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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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Chen W, Cheng S. Nomogram and calculator for predicting the prognosis of patients with giant hepatocellular carcinoma. Expert Rev Anticancer Ther 2024; 24:781-788. [PMID: 38874538 DOI: 10.1080/14737140.2024.2369129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Accepted: 05/29/2024] [Indexed: 06/15/2024]
Abstract
OBJECTIVES This study aimed to explore the factors affecting the overall survival (OS) of giant hepatocellular carcinoma (G-HCC) patients and establish a nomogram and an Internet-based OS calculator for evaluating the OS of G-HCC patients. RESEARCH DESIGN AND METHODS A total of 2445 G-HCC patients were searched in the SEER database. The independent variables affecting OS of G-HCC patients were determined by univariate and multivariate analyses, and a nomogram and Internet-based OS calculator were established. The accuracy of the nomogram was evaluated by the C-index, the AUC curve, and calibration curve. RESULTS Grade, surgery, radiotherapy, chemotherapy, T-staging, M-staging, AFP, and fibrosis were identified as independent variables affecting OS. These variables were included in the nomogram model and Internet-based OS calculator to evaluate OS in G-HCC patients. The C-indices and AUC of the nomogram are better than AJCC-staging system. Similarly, the calibration curves revealed that the actual survival was consistent with nomogram-based survival. CONCLUSION The nomogram and Internet-based OS calculator are superior to the traditional AJCC-staging system in the reliability and convenience of prognosis assessment for G-HCC patients, which is more conducive for clinicians to predict the survival of G-HCC patients and make the best treatment strategy.
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Affiliation(s)
- Wanjin Chen
- Key Laboratory of Clinical Laboratory Diagnostics (Ministry of Education), College of Laboratory Medicine, Chongqing Medical University, Chongqing, China
- The Key Laboratory of Molecular Biology of Infectious Diseases Designated by the Chinese Ministry of Education, Chongqing Medical University, Chongqing, China
| | - Shengtao Cheng
- The Key Laboratory of Molecular Biology of Infectious Diseases Designated by the Chinese Ministry of Education, Chongqing Medical University, Chongqing, China
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Dong B, Zhang H, Duan Y, Yao S, Chen Y, Zhang C. Development of a machine learning-based model to predict prognosis of alpha-fetoprotein-positive hepatocellular carcinoma. J Transl Med 2024; 22:455. [PMID: 38741163 PMCID: PMC11092049 DOI: 10.1186/s12967-024-05203-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 04/15/2024] [Indexed: 05/16/2024] Open
Abstract
BACKGROUND Patients with alpha-fetoprotein (AFP)-positive hepatocellular carcinoma (HCC) have aggressive biological behavior and poor prognosis. Therefore, survival time is one of the greatest concerns for patients with AFP-positive HCC. This study aimed to demonstrate the utilization of six machine learning (ML)-based prognostic models to predict overall survival of patients with AFP-positive HCC. METHODS Data on patients with AFP-positive HCC were extracted from the Surveillance, Epidemiology, and End Results database. Six ML algorithms (extreme gradient boosting [XGBoost], logistic regression [LR], support vector machine [SVM], random forest [RF], K-nearest neighbor [KNN], and decision tree [ID3]) were used to develop the prognostic models of patients with AFP-positive HCC at one year, three years, and five years. Area under the receiver operating characteristic curve (AUC), confusion matrix, calibration curves, and decision curve analysis (DCA) were used to evaluate the model. RESULTS A total of 2,038 patients with AFP-positive HCC were included for analysis. The 1-, 3-, and 5-year overall survival rates were 60.7%, 28.9%, and 14.3%, respectively. Seventeen features regarding demographics and clinicopathology were included in six ML algorithms to generate a prognostic model. The XGBoost model showed the best performance in predicting survival at 1-year (train set: AUC = 0.771; test set: AUC = 0.782), 3-year (train set: AUC = 0.763; test set: AUC = 0.749) and 5-year (train set: AUC = 0.807; test set: AUC = 0.740). Furthermore, for 1-, 3-, and 5-year survival prediction, the accuracy in the training and test sets was 0.709 and 0.726, 0.721 and 0.726, and 0.778 and 0.784 for the XGBoost model, respectively. Calibration curves and DCA exhibited good predictive performance as well. CONCLUSIONS The XGBoost model exhibited good predictive performance, which may provide physicians with an effective tool for early medical intervention and improve the survival of patients.
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Affiliation(s)
- Bingtian Dong
- Department of Ultrasound, the First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Hua Zhang
- Department of Ultrasound, Affiliated Hangzhou First People's Hospital, School of Medicine, Westlake University, Hangzhou, China
| | - Yayang Duan
- Department of Ultrasound, the First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Senbang Yao
- Department of Oncology, The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
- Department of Oncology, Anhui Medical University, Hefei, Anhui, China
| | - Yongjian Chen
- Department of Ultrasound, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China.
| | - Chaoxue Zhang
- Department of Ultrasound, the First Affiliated Hospital of Anhui Medical University, Hefei, China.
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Fang C, Xu C, Jia X, Li X, Yin C, Xing X, Li W, Wang Z. Development and validation of a clinical prediction model for the risk of distal metastasis in intrahepatic cholangiocarcinoma: a real-world study. BMC Gastroenterol 2024; 24:1. [PMID: 38166611 PMCID: PMC10759461 DOI: 10.1186/s12876-023-03084-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Accepted: 12/08/2023] [Indexed: 01/05/2024] Open
Abstract
BACKGROUND Cholangiocarcinoma (CCA) is a highly malignant and easily metastatic bile duct tumor with poor prognosis. We aimed at studying the associated risk factors affecting distal metastasis of CCA and using nomogram to guide clinicians in predicting distal metastasis of CCA. METHODS Based on inclusion and exclusion criteria, 345 patients with CCA were selected from the Fifth Medical Center of Chinese PLA General Hospital and were divided into distal metastases (N = 21) and non-distal metastases (N = 324). LASSO regression models were used to screen for relevant parameters and to compare basic clinical information between the two groups of patients. Risk factors for distal metastasis were identified based on the results of univariate and multivariate logistic regression analyses. The nomogram was established based on the results of multivariate logistic regression, and we drawn the corresponding correlation heat map. The predictive accuracy of the nomogram was evaluated by receiver operating characteristic (ROC) curves and calibration plots. The utility of the model in clinical applications was illustrated by applying decision curve analysis (DCA), and overall survival(OS) analysis was performed using the method of Kaplan-meier. RESULTS This study identified 4 independent risk factors for distal metastasis of CCA, including CA199, cholesterol, hypertension and margin invasion, and developed the nomogram based on this. The result of validation showed that the model had significant accuracy for diagnosis with the area under ROC (AUC) of 0.882 (95% CI: 0.843-0.914). Calibration plots and DCA showed that the model had high clinical utility. CONCLUSIONS This study established and validated a model of nomogram for predicting distal metastasis in patients with CCA. Based on this, it could guide clinicians to make better decisions and provide more accurate prognosis and treatment for patients with CCA.
