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Li Z, Hong Q, Guo Z, Liu X, Tan C, Feng Z, Li K. Construction and validation of a nomogram for predicting cancer-specific survival in middle-aged patients with advanced hepatocellular carcinoma: A SEER-based study. Medicine (Baltimore) 2024; 103:e39480. [PMID: 39312373 PMCID: PMC11419510 DOI: 10.1097/md.0000000000039480] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Accepted: 08/07/2024] [Indexed: 09/25/2024] Open
Abstract
Hepatocellular carcinoma is the predominant form of primary liver cancer and is the leading cause of cancer-related death. The aim of this study was to construct a nomogram to predict cancer-specific survival (CSS) in middle-aged patients with advanced hepatocellular carcinoma. Clinical data were downloaded from the Surveillance, Epidemiology and End Results (SEER) database for middle-aged patients diagnosed with advanced hepatocellular carcinoma (AJCC stage III and IV) from 2000 to 2019. The patients were randomized in a 7:3 ratio into training cohort and validation cohort. Univariate and multivariate Cox regression analyses were performed in the training cohort to screen for independent risk factors associated with cancer-specific survival for the construction of nomogram. The nomogram was examined and evaluated using the consistency index (C-index), area under the curve (AUC), and calibration plots. The clinical application value of the model was evaluated using decision curve analysis (DCA). A total of 3026 patients were selected, including 2244 in the training cohort and 962 in the validation cohort. Multivariate analysis revealed gender, marital status, American Joint Committee on Cancer (AJCC) stage, tumor size, bone metastasis, lung metastasis, alpha-fetoprotein (AFP) level, surgery, radiotherapy, chemotherapy as independent risk factors, which were all included in the construction of the nomogram. In the training cohort, the AUC values were 0.74 (95% CI: 0.76-0.72), 0.78 (95% CI: 0.82-0.75), and 0.82 (95% CI: 0.86-0.78) at 1-, 3-, and 5-year CSS, respectively. The calibration plots showed good consistency between the actual and predicted values. The DCA curves indicated that the nomogram model could more accurately predict CSS at 1-, 3-, and 5-year in middle-aged patients with advanced hepatocellular carcinoma compared with the AJCC staging system. Highly similar results to the training cohort were also observed in the validation cohort. In the risk stratification system, good differentiation was shown between the 2 groups, and Kaplan-Meier survival analysis indicated that surgery could prolong patient survival. In this study, we developed a nomogram and risk stratification system for predicting CSS in middle-aged patients with advanced hepatocellular carcinoma. The prediction model has good predictive performance and can help clinicians in judging prognosis and clinical decision making.
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Affiliation(s)
- Ziqiang Li
- Department of Hepatobiliary and Pancreatic Surgery, Hubei Provincial Clinical Medicine Research Center for Minimally Invasive Diagnosis and Treatment of Hepatobiliary and Pancreatic Diseases, Zhongnan Hospital of Wuhan University, Wuhan, China
- Department of General Surgery, Tongji Hospital, Tongji University School of Medicine, Shanghai, China
| | - Qingyong Hong
- Department of Hepatobiliary and Pancreatic Surgery, Hubei Provincial Clinical Medicine Research Center for Minimally Invasive Diagnosis and Treatment of Hepatobiliary and Pancreatic Diseases, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Zhidong Guo
- Department of Hepatobiliary and Pancreatic Surgery, Hubei Provincial Clinical Medicine Research Center for Minimally Invasive Diagnosis and Treatment of Hepatobiliary and Pancreatic Diseases, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Xiaohong Liu
- Department of Hepatobiliary and Pancreatic Surgery, Hubei Provincial Clinical Medicine Research Center for Minimally Invasive Diagnosis and Treatment of Hepatobiliary and Pancreatic Diseases, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Chengpeng Tan
