Dataset Information

Prognostic factors and survival prediction in hepatocellular carcinoma: development and validation of a novel nomogram based on the SEER database.

ABSTRACT:

Background

Current staging systems for hepatocellular carcinoma (HCC) still have limitations in clinical practice. Our study aimed to explore the prognostic factors and develop a new nomogram to predict the cancer-specific survival (CSS) for patients with HCC.

Methods

A total of 6,166 HCC patients were identified from the Surveillance, Epidemiology, and End Results (SEER) database. Patients were randomly grouped into the training cohort (70%) and validation cohort (30%). Multivariate Cox analysis was used to identify prognostics factors for CSS of patients, then we incorporated these variables and presented a new nomogram to predict 2- and 5-year CSS. The performance of the nomogram was assessed with respect to its calibration, concordance index (C-index), area under the receiver operating characteristic (ROC) curve (AUC), and decision curve analysis (DCA).

Results

Multivariate Cox analysis revealed that American Joint Committee on Cancer (AJCC) stage, race, grade, surgery, chemotherapy, radiation, tumor size, bone metastasis (BM), and alpha-fetoprotein (AFP) were independently associated with CSS. The prediction nomogram which contained these predictors showed good performance, with a C-index of 0.802 [95% confidence interval (CI), 0.792-0.812] in the training cohort and 0.801 (95% CI, 0.787-0.815) in the validation cohort. The calibration curves demonstrated good agreement between the actual observation and the nomogram prediction. Furthermore, the nomogram showed improved discriminative capacity (AUC, 0.873 and 0.875 for 2- and 5-year CSS in validation set) compared to the 7^th tumor-node-metastasis (TNM) staging system (AUC, 0.735 and 0.717). The DCA also indicated good application of the nomogram.

Conclusions

This study presents a novel nomogram that incorporates the important prognostic factors of HCC, which can be conveniently used to accurately predict the 2- and 5-year CSS of patients with HCC, thus assisting individualized clinical decision making.

SUBMITTER: Huang S

PROVIDER: S-EPMC10502553 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Prognostic factors and survival prediction in hepatocellular carcinoma: development and validation of a novel nomogram based on the SEER database.

Huang Shanshan S Zhu Zheng Z Ruan Yejiao Y Zhang Fayuan F Xu Yueting Y Jin Lingxiang L Lopez-Lopez Victor V Merle Philippe P Lu Guangrong G Li Liyi L

Journal of gastrointestinal oncology 20230721 4

<h4>Background</h4>Current staging systems for hepatocellular carcinoma (HCC) still have limitations in clinical practice. Our study aimed to explore the prognostic factors and develop a new nomogram to predict the cancer-specific survival (CSS) for patients with HCC.<h4>Methods</h4>A total of 6,166 HCC patients were identified from the Surveillance, Epidemiology, and End Results (SEER) database. Patients were randomly grouped into the training cohort (70%) and validation cohort (30%). Multivari ...[more]

PMID: 37720431

Similar Datasets

Project description:Background The goal is to establish and validate an innovative prognostic risk stratification and nomogram in patients of hepatocellular carcinoma (HCC) with microvascular invasion (MVI) for predicting the cancer-specific survival (CSS). Methods 1487 qualified patients were selected from the Surveillance, Epidemiology and End Results (SEER) database and randomly assigned to the training cohort and validation cohort in a ratio of 7:3. Concordance index (C-index), area under curve (AUC) and calibration plots were adopted to evaluate the discrimination and calibration of the nomogram. Decision curve analysis (DCA) was used to quantify the net benefit of the nomogram at different threshold probabilities and compare it to the American Joint Committee on Cancer (AJCC) tumor staging system. C-index, net reclassification index (NRI) and integrated discrimination improvement (IDI) were applied to evaluate the improvement of the new model over the AJCC tumor staging system. The new risk stratifications based on the nomogram and the AJCC tumor staging system were compared. Results Eight prognostic factors were used to construct the nomogram for HCC patients with MVI. The C-index for the training and validation cohorts was 0.785 and 0.776 respectively. The AUC values were higher than 0.7 both in the training cohort and validation cohort. The calibration plots showed good consistency between the actual observation and the nomogram prediction. The IDI values of 1-, 3-, 5-year CSS in the training cohort were 0.17, 0.16, 0.15, and in the validation cohort were 0.17, 0.17, 0.17 (P<0.05). The NRI values of the training cohort were 0.75 at 1-year, 0.68 at 3-year and 0.67 at 5-year. The DCA curves indicated that the new model more accurately predicted 1-year, 3-year, and 5-year CSS in both training and validation cohort, because it added more net benefit than the AJCC staging system. Furthermore, the risk stratification system showed the CSS in different groups had a good regional division. Conclusions A comprehensive risk stratification system and nomogram were established to forecast CSS for patients of HCC with MVI.