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Affiliation(s)
- Caixia Fang
- Pharmacy Department, Clinical Drug Research Center, Qingyang People's Hospital, Qingyang, China
| | - Chan Xu
- State Key Laboratory of MolecularVaccinology and Molecular Diagnostics & Center for Molecular Imaging and Translational Medicine, School of Public Health, Xiamen University, Xiamen, 361102, China
| | - Xiaodong Jia
- Comprehensive Liver Cancer Center, The Fifth Medical Center of PLA General Hospital, Beijing, China
| | - Xiaoping Li
- Shulan International Medical College, Zhejiang Shuren University, Hangzhou, China
| | - Chengliang Yin
- Faculty of Medicine, Macau University of Science and Technology, Macau, China
| | - Xiaojuan Xing
- Department of Neurology, Qingyang People's Hospital, Qingyang, China.
| | - Wenle Li
- State Key Laboratory of MolecularVaccinology and Molecular Diagnostics & Center for Molecular Imaging and Translational Medicine, School of Public Health, Xiamen University, Xiamen, 361102, China.
| | - Zhenyun Wang
- Urology Department of Qingyang People's Hospital, Qingyang, China.
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Yang YP, Guo CJ, Gu ZX, Hua JJ, Zhang JX, Shi J. Conditional survival probability of distant-metastatic hepatocellular carcinoma: A population-based study. World J Gastrointest Oncol 2023; 15:1874-1890. [DOI: 10.4251/wjgo.v15.i11.1874] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/24/2023] [Revised: 08/20/2023] [Accepted: 09/06/2023] [Indexed: 11/15/2023] Open
Abstract
BACKGROUND The prognosis of many patients with distant metastatic hepatocellular carcinoma (HCC) improved after they survived for several months. Compared with traditional survival analysis, conditional survival (CS) which takes into account changes in survival risk could be used to describe dynamic survival probabilities.
AIM To evaluate CS of distant metastatic HCC patients.
METHODS Patients diagnosed with distant metastatic HCC between 2010 and 2015 were extracted from the Surveillance, Epidemiology and End Results database. Univariate and multivariate Cox regression analysis were used to identify risk factors for overall survival (OS), while competing risk model was used to identify risk factors for cancer-specific survival (CSS). Six-month CS was used to calculate the probability of survival for an additional 6 mo at a specific time after initial diagnosis, and standardized difference (d) was used to evaluate the survival differences between subgroups. Nomograms were constructed to predict CS.
RESULTS Positive α-fetoprotein expression, higher T stage (T3 and T4), N1 stage, non-primary site surgery, non-chemotherapy, non-radiotherapy, and lung metastasis were independent risk factors for actual OS and CSS through univariate and multivariate analysis. Actual survival rates decreased over time, while CS rates gradually increased. As for the 6-month CS, the survival difference caused by chemotherapy and radiotherapy gradually disappeared over time, and the survival difference caused by lung metastasis reversed. Moreover, the influence of age and gender on survival gradually appeared. Nomograms were fitted for patients who have lived for 2, 4 and 6 mo to predict 6-month conditional OS and CSS, respectively. The area under the curve (AUC) of nomograms for conditional OS decreased as time passed, and the AUC for conditional CSS gradually increased.
CONCLUSION CS for distant metastatic HCC patients substantially increased over time. With dynamic risk factors, nomograms constructed at a specific time could predict more accurate survival rates.
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Affiliation(s)
- Yong-Ping Yang
- Department of General Surgery, The Second Hospital of Jilin University, Changchun 130041, Jilin Province, China
| | - Cheng-Jun Guo
- Department of General Surgery, The Second Hospital of Jilin University, Changchun 130041, Jilin Province, China
| | - Zhao-Xuan Gu
- Department of General Surgery, The Second Hospital of Jilin University, Changchun 130041, Jilin Province, China
| | - Jun-Jie Hua
- Department of General Surgery, The Second Hospital of Jilin University, Changchun 130041, Jilin Province, China
| | - Jia-Xuan Zhang
- Department of General Surgery, The Second Hospital of Jilin University, Changchun 130041, Jilin Province, China
| | - Jian Shi
- Department of General Surgery, The Second Hospital of Jilin University, Changchun 130041, Jilin Province, China
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Kuang T, Ma W, Zhang J, Yu J, Deng W, Dong K, Wang W. Construction of a Nomogram to Predict Overall Survival in Patients with Early-Onset Hepatocellular Carcinoma: A Retrospective Cohort Study. Cancers (Basel) 2023; 15:5310. [PMID: 38001570 PMCID: PMC10670167 DOI: 10.3390/cancers15225310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 10/31/2023] [Accepted: 11/03/2023] [Indexed: 11/26/2023] Open
Abstract
Hepatocellular carcinoma (HCC) is a widespread and impactful cancer which has pertinent implications worldwide. Although most cases of HCC are typically diagnosed in individuals aged ≥60 years, there has been a notable rise in the occurrence of HCC among younger patients. However, there is a scarcity of precise prognostic models available for predicting outcomes in these younger patients. A retrospective analysis was conducted to investigate early-onset hepatocellular carcinoma (EO-LIHC) using data from the Surveillance, Epidemiology, and End Results (SEER) database from 2004 to 2018. The analysis included 1392 patients from the SEER database and our hospital. Among them, 1287 patients from the SEER database were assigned to the training cohort (n = 899) and validation cohort 1 (n = 388), while 105 patients from our hospital were assigned to validation cohort 2. A Cox regression analysis showed that age, sex, AFP, grade, stage, tumor size, surgery, and chemotherapy were independent risk factors. The nomogram developed in this study demonstrated its discriminatory ability to predict the 1-, 3-, and 5-year overall survival (OS) rates in EO-LIHC patients based on individual characteristics. Additionally, a web-based OS prediction model specifically tailored for EO-LIHC patients was created and validated. Overall, these advancements contribute to improved decision-making and personalized care for individuals with EO-LIHC.