- Department of Hepatobiliary and Pancreatic Surgery, Hubei Provincial Clinical Medicine Research Center for Minimally Invasive Diagnosis and Treatment of Hepatobiliary and Pancreatic Diseases, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Zhe Feng
- Department of Hepatobiliary and Pancreatic Surgery, Hubei Provincial Clinical Medicine Research Center for Minimally Invasive Diagnosis and Treatment of Hepatobiliary and Pancreatic Diseases, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Kun Li
- Department of Hepatobiliary and Pancreatic Surgery, Hubei Provincial Clinical Medicine Research Center for Minimally Invasive Diagnosis and Treatment of Hepatobiliary and Pancreatic Diseases, Zhongnan Hospital of Wuhan University, Wuhan, China
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Wang S, Shao M, Fu Y, Zhao R, Xing Y, Zhang L, Xu Y. Deep learning models for predicting the survival of patients with hepatocellular carcinoma based on a surveillance, epidemiology, and end results (SEER) database analysis. Sci Rep 2024; 14:13232. [PMID: 38853169 PMCID: PMC11163004 DOI: 10.1038/s41598-024-63531-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2024] [Accepted: 05/29/2024] [Indexed: 06/11/2024] Open
Abstract
Hepatocellular carcinoma (HCC) is a common malignancy with poor survival and requires long-term follow-up. Hence, we collected information on patients with Primary Hepatocellular Carcinoma in the United States from the Surveillance, Epidemiology, and EndResults (SEER) database. We used this information to establish a deep learning with a multilayer neural network (the NMTLR model) for predicting the survival rate of patients with Primary Hepatocellular Carcinoma. HCC patients pathologically diagnosed between January 2011 and December 2015 in the SEER (Surveillance, Epidemiology, and End Results) database of the National Cancer Institute of the United States were selected as study subjects. We utilized two deep learning-based algorithms (DeepSurv and Neural Multi-Task Logistic Regression [NMTLR]) and a machine learning-based algorithm (Random Survival Forest [RSF]) for model training. A multivariable Cox Proportional Hazards (CoxPH) model was also constructed for comparison. The dataset was randomly divided into a training set and a test set in a 7:3 ratio. The training dataset underwent hyperparameter tuning through 1000 iterations of random search and fivefold cross-validation. Model performance was assessed using the concordance index (C-index), Brier score, and Integrated Brier Score (IBS). The accuracy of predicting 1-year, 3-year, and 5-year survival rates was evaluated using Receiver Operating Characteristic (ROC) curves, calibration plots, and Area Under the Curve (AUC). The primary outcomes were the 1-year, 3-year, and 5-year overall survival rates. Models were developed using DeepSurv, NMTLR, RSF, and Cox Proportional Hazards regression. Model differentiation was evaluated using the C-index, calibration with concordance plots, and risk stratification capability with the log-rank test. The study included 2197 HCC patients, randomly divided into a training cohort (70%, n = 1537) and a testing cohort (30%, n = 660). Clinical characteristics between the two cohorts showed no significant statistical difference (p > 0.05). The deep learning models outperformed both RSF and CoxPH models, with C-indices of 0.735 (NMTLR) and 0.731 (DeepSurv) in the test dataset. The NMTLR model demonstrated enhanced accuracy and well-calibrated survival estimates, achieving an Area Under the Curve (AUC) of 0.824 for 1-year survival predictions, 0.813 for 3-year, and 0.803 for 5-year survival rates. This model's superior calibration and discriminative ability enhance its utility for clinical prognostication in Primary Hepatocellular Carcinoma. We deployed the NMTLR model as a web application for clinical practice. The NMTLR model have potential advantages over traditional linear models in prognostic assessment and treatment recommendations. This novel analytical approach may provide reliable information on individual survival and treatment recommendations for patients with primary liver cancer.
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Affiliation(s)
- Shoucheng Wang
- Department of Gastroenterology, The First Affiliated Hospital of Henan University of Chinese Medicine, The First Clinical Medical College of Henan University of Chinese Medicine, Zhengzhou, 450000, China
| | - Mingyi Shao
- Personnel Department, The First Affiliated Hospitalof Henan University of Chinese Medicine, Zhengzhou, 450000, China.
| | - Yu Fu
- Research Department, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, 450000, China
| | - Ruixia Zhao
- Henan Evidence-Based Medicine Center of Traditional Chinese Medicine, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, 450000, China
| | - Yunfei Xing
- Henan Evidence-Based Medicine Center of Traditional Chinese Medicine, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, 450000, China
| | - Liujie Zhang
- Department of Gastroenterology, The First Affiliated Hospital of Henan University of Chinese Medicine, The First Clinical Medical College of Henan University of Chinese Medicine, Zhengzhou, 450000, China
| | - Yang Xu
- Department of Gastroenterology, The First Affiliated Hospital of Henan University of Chinese Medicine, The First Clinical Medical College of Henan University of Chinese Medicine, Zhengzhou, 450000, China
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Kang X, Liu X, Li Y, Yuan W, Xu Y, Yan H. Development and evaluation of nomograms and risk stratification systems to predict the overall survival and cancer-specific survival of patients with hepatocellular carcinoma. Clin Exp Med 2024; 24:44. [PMID: 38413421 PMCID: PMC10899391 DOI: 10.1007/s10238-024-01296-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2023] [Accepted: 01/13/2024] [Indexed: 02/29/2024]
Abstract
Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer, and patients with HCC have a poor prognosis and low survival rates. Establishing a prognostic nomogram is important for predicting the survival of patients with HCC, as it helps to improve the patient's prognosis. This study aimed to develop and evaluate nomograms and risk stratification to predict overall survival (OS) and cancer-specific survival (CSS) in HCC patients. Data from 10,302 patients with initially diagnosed HCC were extracted from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2017. Patients were randomly divided into the training and validation set. Kaplan-Meier survival, LASSO regression, and Cox regression analysis were conducted to select the predictors of OS. Competing risk analysis, LASSO regression, and Cox regression analysis were conducted to select the predictors of CSS. The validation of the nomograms was performed using the concordance index (C-index), the Akaike information criterion (AIC), the Bayesian information criterion (BIC), Net Reclassification Index (NRI), Discrimination Improvement (IDI), the receiver operating characteristic (ROC) curve, calibration curves, and decision curve analyses (DCAs). The results indicated that factors including age, grade, T stage, N stage, M stage, surgery, surgery to lymph node (LN), Alpha-Fetal Protein (AFP), and tumor size were independent predictors of OS, whereas grade, T stage, surgery, AFP, tumor size, and distant lymph node metastasis were independent predictors of CSS. Based on these factors, predictive models were built and virtualized by nomograms. The C-index for predicting 1-, 3-, and 5-year OS were 0.788, 0.792, and 0.790. The C-index for predicting 1-, 3-, and 5-year CSS were 0.803, 0.808, and 0.806. AIC, BIC, NRI, and IDI suggested that nomograms had an excellent predictive performance with no significant overfitting. The calibration curves showed good consistency of OS and CSS between the actual observation and nomograms prediction, and the DCA showed great clinical usefulness of the nomograms. The risk stratification of OS and CSS was built that could perfectly classify HCC patients into three risk groups. Our study developed nomograms and a corresponding risk stratification system predicting the OS and CSS of HCC patients. These tools can assist in patient counseling and guiding treatment decision making.
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Affiliation(s)
- Xichun Kang
- Department of Epidemiology and Health Statistics, School of Public Health, Hebei Medical University, Shijiazhuang, 050017, China
| | - Xiling Liu
- Department of Epidemiology and Health Statistics, School of Public Health, Hebei Medical University, Shijiazhuang, 050017, China
| | - Yaoqi Li
- Department of Epidemiology and Health Statistics, School of Public Health, Hebei Medical University, Shijiazhuang, 050017, China
| | - Wenfang Yuan
- Department of the Sixth Infection, The Fifth Hospital of Shijiazhuang, Shijiazhuang, 050021, China
| | - Yi Xu
- Department of Laboratory Medicine, The Fifth Hospital of Shijiazhuang, Shijiazhuang, 050021, China
| | - Huimin Yan
- Department of Epidemiology and Health Statistics, School of Public Health, Hebei Medical University, Shijiazhuang, 050017, China.
- Clinical Research Center, The Fifth Hospital of Shijiazhuang, Shijiazhuang, 050021, China.
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Wang Y, Ge L, Cai Y. The novel predictive nomograms for early death in metastatic hepatocellular carcinoma: A large cohort study. Medicine (Baltimore) 2024; 103:e36812. [PMID: 38181257 PMCID: PMC10766267 DOI: 10.1097/md.0000000000036812] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 12/07/2023] [Indexed: 01/07/2024] Open
Abstract
Metastatic hepatocellular carcinoma (HCC) is an aggressive disease which usually have a poor prognosis. Early mortality and risk factors in patients with metastatic HCC are poorly understood. Our study sought to identify associated risk factors and develop the nomograms for predicting early death in metastatic HCC patients. The patients diagnosed with metastatic HCC were chosen from the surveillance, epidemiology, and end results database between 2010 and 2015. To identify significant independent risk factors for early death, both univariate and multivariate logistic regression models were used. We constructed a pragmatic nomogram and then evaluated by using receiver operating characteristic curves, calibration plots, and decision curve analysis. The prediction model included 2587 patients with metastatic HCC. Among them, 1550 experienced early death (died within 3 months of initial diagnosis) and 1437 died from cancer-specific causes. Multivariate logistic regression analysis found that grade, surgery, radiation, chemotherapy, alpha-fetoprotein levels, and lung metastasis were independent risk factors for both all-cause early death and cancer-specific early death. In addition, bone metastasis were independent risk factors for all-cause early death, T-stage and brain metastasis were also independent risk factors for cancer-specific early death. Then we used the relevant risk factors to developed the practical nomograms of all-cause and cancer-specific early deaths. The nomograms demonstrated good predictive power and clinical utility under receiver operating characteristic curves and decision curve analysis. We developed 2 novel comprehensive nomograms to predict early death among metastatic HCC patients. Nomograms may help oncologists develop better treatment strategies and implementation of individualized treatment plans.
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Affiliation(s)
- Yue Wang
- Department of Medical Insurance Office, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Long Ge
- Department of Medical Insurance Office, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Yan Cai
- Department of Medical Insurance Office, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China
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Zhan G, Cao P, Peng H. Construction of web -based prediction nomogram models for cancer -specific survival in patients at stage IV of hepatocellular carcinoma depending on SEER database. ZHONG NAN DA XUE XUE BAO. YI XUE BAN = JOURNAL OF CENTRAL SOUTH UNIVERSITY. MEDICAL SCIENCES 2023; 48:1546-1560. [PMID: 38432884 PMCID: PMC10929905 DOI: 10.11817/j.issn.1672-7347.2023.230040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Indexed: 03/05/2024]
Abstract
OBJECTIVES Hepatocellular carcinoma (HCC) prognosis involves multiple clinical factors. Although nomogram models targeting various clinical factors have been reported in early and locally advanced HCC, there are currently few studies on complete and effective prognostic nomogram models for stage IV HCC patients. This study aims to creat nomograms for cancer-specific survival (CSS) in patients at stage IV of HCC and developing a web predictive nomogram model to predict patient prognosis and guide individualized treatment. METHODS Clinicopathological information on stage IV of HCC between January, 2010 and December, 2015 was collected from the Surveillance, Epidemiology, and End Results (SEER) database. The patients at stage IV of HCC were categorized into IVA (without distant metastases) and IVB (with distant metastases) subgroups based on the presence of distant metastasis, and then the patients from both IVA and IVB subgroups were randomly divided into the training and validation cohorts in a 7꞉3 ratio. Univariate and multivariate Cox regression analyses were used to analyze the independent risk factors that significantly affected CSS in the training cohort, and constructed nomogram models separately for stage IVA and stage IVB patients based