Project description:BackgroundLaryngeal sarcoma is an extremely rare malignant tumor of larynx and usually reported as case reports or small series. At present, there is no research based on big data about the prognostic factors affecting laryngeal sarcoma. Our study aimed to investigate the prognostic survival factors of laryngeal sarcoma and develop a comprehensive nomogram for predicting the survival of laryngeal sarcoma.MethodsData were obtained from the Surveillance, Epidemiology, and End Results (SEER) database to find patients diagnosed with laryngeal sarcoma from 1998 to 2016. The data were obtained using SEER Stat 8.3.5 software, collated, and analyzed by Excel 2016 software and SPSS (v25.0). Kaplan-Meier curves were used for survival analysis. The variables obtained by univariate analysis were introduced into the Cox proportional hazard model for multivariate analysis. The risk factors affecting the prognosis of laryngeal sarcoma were obtained (P<0.05 indicated statistical significance). The independent prognostic factors of laryngeal sarcoma were integrated and used to construct a nomogram.ResultsA total of 381 patients with laryngeal sarcoma were included. The median age of diagnosis was 67 years. The proportion of patients who had received surgical treatment was 62.73%, while 22.31% of patients had received no surgery. The 1-, 3-, 5-, and 10-year survival rates were 87%, 76%, 61%, and 45%, respectively. The median survival time was 102.35 months. Univariate analysis showed that increased age, primary site, pathology, pathological grade, and surgical treatment were significantly correlated with patient survival time and were risk factors for the patients' prognosis. Race, gender, and even lymph node metastasis were not significantly correlated with patient prognosis. The risk factors obtained from the univariate analysis were incorporated into the Cox risk model for multivariate analysis, the independent risk factors for prognosis of patients were: age (HR 1.569, 95% CI: 1.358-1.813, P<0.005), pathology (HR 0.834, 95% CI: 0.734-0.948, P<0.005), pathological grade (HR 1.433, 95% CI: 1.164-1.764, P<0.001), surgical treatment (HR 0.778, 95% CI: 0.696-0.870, P<0.000), primary site was excluded (P=0.092). We included all the risk factors from the multi-factor analysis to construct a nomogram, and the C-index value was 0.73, indicating that it was well-calibrated in the medium and long term.ConclusionsLaryngeal sarcoma is a rare malignant tumor of the larynx, which most often affects people between the ages of 50 and 79 and males. Our study shows that age, pathology, pathological grade, surgical treatment may be the risk factors for patients' prognosis. Based on this, we constructed a nomogram model with a prediction accuracy of 73% that could help clinicians make decisions on an individual basis.

Project description:BackgroundGastroenteropancreatic neuroendocrine carcinoma (GEP-NEC) is a rare group of diseases with poor prognosis and the assessment of its prognosis is a significant challenge. This study aimed to develop and validate a prognostic nomogram to assess overall survival (OS) in patients with GEP-NEC.MethodsPatients diagnosed with poorly differentiated GEP-NEC were collected from the Surveillance, Epidemiology, and End Results (SEER) database between 2011 and 2015 and were randomly assigned to the training or validation cohort in a 7:3 ratio. The data included details of clinicopathological characteristics, therapeutic interventions and survival outcomes. Univariate and multivariate Cox regression analyses were used to identify independent prognostic factors. Nomogram was used to predict OS at 1 and 2 years. The nomogram was internally validated with validation cohort, and its predictive ability was evaluated using concordance index (C-index), receiver operating characteristic (ROC) curves, calibration plots, decision curve analysis (DCA), and integrated discrimination improvement (IDI) index.ResultsA total of 887 patients were divided into the training group (n=623) and the validation group (n=264). A total of 476 patients (53.66%) were in stage IV. Based on multivariate analysis, a nomogram was constructed with age, gender, N stage, tumor size, primary tumor resection, radiotherapy and chemotherapy (P<0.05). The C-index was 0.701 [95% confidential interval (CI): 0.677-0.725] and 0.731 (95% CI: 0.698-0.764) for the training and validation groups, respectively. The C-index, ROC, IDI and DCA results indicated that this nomogram model has a good predictive value.ConclusionsIn this study, a nomogram model based on seven independent prognostic factors provided visualization of the risk and could help clinicians predict the 1-year and 2-year OS for GEP-NEC. This tool can provide personalized survival predictions and improve clinical decision making for the management of GEP-NEC.