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Affiliation(s)
- Tianrui Kuang
- Department of General Surgery, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Central Laboratory, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Wangbin Ma
- Department of General Surgery, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Central Laboratory, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Jiacheng Zhang
- Department of General Surgery, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Central Laboratory, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Jia Yu
- Department of General Surgery, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Central Laboratory, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Wenhong Deng
- Department of General Surgery, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Central Laboratory, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Keshuai Dong
- Department of General Surgery, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Central Laboratory, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Weixing Wang
- Department of General Surgery, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Central Laboratory, Renmin Hospital of Wuhan University, Wuhan 430060, China
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Che WQ, Li YJ, Tsang CK, Wang YJ, Chen Z, Wang XY, Xu AD, Lyu J. How to use the Surveillance, Epidemiology, and End Results (SEER) data: research design and methodology. Mil Med Res 2023; 10:50. [PMID: 37899480 PMCID: PMC10614369 DOI: 10.1186/s40779-023-00488-2] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Accepted: 10/16/2023] [Indexed: 10/31/2023] Open
Abstract
In the United States (US), the Surveillance, Epidemiology, and End Results (SEER) program is the only comprehensive source of population-based information that includes stage of cancer at the time of diagnosis and patient survival data. This program aims to provide a database about cancer incidence and survival for studies of surveillance and the development of analytical and methodological tools in the cancer field. Currently, the SEER program covers approximately half of the total cancer patients in the US. A growing number of clinical studies have applied the SEER database in various aspects. However, the intrinsic features of the SEER database, such as the huge data volume and complexity of data types, have hindered its application. In this review, we provided a systematic overview of the commonly used methodologies and study designs for retrospective epidemiological research in order to illustrate the application of the SEER database. Therefore, the goal of this review is to assist researchers in the selection of appropriate methods and study designs for enhancing the robustness and reliability of clinical studies by mining the SEER database.
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Affiliation(s)
- Wen-Qiang Che
- Department of Neurosurgery, the First Affiliated Hospital of Jinan University, Guangzhou, 510632, China
- Department of Clinical Research, the First Affiliated Hospital of Jinan University, Guangzhou, 510632, China
| | - Yuan-Jie Li
- Planning & Discipline Construction Office, the First Affiliated Hospital of Jinan University, Guangzhou, 510632, China
| | - Chi-Kwan Tsang
- Clinical Neuroscience Institute, the First Affiliated Hospital of Jinan University, Guangzhou, 510632, China
| | - Yu-Jiao Wang
- Department of Pathology, Shanxi Provincial People's Hospital, Taiyuan, 030012, China
| | - Zheng Chen
- Department of Urology, the First Affiliated Hospital of Jinan University, Guangzhou, 510632, China
| | - Xiang-Yu Wang
- Department of Neurosurgery, the First Affiliated Hospital of Jinan University, Guangzhou, 510632, China.
| | - An-Ding Xu
- Department of Neurology, the First Affiliated Hospital of Jinan University, Guangzhou, 510632, China.
| | - Jun Lyu
- Department of Clinical Research, the First Affiliated Hospital of Jinan University, Guangzhou, 510632, China.
- Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Informatization, Guangzhou, 510632, 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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Zhou H, Chen J, Liu K, Xu H. Prognostic factors and predictive nomogram models for early death in elderly patients with hepatocellular carcinoma: a population-based study. Front Mol Biosci 2023; 10:1275791. [PMID: 37908229 PMCID: PMC10613697 DOI: 10.3389/fmolb.2023.1275791] [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: 08/10/2023] [Accepted: 10/05/2023] [Indexed: 11/02/2023] Open
Abstract
Background: Owing to an aging society, there has been an observed increase in the average age of patients diagnosed with hepatocellular carcinoma (HCC). Consequently, this study is centered on identifying the prognostic factors linked with early death among this elderly demographic diagnosed with HCC. Additionally, our focus extends to developing nomograms capable of predicting such outcomes. Methods: The Surveillance, Epidemiology and End Results (SEER) database underpinned this study, showcasing participants aged 75 and above diagnosed with HCC within the timeframe from 2010 to 2015. These participants were divided randomly, at a 7:3 ratio, into training and validation cohorts. Univariable and multivariable logistic regressions were applied to the training cohort in the identification of prognostic indicators of early death, forming the basis for nomogram development. To measure the efficacy of these nomograms within both cohorts, we resorted to Receiver Operating Characteristic (ROC) curves, along with GiViTI calibration belt and Decision Curve Analysis (DCA). Results: The study involved 1,163 elderly individuals diagnosed with HCC, having reported instances of 397 all-cause early deaths and 356 HCC-specific early deaths. The sample group was divided into two cohorts: a training group consisting of 815 individuals, and a validation cohort, comprised of 348 individuals. Multifactorial analysis identified grade, T-stage, surgery, radiation, chemotherapy, bone and lung metastasis as significant predictors of mortality from all causes. Meanwhile, race, grade, T-stage, surgery, radiation, chemotherapy, and bone metastasis were revealed to be estimative factors for cancer-specific mortality. Subsequently, these factors were used to develop nomograms for prediction. GiViTI calibration belt corroborated the acceptable coherence of the nomograms, DCA confirmed their valuable clinical applicability, and ROC curves evidenced satisfactory discriminative capacity within both training and validation cohorts. Conclusion: The nomograms utilized in this study proved instrumental in detecting early death among elderly individuals afflicted with HCC. This tool could potentially assist physicians in formulating individualized treatment strategies.
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Affiliation(s)
- Hao Zhou
- Department of Hepatobiliary and Pancreatic Surgery II, General Surgery Center, The First Hospital of Jilin University, Changchun, China
| | - Junhong Chen
- Department of Hepatobiliary and Pancreatic Surgery II, General Surgery Center, The First Hospital of Jilin University, Changchun, China
| | - Kai Liu
- Department of Hepatobiliary and Pancreatic Surgery II, General Surgery Center, The First Hospital of Jilin University, Changchun, China
| | - Hongji Xu
- Department of Abdominal Surgery, Guiqian International General Hospital, Guiyang, Guizhou, China
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Liu X, Li H, Wang F, Su K, He B, He J, Zhong J, Han Y, Li Z. Transhepatectomy combined with arterial chemoembolization and transcatheter arterial chemoembolization in the treatment of hepatocellular carcinoma: a clinical prognostic analysis. BMC Gastroenterol 2023; 23:299. [PMID: 37670232 PMCID: PMC10478419 DOI: 10.1186/s12876-023-02886-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Accepted: 07/13/2023] [Indexed: 09/07/2023] Open
Abstract
BACKGROUND The prognosis of patients undergoing hepatectomy combined with transarterial chemoembolization (TACE) and TACE alone was examined in order to better understand the role of hepatectomy in the treatment of hepatocellular carcinoma (HCC). In this work, we also created a model and investigated the variables influencing overall survival (OS) in HCC patients. METHODS Retrospective analysis of 1083 patients who received TACE alone as the control group and 188 patients who received TACE after surgery in a total of 1271 HCC patients treated with LR + TACE or TACE at three third-class hospitals in China. It was done using the Propensity Score Matching (PSM) technique. The differences in OS between the two groups were compared, and OS-influencing factors were looked at. The main endpoint is overall survival. In this study, the COX regression model was used to establish the nomogram. RESULTS The median OS of the LR + TACE group was not attained after PSM. The median OS for the TACE group was 28.8 months (95% CI: 18.9-38.7). The median OS of the LR + TACE group was higher than that of the TACE group alone, indicating a significant difference between the two groups (χ2 = 16.75, P < 0.001). While it was not achieved in the LR + TACE group, the median OS for patients with lymph node metastases in the TACE group alone was 18.8 months. The two groups differed significantly from one another (χ2 = 4.105, P = 0.043). In patients with distant metastases, the median OS of the LR + TACE treatment group was not achieved, and the median OS of the TACE group alone was 12.0 months. The difference between the two groups was sizable (χ2 = 5.266, P = 0.022). The median OS for patients with PVTT following PSM was 30.1 months in the LR + TACE treatment group and 18.7 months in the TACE alone group, respectively. The two groups differed significantly from one another (χ2 = 5.178, P = 0.023); There was no discernible difference between the two groups in terms of median overall survival (OS), which was 30.1 months for patients with lymph node metastasis and 19.2 months for those without (P > 0.05); Regarding the median OS for patients with distant metastases, which was not achieved and 8.5 months, respectively, there was a significant difference between the two groups (χ2 = 5.759, P = 0.016). We created a new nomogram to predict 1-, 2-, and 3-year survival rates based on multiple independent predictors in COX multivariate analysis. The cohort's C-index is 0.705. The area under the curve (AUC value) for predicting 1-, 2-, and 3-year survival rates were shown by the subject operating characteristic (ROC) curve linked to the nomogram to be 0.730, 0.728, and 0.691, respectively. CONCLUSIONS LR + TACE can increase OS, delay tumor recurrence, and improve prognosis in HCC patients when compared to TACE alone. Additionally, the nomogram we created does a good job of forecasting the 1-year survival rate of hepatocellular carcinoma.
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Affiliation(s)
- Xin Liu
- Department of Oncology, Affiliated Hospital of Southwest Medical University, Luzhou, China
- Department of Radiophysics and Technology, Shandong First Medical University (Shandong Academy of Medical Sciences), Shandong Institute of Cancer Prevention and Treatment (Shandong Cancer Hospital), Jinan, China
| | - Haodong Li
- Department of Radiophysics and Technology, Shandong First Medical University (Shandong Academy of Medical Sciences), Shandong Institute of Cancer Prevention and Treatment (Shandong Cancer Hospital), Jinan, China
- Graduate Department of Shandong First Medical University (Shandong Academy of Medical Sciences), Jinan, China
| | - Fei Wang
- Department of General Surgery, Luxian People's Hospital, Luzhou, China
| | - Ke Su
- Department of Oncology, Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Bingsheng He
- Department of Oncology, Affiliated Hospital of Southwest Medical University, Luzhou, China
- Shandong Institute of Cancer Prevention and Treatment (Shandong Cancer Hospital), Department of Radiotherapy, Shandong First Medical University (Shandong Academy of Medical Sciences), Jinan, China
| | - Jie He
- Department of Oncology, Affiliated Hospital of Southwest Medical University, Luzhou, China
- Shandong Institute of Cancer Prevention and Treatment (Shandong Cancer Hospital), Department of Radiotherapy, Shandong First Medical University (Shandong Academy of Medical Sciences), Jinan, China
| | - Jiaqi Zhong
- Department of Oncology, Affiliated Hospital of Southwest Medical University, Luzhou, China
- Shandong Institute of Cancer Prevention and Treatment (Shandong Cancer Hospital), Department of Radiotherapy, Shandong First Medical University (Shandong Academy of Medical Sciences), Jinan, China
| | - Yunwei Han
- Department of Oncology, Affiliated Hospital of Southwest Medical University, Luzhou, China.
| | - Zhenjiang Li
- Department of Radiophysics and Technology, Shandong First Medical University (Shandong Academy of Medical Sciences), Shandong Institute of Cancer Prevention and Treatment (Shandong Cancer Hospital), Jinan, China.
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Yang R, Yu X, Zeng P. Construction and validation of a SEER-based prognostic nomogram for young and middle-aged males patients with hepatocellular carcinoma. J Cancer Res Clin Oncol 2023; 149:10099-10108. [PMID: 37266663 DOI: 10.1007/s00432-023-04901-0] [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: 04/15/2023] [Accepted: 05/20/2023] [Indexed: 06/03/2023]
Abstract
BACKGROUND Hepatocellular carcinoma (HCC) is the most common digestive tumor, and we aimed to develop and validate nomogram models, predicting the overall survival (OS) of young and middle-aged male patients with HCC. METHODS We extracted eligible data from relevant patients between 2000 and 2017 from the Surveillance, Epidemiology, and End Results (SEER) database. In addition, randomly divided all patients into two groups (training and validation = 7:3). The nomogram was established using effective risk factors based on univariate and multivariate analysis. The area under the time-dependent curve, calibration plots, and decision curve analysis (DCA) were used to evaluate the effective performance of the nomogram. The risk stratifications of the nomogram and the AJCC criteria-based tumor stage were compared. RESULTS 11 variables were selected by univariate and multivariate analysis to establish the nomogram of HCC. The AUC values of 3, 4, and 5 years of the time-ROC curve are 0.858, 0.862 and 0.859 for the training cohort, and 0.858, 0.877 and 0.869 for the validation cohort, respectively, indicating that the nomogram has a good ability of discrimination. The calibration plots showed favorable consistency between the prediction of the nomogram and actual observations in both the training and validation cohorts. In addition, the decision curve DCA showed that the nomogram was clinically useful and had better discriminative ability to recognize patients at high risk than the AJCC criteria-based tumor stage. CONCLUSION Prognostic nomogram of young and middle-aged male patients with HCC was developed and validated to help clinicians evaluate the prognosis of patients.
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Affiliation(s)
- Renyi Yang
- School of Integrated Traditional Chinese and Western Medicine, Hunan University of Chinese Medicine, Changsha, Hunan, 410208, People's Republic of China
| | - Xiaopeng Yu
- Hunan University of Chinese Medicine, Changsha, Hunan, 410208, People's Republic of China
| | - Puhua Zeng
- Cancer Research Institute of Hunan Academy of Traditional Chinese Medicine, Changsha, 410006, People's Republic of China.
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Huang S, Zhu Z, Ruan Y, Zhang F, Xu Y, Jin L, Lopez-Lopez V, Merle P, Lu G, Li L. Prognostic factors and survival prediction in hepatocellular carcinoma: development and validation of a novel nomogram based on the SEER database. J Gastrointest Oncol 2023; 14:1817-1829. [PMID: 37720431 PMCID: PMC10502553 DOI: 10.21037/jgo-23-427] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 07/14/2023] [Indexed: 09/19/2023] Open
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 7th 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.
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Affiliation(s)
- Shanshan Huang
- Department of Infectious Disease, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, China
| | - Zheng Zhu
- The Second Clinical Medical College, Wenzhou Medical University, Wenzhou, China
| | - Yejiao Ruan
- Department of Gastroenterology, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, China
| | - Fayuan Zhang
- The Second Clinical Medical College, Wenzhou Medical University, Wenzhou, China
| | - Yueting Xu
- The Second Clinical Medical College, Wenzhou Medical University, Wenzhou, China
| | - Lingxiang Jin
- Department of Infectious Disease, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, China
| | - Victor Lopez-Lopez
- Department of General, Visceral and Transplantation Surgery, Clinic and University Hospital Virgen de la Arrixaca, IMIB-Arrixaca, Murcia, Spain
| | - Philippe Merle
- Hepatology Unit, University Croix-Rousse Hospital, Hospices Civils de Lyon, Lyon, France
| | - Guangrong Lu
- Department of Gastroenterology, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, China
| | - Liyi Li
- Department of Gastrointestinal Surgery, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, China
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Ding Q, Wang K, Li Y, Peng P, Zhang D, Chang D, Wang W, Ren L, Tang F, Li Z. Clinical Characteristics and Survival Analysis of Patients With Second Primary Malignancies After Hepatocellular Carcinoma Liver Transplantation: A SEER-based Analysis. Am J Clin Oncol 2023; 46:284-292. [PMID: 37145881 PMCID: PMC10281177 DOI: 10.1097/coc.0000000000001004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/07/2023]
Abstract
BACKGROUND Second primary malignancies (SPMs) after liver transplantation (LT) are becoming the leading causes of death in LT recipients. The purpose of this study was to explore prognostic factors for SPMs and to establish an overall survival nomogram. METHODS A retrospective analysis was conducted of data from the Surveillance, Epidemiology, and End Results (SEER) database on adult patients with primary hepatocellular carcinoma who had undergone LT between 2004 and 2015. Cox regression analysis was used to explore the independent prognostic factors for SPMs. Nomogram was constructed using R software to predict the overall survival at 2, 3, and 5 years. The concordance index, calibration curves, and decision curve analysis were used to evaluate the clinical prediction model. RESULTS Data from a total of 2078 patients were eligible, of whom 221 (10.64%) developed SPMs. A total of 221 patients were split into a training cohort (n=154) or a validation cohort (n=67) with a 7:3 ratio. The 3 most common SPMs were lung cancer, prostate cancer, and non-Hodgkin lymphoma. Age at initial diagnosis, marital status, year of diagnosis, T stage, and latency were the prognostic factors for SPMs. The C-index of the nomogram for overall survival in the training and validation cohorts were 0.713 and 0.729, respectively. CONCLUSIONS We analyzed the clinical characteristics of SPMs and developed a precise prediction nomogram, with a good predictive performance. The nomogram we developed may help clinicians provide personalized decisions and clinical treatment for LT recipients.
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Affiliation(s)
| | | | | | - Peng Peng
- Center for Big Data Research in Health and Medicine, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Jinan, Shandong Province, China
| | | | | | | | - Lei Ren
- Department of General Surgery
| | - Fang Tang
- Center for Big Data Research in Health and Medicine, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Jinan, Shandong Province, China
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Su K, Shen Q, Tong J, Gu T, Xu K, Li H, Chi H, Liu Y, Li X, Wen L, Song Y, Guo Q, Chen J, Wu Z, Jiang Y, He K, Guo L, Han Y. Construction and validation of a nomogram for HBV-related hepatocellular carcinoma: A large, multicenter study. Ann Hepatol 2023; 28:101109. [PMID: 37100384 DOI: 10.1016/j.aohep.2023.101109] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Revised: 03/15/2023] [Accepted: 03/31/2023] [Indexed: 04/28/2023]
Abstract
INTRODUCTION AND OBJECTIVES We initiated this multicenter study to integrate important risk factors to create a nomogram for hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) for clinician decision-making. PATIENTS AND METHODS Between April 2011 and March 2022, 2281 HCC patients with an HBV-related diagnosis were included. All patients were randomly divided into two groups in a ratio of 7:3 (training cohort, n = 1597; validation cohort, n = 684). The nomogram was built in the training cohort via Cox regression model and validated in the validation cohort. RESULTS Multivariate Cox analyses revealed that the portal vein tumor thrombus, Child-Pugh class, tumor diameter, alanine aminotransferase level, tumor number, extrahepatic metastases, and therapy were independent predictive variables impacting overall survival. We constructed a new nomogram to predict 1-, 2-, and 3-year survival rates based on these factors. The nomogram-related receiver operating characteristics (ROC) curves indicated that the area under the curve (AUC) values were 0.809, 0.806, and 0.764 in predicting 1-, 2-, and 3-year survival rates, respectively. Furthermore, the calibration curves revealed good agreement between real measurements and nomogram predictions. The decision curve analyses (DCA) curves demonstrated excellent therapeutic application potential. In addition, stratified by risk scores, low-risk groups had longer median OS than medium-high-risk groups (p < 0.001). CONCLUSIONS The nomogram we constructed showed good performance in predicting the 1-year survival rate for HBV- related HCC.
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Affiliation(s)
- Ke Su
- Department of Oncology, The Affiliated Hospital of Southwest Medical University, 646000 Luzhou, China
| | - Qiuni Shen
- Department of Obstetrics and Gynecology, The Affiliated Hospital of Southwest Medical University, 646000 Luzhou, China
| | - Jian Tong
- Department of Spinal Surgery, No.1 Orthopedics Hospital of Chengdu, 610000 Chengdu, China
| | - Tao Gu
- Department of Oncology, The Affiliated Hospital of Southwest Medical University, 646000 Luzhou, China
| | - Ke Xu
- Department of Oncology, Chongqing General Hospital, 401147 Chongqing, China
| | - Han Li
- Department of Oncology, The Affiliated Hospital of Southwest Medical University, 646000 Luzhou, China
| | - Hao Chi
- Clinical Medical College, Southwest Medical University, 646000 Luzhou, China
| | - Yanlin Liu
- Department of Oncology, The Affiliated Hospital of Southwest Medical University, 646000 Luzhou, China
| | - Xueting Li
- Department of Oncology, 363 Hospital, 610041 Chengdu, China
| | - Lianbin Wen
- Department of Geriatric Cardiology, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, 610072 Chengdu, China
| | - Yanqiong Song
- Department of Radiotherapy, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, 610042 Chengdu, China
| | - Qulian Guo
- Department of Paediatrics, The Affiliated Hospital of Southwest Medical University, 646000 Luzhou, China
| | - Jiali Chen
- Department of Oncology, The Affiliated Hospital of Southwest Medical University, 646000 Luzhou, China
| | - Zhenying Wu
- Department of Oncology, The Affiliated Hospital of Southwest Medical University, 646000 Luzhou, China
| | - Yi Jiang
- Department of Oncology, The Affiliated Hospital of Southwest Medical University, 646000 Luzhou, China
| | - Kun He
- Clinical Research Institute, The Affiliated Hospital of Southwest Medical University, 646000 Luzhou, China.
| | - Lu Guo
- Department of Ophthalmology, The Affiliated Hospital of Southwest Medical University, 646000 Luzhou, China.
| | - Yunwei Han
- Department of Oncology, The Affiliated Hospital of Southwest Medical University, 646000 Luzhou, China.
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Chen J, Zhou H, Jin H, Liu K. A nomogram for individually predicting the overall survival in colonic adenocarcinoma patients presenting with perineural invasion: a population study based on SEER database. Front Oncol 2023; 13:1152931. [PMID: 37274243 PMCID: PMC10235682 DOI: 10.3389/fonc.2023.1152931] [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: 01/31/2023] [Accepted: 05/09/2023] [Indexed: 06/06/2023] Open
Abstract
Background Colonic adenocarcinoma, representing the predominant histological subtype of neoplasms in the colon, is commonly denoted as colon cancer. This study endeavors to develop and validate a nomogram model designed for predicting overall survival (OS) in patients with colon cancer, specifically those presenting with perineural invasion (PNI). Methods The Surveillance, Epidemiology, and End Results (SEER) database supplied pertinent data spanning from 2010 to 2015, which facilitated the randomization of patients into distinct training and validation cohorts at a 7:3 ratio. Both univariate and multivariate analyses were employed to construct a prognostic nomogram based on the training cohort. Subsequently, the nomogram's accuracy and efficacy were rigorously evaluated through the application of a concordance index (C-index), calibration plots, decision curve analysis (DCA), and receiver operating characteristic (ROC) curves. Results In the training cohorts, multivariable analysis identified age, grade, T-stage, N-stage, M-stage, chemotherapy, tumor size, carcinoembryonic antigen (CEA), marital status, and insurance as independent risk factors for OS, all with P-values less than 0.05. Subsequently, a new nomogram was constructed. The C-index of this nomogram was 0.765 (95% CI: 0.755-0.775), outperforming the American Joint Committee on Cancer (AJCC) TNM staging system's C-index of 0.686 (95% CI: 0.674-0.698). Calibration plots for 3- and 5-year OS demonstrated good consistency, while DCA for 3- and 5-year OS revealed excellent clinical utility in the training cohorts. Comparable outcomes were observed in the validation cohorts. Furthermore, we developed a risk stratification system, which facilitated better differentiation among three risk groups (low, intermediate, and high) in terms of OS for all patients. Conclusion In this study, we have devised a robust nomogram and risk stratification system to accurately predict OS in colon cancer patients exhibiting PNI. This innovative tool offers valuable guidance for informed clinical decision-making, thereby enhancing patient care and management in oncology practice.
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Zeng H, Su K, Chen X, Li X, Wen L, Song Y, Chen L, Li H, Guo L, Han Y. A propensity score matching study on survival benefits of radiotherapy in patients with inoperable hepatocellular carcinoma. Sci Rep 2023; 13:6879. [PMID: 37106014 PMCID: PMC10140032 DOI: 10.1038/s41598-023-34135-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Accepted: 04/25/2023] [Indexed: 04/29/2023] Open
Abstract
With the advancements in radiotherapy (RT) in recent years, several studies have shown that RT can significantly prolong the survival of patients with hepatocellular carcinoma (HCC). As a noninvasive treatment option, the application of RT for the treatment of HCC is garnering increasing attention. In this retrospective study, we included data from 13,878 patients with HCC from the Surveillance, Epidemiology, and End Results (SEER) database between 2000 and 2019 and 325 patients with HCC treated in three tertiary hospitals in China between 2015 and 2021. Patient data were divided into RT and non-RT groups based on whether the patients underwent RT. Propensity score matching analysis was performed to minimize the deviation between the RT and non-RT groups, and the Kaplan-Meier method, Cox proportional hazard model, and nomogram were used to assess the efficacy of undergoing RT. The median overall survival (mOS) of the RT group was significantly longer compared with that of the non-RT group for the SEER data (16 months versus 9 months, p < 0.01). Similarly, the survival benefit was more significant in the RT group than in the non-RT group at our hospitals (34.1 months versus 15.4 months, p < 0.01). Furthermore, multivariate Cox analysis revealed that factors, including tumor (T) stage, patient age, tumor grade, serum AFP level, and chemotherapy, also affected patient survival. Moreover, these factors were also used to construct a nomogram. Subgroup analysis of these factors showed that RT was effective in prolonging patient survival in different populations. RT significantly improves the survival time of patients with inoperable HCC, thereby providing a basis for selecting HCC patients who can benefit from RT.
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Affiliation(s)
- Hao Zeng
- Department of Oncology, The Affiliated Hospital of Southwest Medical University, No.25 Taiping Street, Luzhou, Sichuan Province, China
| | - Ke Su
- Department of Oncology, The Affiliated Hospital of Southwest Medical University, No.25 Taiping Street, Luzhou, Sichuan Province, China
| | - Xiaojing Chen
- Department of Oncology, The Affiliated Hospital of Southwest Medical University, No.25 Taiping Street, Luzhou, Sichuan Province, China
| | - Xueting Li
- Department of Oncology, 363 Hospital, Chengdu, China
| | - Lianbin Wen
- Department of Geriatric Cardiology, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, Chengdu, China
| | - Yanqiong Song
- Department of Radiotherapy, School of Medicine, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, Chengdu, China
| | - Lan Chen
- Department of Oncology and Hematology, The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University, Luzhou, China
| | - Han Li
- Department of Oncology, The Affiliated Hospital of Southwest Medical University, No.25 Taiping Street, Luzhou, Sichuan Province, China
| | - Lu Guo
- Department of Ophthalmology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Yunwei Han
- Department of Oncology, The Affiliated Hospital of Southwest Medical University, No.25 Taiping Street, Luzhou, Sichuan Province, China.
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Lv J, Wang C, Gao X, Yang J, Zhang X, Ye Y, Dong Q, Fu R, Sun H, Yan X, Zhao Y, Wang Y, Xu H, Yang Y. Development and validation of dynamic models to predict postdischarge mortality risk in patients with acute myocardial infarction: results from China Acute Myocardial Infarction Registry. BMJ Open 2023; 13:e069505. [PMID: 36990493 PMCID: PMC10069604 DOI: 10.1136/bmjopen-2022-069505] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/31/2023] Open
Abstract
OBJECTIVES The risk of adverse events and prognostic factors are changing in different time phases after acute myocardial infarction (AMI). The incidence of adverse events is considerable in the early period after AMI hospitalisation. Therefore, dynamic risk prediction is needed to guide postdischarge management of AMI. This study aimed to develop a dynamic risk prediction instrument for patients following AMI. DESIGN A retrospective analysis of a prospective cohort. SETTING 108 hospitals in China. PARTICIPANTS A total of 23 887 patients after AMI in the China Acute Myocardial Infarction Registry were included in this analysis. PRIMARY OUTCOME MEASURES All-cause mortality. RESULTS In multivariable analyses, age, prior stroke, heart rate, Killip class, left ventricular ejection fraction (LVEF), in-hospital percutaneous coronary intervention (PCI), recurrent myocardial ischaemia, recurrent myocardial infarction, heart failure (HF) during hospitalisation, antiplatelet therapy and statins at discharge were independently associated with 30-day mortality. Variables related to mortality between 30 days and 2 years included age, prior renal dysfunction, history of HF, AMI classification, heart rate, Killip class, haemoglobin, LVEF, in-hospital PCI, HF during hospitalisation, HF worsening within 30 days after discharge, antiplatelet therapy, β blocker and statin use within 30 days after discharge. The inclusion of adverse events and medications significantly improved the predictive performance of models without these indexes (likelihood ratio test p<0.0001). These two sets of predictors were used to establish dynamic prognostic nomograms for predicting mortality in patients with AMI. The C indexes of 30-day and 2-year prognostic nomograms were 0.85 (95% CI 0.83-0.88) and 0.83 (95% CI 0.81-0.84) in derivation cohort, and 0.79 (95% CI 0.71-0.86) and 0.81 (95% CI 0.79-0.84) in validation cohort, with satisfactory calibration. CONCLUSIONS We established dynamic risk prediction models incorporating adverse event and medications. The nomograms may be useful instruments to help prospective risk assessment and management of AMI. TRIAL REGISTRATION NUMBER NCT01874691.
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Affiliation(s)
- Junxing Lv
- Department of Cardiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Chuangshi Wang
- Medical Research and Biometrics Center, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xiaojin Gao
- Department of Cardiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jingang Yang
- Department of Cardiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xuan Zhang
- Department of Cardiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yunqing Ye
- Department of Cardiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Qiuting Dong
- Department of Cardiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Rui Fu
- Department of Cardiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Hui Sun
- Department of Cardiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xinxin Yan
- Department of Cardiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yanyan Zhao
- Medical Research and Biometrics Center, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yang Wang
- Medical Research and Biometrics Center, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Haiyan Xu
- Department of Cardiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yuejin Yang
- Department of Cardiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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Li D. Establishment and validation of a prognostic nomogram for patients with early-onset stage I–II colon cancer. World J Surg Oncol 2023; 21:103. [PMID: 36964525 PMCID: PMC10037885 DOI: 10.1186/s12957-023-02988-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Accepted: 03/18/2023] [Indexed: 03/26/2023] Open
Abstract
Background The aims of this study were to establish and validate a nomogram model for predicting the survival of patients with early-onset stage I–II colon cancer (CC). Methods Data of eligible patients enrolled from 2012 to 2015 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Patients were randomly allocated to training and validation groups in a 7:3 ratio. Significant prognostic factors were identified by univariate and multivariate analysis and a nomogram model constructed. The predictive performance of the nomogram was evaluated by the concordance index (C-index), calibration plots, and decision curve analysis. Results Our study cohort comprised 3528 early-onset CC patients with stage I–II disease, 2469 of whom were allocated to the training cohort and 1059 to the validation cohort. Race, age, marital status, tumor grade, tumor size, tumor stage (T stage), and chemotherapy were considered the significant predictor by univariate analysis. Race, marital status, and T stage were found to be independent prognostic factors by multivariate analysis. The C-indexes of the nomogram were 0.724 and 0.692 in the training and validation cohorts, respectively. Likewise, the calibration plots showed good agreement regarding the probability of 3- and 5-year observed and nomogram-predicted overall survival in the training group. Decision curve analysis showed that the nomogram model was clinically practical and effective. Moreover, applying the nomogram enabled dividing of the patients into two cohorts with different risk scores. The low-risk group thus created had a better survival than the high-risk group. Conclusions We developed and validated a meaningful prognostic nomogram model for patients with early-onset stage I–II CC that clinicians can use to make better decisions for individual patients.
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Affiliation(s)
- Dongdong Li
- grid.16821.3c0000 0004 0368 8293Department of General Surgery, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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The Efficacy of Surgical Resection versus Radiofrequency Ablation for the Treatment of Single Hepatocellular Carcinoma: A SEER-Based Study. Gastroenterol Res Pract 2023; 2023:1269504. [PMID: 36865983 PMCID: PMC9974275 DOI: 10.1155/2023/1269504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Accepted: 08/18/2022] [Indexed: 03/04/2023] Open
Abstract
Background There is controversy regarding whether patients with single hepatocellular carcinoma (HCC) should be offered radiofrequency ablation (RFA) as a first-line treatment option. Thus, this study compared overall survival after surgical resection (SR) and RFA for single HCC. Methods The Surveillance, Epidemiology, and End Results (SEER) database was used for this retrospective study. The study included 30- to 84-year-old patients diagnosed with HCC from 2000 to 2018. Selection bias was reduced via propensity score matching (PSM). The study compared the overall survival (OS) and cancer-specific survival (CSS) of patients with single HCC who were treated with SR and RFA. Results Before and after PSM, the median OS and median CSS were significantly longer in the SR group than in the RFA group (p < 0.05). In the subgroup analysis, the median OS and median CSS for male and female patients with male and female patients with tumor sizes <3, 3-5, and>5 cm, age at diagnosis between 60 and 84 years, and grades I-IV tumors were longer than in the SR group than in the RFA group (p < 0.05). Similar results were reported for patients who received chemotherapy (p < 0.05). Univariate and multivariate analyses revealed that compared with RFA, SR was an independent favorable factor for OS and CSS (p < 0.05) before and after PSM. Conclusion Patients with SR who had a single HCC showed higher OS and CSS compared with patients who received RFA. Hence, SR should be used as a first-line treatment in cases of single HCC.