on relevant independent risk factors. Two nomogram's accuracy and discrimination were evaluated by receiver operator characteristic (ROC) curves and calibration curves. Furthermore, web-based nomogram models were developed specifically for stage IVA and stage IVB HCC patients by R software. A decision analysis curve (DCA) was used to evaluate the clinical utility of the web-based nomogram models. RESULTS A total of 3 060 patients were included in this study, of which 883 were in stage IVA, and 2 177 were in stage IVB. Based on multivariate analysis results, tumor size, alpha-fetoprotein (AFP), T stage, histological grade, surgery, radiotherapy, and chemotherapy were independent prognostic factors for patients with stage IVA of HCC; and tumor size, AFP, T stage, N stage, histological grade, lung metastasis, surgery, radiotherapy, and chemotherapy were independent prognostic factors for patients with stage IVB HCC. In stage IVA patients, the 3-, 6-, 9-, 12-, 15-, and 18-month areas under the ROC curves for the training cohort were 0.823, 0.800, 0.772, 0.784, 0.784, and 0.786, respectively; and the 3-, 6-, 9-, 12-, 15-, and 18-month areas under the ROC curves for the validation cohort were 0.793, 0.764, 0.739, 0.773, 0.798, and 0.799, respectively. In stage IVB patients, the 3-, 6-, 9-, and 12-month areas under the ROC curves for the training cohort were 0.756, 0.750, 0.755, and 0.743, respectively; and the 3-, 6-, 9-, and 12-month areas under the ROC curves for the validation cohort were 0.744, 0.747, 0.775, and 0.779, respectively; showing that the nomograms had an excellent predictive ability. The calibration curves showed a good consistency between the predictions and actual observations. CONCLUSIONS Predictive nomogram models for CSS in stage IVA and IVB HCC patients are developed and validated based on the SEER database, which might be used for clinicians to predict the prognosis, implement individualized treatment, and follow up those patients.
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Affiliation(s)
- Gouling Zhan
- Department of Oncology, Third Xiangya Hospital, Central South University, Changsha 410013, China.
| | - Peiguo Cao
- Department of Oncology, Third Xiangya Hospital, Central South University, Changsha 410013, China.
| | - Honghua Peng
- Department of Oncology, Third Xiangya Hospital, Central South University, Changsha 410013, China.
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A nomogram for predicting prognosis of multiple myeloma patients based on a ubiquitin-proteasome gene signature. Aging (Albany NY) 2022; 14:9951-9968. [PMID: 36534449 PMCID: PMC9831738 DOI: 10.18632/aging.204432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Accepted: 11/21/2022] [Indexed: 12/23/2022]
Abstract
BACKGROUND Multiple myeloma (MM) is a malignant hematopoietic disease that is usually incurable. However, the ubiquitin-proteasome system (UPS) genes have not yet been established as a prognostic predictor for MM, despite their potential applications in other cancers. METHODS RNA sequencing data and corresponding clinical information were acquired from Multiple Myeloma Research Foundation (MMRF)-COMMPASS and served as a training set (n=787). Validation of the prediction signature were conducted by the Gene Expression Omnibus (GEO) databases (n=1040). To develop a prognostic signature for overall survival (OS), least absolute shrinkage and selection operator regressions, along with Cox regressions, were used. RESULTS A six-gene signature, including KCTD12, SIAH1, TRIM58, TRIM47, UBE2S, and UBE2T, was established. Kaplan-Meier survival analysis of the training and validation cohorts revealed that patients with high-risk conditions had a significantly worse prognosis than those with low-risk conditions. Furthermore, UPS-related signature is associated with a positive immune response. For predicting survival, a simple to use nomogram and the corresponding web-based calculator (https://jiangyanxiamm.shinyapps.io/MMprognosis/) were built based on the UPS signature and its clinical features. Analyses of calibration plots and decision curves showed clinical utility for both training and validation datasets. CONCLUSIONS As a result of these results, we established a genetic signature for MM based on UPS. This genetic signature could contribute to improving individualized survival prediction, thereby facilitating clinical decisions in patients with MM.
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