Project description:BackgroundRenal cell carcinoma (RCC) is a lethal urological malignancy. Precise risk-stratification is very important for decision-making in postoperative patient management. This study aimed to establish and validate a prognostic nomogram of overall survival (OS) in patients with RCC based on Surveillance, Epidemiology, and End Results (SEER) and TCGA database.MethodsThe retrospective data of 40,154 patients diagnosed with RCC during 2010 to 2015 from SEER database (development cohort) and 1,188 patients from TCGA database (validation cohort) were downloaded for analysis. Independent prognostic factors were identified by univariate and multivariate Cox regression analyses and adopted to set up a predictive nomogram of OS. The discrimination and calibration of the nomogram were evaluated by ROC curves, C-index values, and calibration plots, and survival analyses were conducted using Kaplan-Meier curves and long-rank tests.ResultsThe results of multivariate Cox regression analysis demonstrated that age, sex, tumor grade, the American Joint Committee on Cancer (AJCC) stage, tumor size, and pathological types were independent predictors of the OS of RCC patients. These variables were integrated to construct the nomogram, and verification was conducted subsequently. The area under the ROC curve values of 3- and 5-year survival were 0.785 and 0.769 in the development cohort and 0.786 and 0.763 in the validation cohort. The C-index was 0.746 (95% CI: 0.740-0.752) in the development cohort and 0.763 (95% CI: 0.738-0.788) in the validation cohort, indicating good performance of the nomogram. Calibration curve analysis also suggested supreme accuracy on prediction. Finally, patients in the development and validation cohorts were stratified into three risk-level groups (high, intermediate, and low) based on the risk scores calculated by the nomogram, and significant differences in OS were observed among these three groups.ConclusionsIn this study, a prognostic nomogram was established to provide tool for clinicians to better advise RCC patients, determine the follow-up strategies and to select suitable patients for clinical trials.

Project description:Objectives Clear cell renal cell carcinoma (ccRCC) is highly prevalent, prone to metastasis, and has a poor prognosis after metastasis. Therefore, this study aimed to develop a prognostic model to predict the individualized prognosis of patients with metastatic clear cell renal cell carcinoma (mccRCC). Patients and Methods Data of 1790 patients with mccRCC, registered from 2010 to 2015, were extracted from the Surveillance, Epidemiology and End Results (SEER) database. The included patients were randomly divided into a training set (n = 1253) and a validation set (n = 537) based on the ratio of 7:3. The univariate and multivariate Cox regression analyses were used to identify the important independent prognostic factors. A nomogram was then constructed to predict cancer specific survival (CSS). The performance of the nomogram was internally validated by using the concordance index (C-index), calibration plots, receiver operating characteristic curves, net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA). We compared the nomogram with the TNM staging system. Kaplan–Meier survival analysis was applied to validate the application of the risk stratification system. Results Diagnostic age, T-stage, N-stage, bone metastases, brain metastases, liver metastases, lung metastases, chemotherapy, radiotherapy, surgery, and histological grade were identified as independent predictors of CSS. The C-index of training and validation sets are 0.707 and 0.650 respectively. In the training set, the AUC of CSS predicted by nomogram in patients with mccRCC at 1-, 3- and 5-years were 0.770, 0.758, and 0.757, respectively. And that in the validation set were 0.717, 0.700, and 0.700 respectively. Calibration plots also showed great prediction accuracy. Compared with the TNM staging system, NRI and IDI results showed that the predictive ability of the nomogram was greatly improved, and DCA showed that patients obtained clinical benefits. The risk stratification system can significantly distinguish the patients with different survival risks. Conclusion In this study, we developed and validated a nomogram to predict the CSS rate in patients with mccRCC. It showed consistent reliability and clinical applicability. Nomogram may assist clinicians in evaluating the risk factors of patients and formulating an optimal individualized treatment strategy.

Dataset Information

Prognostic factors and survival prediction in hepatocellular carcinoma: development and validation of a novel nomogram based on the SEER database.

Background

Methods

Results

Conclusions

Publications

Prognostic factors and survival prediction in hepatocellular carcinoma: development and validation of a novel nomogram based on the SEER database.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets