, and nomograms were constructed for analysis of 3-year and 5-year CSS. The performance of the nomograms was evaluated using Harrell's concordance index (C-index), the area under the receiver operating characteristic (ROC) curve, calibration curves, decision curve analysis (DCA), net reclassification improvement (NRI), and integrated discrimination improvement (IDI).

RESULTS

Multivariate Cox regression identified sex, age at diagnosis, marital status, tumor site, pathological grade, T stage, N stage, M stage, surgery, retrieval of regional lymph nodes (RORLN), and chemotherapy as independent covariates associated with CSS. In both the training and validation cohorts, the developed nomograms demonstrated superior performance to that of the AJCC staging system, with C-indices of 0.764 and 0.759, respectively. The area under the curve (AUC) values obtained by ROC analysis for 3-year and 5-year CSS prediction significantly surpassed those of the AJCC model. The nomograms were validated using calibration and decision curves, confirming their clinical utility and superior predictive accuracy. The NRI and IDI indicated the enhanced predictive capability of the nomogram model.

CONCLUSION

The SEER-based nomogram offers a significantly superior ability to predict CSS in SBA patients, supporting its potential application in clinical decision-making and personalized approaches to managing SBA to improve survival outcomes.

Prediction of distant metastasis and specific survival prediction of small intestine cancer patients with metastasis: A population-based study

BACKGROUND

Small intestine cancer (SIC) is difficult to diagnose early and presents a poor prognosis due to distant metastasis. This study aimed to develop nomograms for diagnosing and assessing the prognosis of SIC with distant metastasis.

METHODS

Patients diagnosed with SIC between 2010 and 2015 were included from the Surveillance, Epidemiology and End Results database. Univariate and multifactor analysis determined independent risk factors for distant metastasis and prognostic factors for overall and cancer-specific survival. We then constructed the corresponding three nomograms and assessed the diagnostic accuracy of the nomograms by net reclassification improvement, receiver operating characteristic curves and calibration curves, assessed the clinical utility by decision curve analysis.

RESULTS

The cohort consisted of 6697 patients, of whom 1299 had distant metastasis at diagnosis. Tstage, Nstage, age, tumor size, grade, and histological type were independent risk factors for distant metastasis. Age, histological type, T stage, N stage, grade, tumor size, whether receiving surgery, number of lymph nodes removed, and the presence of bone or lung metastases were predictors of both overall survival and cancer-specific survival. The nomograms showed excellent accuracy in predicting distant metastasis and prognosis.

CONCLUSION

Nomograms were developed and validated for SIC patients with distant metastasis, aiding physicians in making rational and personalized clinical decisions.

Clinicopathological characteristics and prognostic factors of elderly small intestine adenocarcinoma using propensity score matching analysis: a study based on SEER database

BACKGROUND

Small intestine adenocarcinoma (SIA) is a scant disease that has no adequate clinical trials, so its prognostic factors are still unclear, especially in elderly patients. In this article, we aimed to explore the clinicopathology presentation, treatments, outcomes, and predictors of small intestine adenocarcinoma patients aged 65 years or older.

METHODS

We retrieved clinicopathology data of small intestine adenocarcinoma patients diagnosed between 2004 and 2015 from the Surveillance Epidemiology and End Results (SEER) database. We clarified patients into two groups: the surgery and the non-surgery group and conducted propensity score matching (PSM) to compare survival outcoming. We identified the prognostic indicators for cancer-specific survival (CSS) and overall survival (OS) by the Cox proportional hazards model.

RESULTS

In total, 1018 eligible cases were enrolled, with a median survival of 16 months; the 3-year OS and CSS rates were 36% and 41.7%, and the 5-year OS and CSS rates were 26.5% and 33.3%. Multivariate analyses revealed that age, grade, tumor stage, surgery, and chemotherapy were independent prognostic factors for OS, while grade, tumor stage, surgery, radiation, and chemotherapy were independent factors for CSS. After PSM, only surgery and tumor stage (AJCC 6th) were independent prognostic factors for OS and CSS.

CONCLUSION

Surgery could bring benefit to survival for elderly SIA patients, and the early stage of the disease was another significant prognostic factor.

Development and validation of lymph node ratio-based nomograms for primary duodenal adenocarcinoma after surgery

Background

The prediction models for primary duodenal adenocarcinoma (PDA) are deficient. This study aimed to determine the predictive value of the lymph node ratio (LNR) in PDA patients and to establish and validate nomograms for predicting overall survival (OS) and cancer-specific survival (CSS) for PDAs after surgical resection.

Methods

We extracted the demographics and clinicopathological information of PDA patients between 2004 and 2018 from the Surveillance, Epidemiology and End Results database. After screening cases, we randomly divided the enrolled patients into training and validation groups. X-tile software was used to obtain the best cut-off value for the LNR. Univariate and multivariate Cox analyses were used in the training group to screen out significant variables to develop nomograms. The predictive accuracy of the nomograms was evaluated by the concordance index (C-index), calibration curves, area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA). Finally, four risk groups were created based on quartiles of the model scores.

Results

A total of 978 patients were included in this study. The best cut-off value for the LNR was 0.47. LNR was a negative predictive factor for both OS and CSS. Age, sex, grade, chemotherapy and LNR were used to construct the OS nomogram, while age, grade, chemotherapy, the number of lymph nodes removed and LNR were incorporated into the CSS nomogram. The C-index, calibration curves and AUC of the training and validation sets revealed their good predictability. DCA showed that the predictive value of the nomograms was superior to that of the American Joint Committee on Cancer (AJCC) TNM staging system (8th edition). In addition, risk stratification demonstrated that patients with higher risk correlated with poor survival.

Conclusions

The LNR was an adverse prognostic determinant for PDAs. The nomograms provided an accurate and applicable tool to evaluate the prognosis of PDA patients after surgery.

Development of a Deep Learning Model for Malignant Small Bowel Tumors Survival: A SEER-Based Study

Background This study aims to explore a deep learning (DL) algorithm for developing a prognostic model and perform survival analyses in SBT patients. Methods The demographic and clinical features of patients with SBTs were extracted from the Surveillance, Epidemiology and End Results (SEER) database. We randomly split the samples into the training set and the validation set at 7:3. Cox proportional hazards (Cox-PH) analysis and the DeepSurv algorithm were used to develop models. The performance of the Cox-PH and DeepSurv models was evaluated using receiver operating characteristic curves, calibration curves, C-statistics and decision-curve analysis (DCA). A Kaplan−Meier (K−M) survival analysis was performed for further explanation on prognostic effect of the Cox-PH model. Results The multivariate analysis demonstrated that seven variables were associated with cancer-specific survival (CSS) (all p < 0.05). The DeepSurv model showed better performance than the Cox-PH model (C-index: 0.871 vs. 0.866). The calibration curves and DCA revealed that the two models had good discrimination and calibration. Moreover, patients with ileac malignancy and N2 stage disease were not responding to surgery according to the K−M analysis. Conclusions This study reported a DeepSurv model that performed well in CSS in SBT patients. It might offer insights into future research to explore more DL algorithms in cohort studies.

Development and validation of prognostic nomograms for patients with metastatic small bowel adenocarcinoma: a retrospective cohort study

We aimed to explore factors associated with prognosis in patients with metastatic small bowel adenocarcinoma (SBA) as well as to develop and validate nomograms to predict overall survival (OS) and cancer-specific survival (CSS). Relevant information of patients diagnosed between 2004 and 2016 was extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Nomograms for predicting 1- and 3-year OS and CSS were established with potential risk factors screened from multivariate cox regression analysis. The discrimination and accuracy of the nomograms were assessed by concordance index (C-index), calibration plots, and the area under receiver operating characteristic curve (AUC). In total, 373 SBA patients with M1 category were enrolled. Multivariate analysis revealed that age, size and grade of primary tumor, primary tumor surgery, and chemotherapy were significant variables associated with OS and CSS. The C-index values of the nomogram for OS were 0.715 and 0.687 in the training and validation cohorts, respectively. For CSS, it was 0.711 and 0.690, respectively. Through AUC, decision curve analysis (DCA) and calibration plots, the nomograms displayed satisfactory prognostic predicted ability and clinical application both in the OS and CSS. Our models could be served as a reliable tool for prognostic evaluation of patients with metastatic SBA, which are favorable in facilitating individualized survival predictions and clinical decision-making.

Development and validation of a nomogram for primary duodenal carcinoma: a multicenter, population-based study

Aim: This study aimed to develop a predictive model for patients with duodenal carcinoma. Methods: Duodenal carcinoma patients from the Surveillance, Epidemiology, and End Results database (2010-2015) and the First Affiliated Hospital of Nanchang University (2010-2021) were enrolled. A nomogram was constructed according to least absolute shrinkage and selection operator regression analysis, the Akaike information criterion approach and Cox regression analysis. Results: Five independent prognostic factors were significantly associated with the prognosis of the duodenal carcinoma patients. A nomogram was constructed with a C-index in the training and validation cohorts of 0.671 (95% CI: 0.578-0.716) and 0.662 (95% CI: 0.529-0.773), respectively. Conclusion: The established nomogram model provided visualization of the risk of each prognostic factor.

A Novel Clinical Nomogram for Predicting Cancer-Specific Survival in Adult Patients After Primary Surgery for Epithelial Ovarian Cancer: A Real-World Analysis Based on the Surveillance, Epidemiology, and End Results Database and External Validation in a Tertiary Center

Background

The present study aimed to construct and validate a nomogram that can be used to predict cancer-specific survival (CSS) in patients with epithelial ovarian cancer (EOC).

Methods

A total of 7,129 adult patients with EOC were extracted from the Surveillance, Epidemiology, and End Results database between 2010 and 2015. Patients were randomly divided into the training and validation cohorts (7:3). Cox regression was conducted to evaluate prognostic factors of CSS. The internal validation of the nomogram was performed using concordance index (C-index), AUC, calibration curves, and decision curve analyses (DCAs). Data from 53 adult EOC patients at Shengjing Hospital of China Medical University from 2008 to 2012 were collected for external verification. Kaplan-Meier curves were plotted to compare survival outcomes among risk subgroups.

Results

Age, grade, histological types, stage, residual lesion size, number of regional lymph nodes resected, number of positive lymph nodes, and chemotherapy were independent risk factors for CSS. Based on the above factors, we constructed a nomogram. The C-indices of the training cohort, internal validation cohort, and external verification group were 0.763, 0.750, and 0.920, respectively. The calibration curve indicated good agreement between the nomogram prediction and actual survival. AUC and DCA results indicated great clinical usefulness of the nomogram. The differences in the Kaplan-Meier curves among different risk subgroups were statistically significant.

Conclusions

We constructed a nomogram to predict CSS in adult patients with EOC after primary surgery, which can assist in counseling and guiding treatment decision making.