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Long Z, Yi M, Qin Y, Ye Q, Che X, Wang S, Lei M. Development and validation of an ensemble machine-learning model for predicting early mortality among patients with bone metastases of hepatocellular carcinoma. Front Oncol 2023; 13:1144039. [PMID: 36890826 PMCID: PMC9986604 DOI: 10.3389/fonc.2023.1144039] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Accepted: 01/30/2023] [Indexed: 02/22/2023] Open
Abstract
Purpose Using an ensemble machine learning technique that incorporates the results of multiple machine learning algorithms, the study's objective is to build a reliable model to predict the early mortality among hepatocellular carcinoma (HCC) patients with bone metastases. Methods We extracted a cohort of 124,770 patients with a diagnosis of hepatocellular carcinoma from the Surveillance, Epidemiology, and End Results (SEER) program and enrolled a cohort of 1897 patients who were diagnosed as having bone metastases. Patients with a survival time of 3 months or less were considered to have had early death. To compare patients with and without early mortality, subgroup analysis was used. Patients were randomly divided into two groups: a training cohort (n = 1509, 80%) and an internal testing cohort (n = 388, 20%). In the training cohort, five machine learning techniques were employed to train and optimize models for predicting early mortality, and an ensemble machine learning technique was used to generate risk probability in a way of soft voting, and it was able to combine the results from the multiply machine learning algorithms. The study employed both internal and external validations, and the key performance indicators included the area under the receiver operating characteristic curve (AUROC), Brier score, and calibration curve. Patients from two tertiary hospitals were chosen as the external testing cohorts (n = 98). Feature importance and reclassification were both operated in the study. Results The early mortality was 55.5% (1052/1897). Eleven clinical characteristics were included as input features of machine learning models: sex (p = 0.019), marital status (p = 0.004), tumor stage (p = 0.025), node stage (p = 0.001), fibrosis score (p = 0.040), AFP level (p = 0.032), tumor size (p = 0.001), lung metastases (p < 0.001), cancer-directed surgery (p < 0.001), radiation (p < 0.001), and chemotherapy (p < 0.001). Application of the ensemble model in the internal testing population yielded an AUROC of 0.779 (95% confidence interval [CI]: 0.727-0.820), which was the largest AUROC among all models. Additionally, the ensemble model (0.191) outperformed the other five machine learning models in terms of Brier score. In terms of decision curves, the ensemble model also showed favorable clinical usefulness. External validation showed similar results; with an AUROC of 0.764 and Brier score of 0.195, the prediction performance was further improved after revision of the model. Feature importance demonstrated that the top three most crucial features were chemotherapy, radiation, and lung metastases based on the ensemble model. Reclassification of patients revealed a substantial difference in the two risk groups' actual probabilities of early mortality (74.38% vs. 31.35%, p < 0.001). Patients in the high-risk group had significantly shorter survival time than patients in the low-risk group (p < 0.001), according to the Kaplan-Meier survival curve. Conclusions The ensemble machine learning model exhibits promising prediction performance for early mortality among HCC patients with bone metastases. With the aid of routinely accessible clinical characteristics, this model can be a trustworthy prognostic tool to predict the early death of those patients and facilitate clinical decision-making.
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Affiliation(s)
- Ze Long
- Department of Orthopedics, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Min Yi
- Institute of Medical Information and Library, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yong Qin
- Department of Joint and Sports Medicine Surgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Qianwen Ye
- Department of Oncology, Hainan Hospital of People's Liberation Army (PLA) General Hospital, Sanya, China
| | - Xiaotong Che
- Department of Evaluation Office, Hainan Cancer Hospital, Haikou, China
| | - Shengjie Wang
- Department of Orthopaedic Surgery, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University, Shanghai, China
| | - Mingxing Lei
- Department of Orthopedic Surgery, Hainan Hospital of People's Liberation Army (PLA) General Hospital, Sanya, China.,Chinese People's Liberation Army (PLA) Medical School, Beijing, China
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Yang D, Su Y, Zhao F, Hu Y, Zhao K, Xiong X, Zhu M, Pei J, Ding Y. Low-grade hepatocellular carcinoma characteristics, a practical nomogram and risk stratification system: a SEER population-based study. Expert Rev Gastroenterol Hepatol 2022; 16:1115-1123. [PMID: 36412566 DOI: 10.1080/17474124.2022.2150610] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
BACKGROUND The purpose of this study is to establish a nomogram and risk stratification system to predict OS in patients with low-grade HCC. RESEARCH DESIGN AND METHODS Data were extracted from the SEER database. C-index, time-dependent AUCs, and calibration plots were used to evaluate the effective performance of the nomogram. NRI, IDI, and DCA curves were adopted to compare the clinical utility of nomogram with AJCC. RESULTS 3415 patients with low-grade HCC were available. The C-indices for the training and validation cohorts were 0.773 and 0.772. The time-dependent AUCs in the training cohort were 0.821, 0.817, and 0.846 at 1, 3 and 5 years. Calibration plots for 1-, 3- and 5-year OS showed good consistency between actual observations and that predicted by the nomogram. The values of NRI at 1, 3, and 5 years were 0.37, 0.66, and 0.64. The IDI values at 1, 3, and 5 years were 0.11, 0.16, and 0.23 (P< 0.001). DCA curves demonstrated that the nomogram showed better ability of predicting 1-, 3-, and 5-year OS probabilities than AJCC. CONCLUSIONS A nomogram and risk stratification system for predicting OS in patients with low-grade HCC were established and validated.
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Affiliation(s)
- Dashuai Yang
- Department of Hepatobiliary Surgery, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Yang Su
- Department of Gastrointestinal Surgery, Tongji Hospital, Tongji Medical College in Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Fangrui Zhao
- Department of Oncology, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Yong Hu
- Departments of Orthopedics, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Kailiang Zhao
- Department of Hepatobiliary Surgery, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Xiangyun Xiong
- Department of Hepatobiliary Surgery, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Mingqiang Zhu
- Department of Hepatobiliary Surgery, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Junpeng Pei
- Department of Hepatobiliary Surgery, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Youming Ding
- Department of Hepatobiliary Surgery, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
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Zhang H, Du X, Dong H, Xu W, Zhou P, Liu S, Qing X, Zhang Y, Yang M, Zhang Y. Risk factors and predictive nomograms for early death of patients with advanced hepatocellular carcinoma: a large retrospective study based on the SEER database. BMC Gastroenterol 2022; 22:348. [PMID: 35854221 PMCID: PMC9297630 DOI: 10.1186/s12876-022-02424-5] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Accepted: 07/12/2022] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Hepatocellular carcinoma (HCC) is a kind of tumor with high invasiveness, and patients with advanced HCC have a higher risk of early death. The aim of the present study was to identify the risk factors of early death in patients with advanced HCC and establish predictive nomograms. METHODS Death that occurred within 3 months of initial diagnosis is defined as early death. Patients diagnosed with stage IV HCC between 2010 and 2015 were collected from the Surveillance, Epidemiology, and End Results database for model establishment and verification. Univariable and multivariable logistic regression analyses were used to identify the risk factors. Predictive nomograms were constructed and an internal validation was performed. Decision curve analysis (DCA) was used to verify the true clinical application value of the models. RESULTS Of 6603 patients (57% age > 60, 81% male, 70% white, 46% married), 21% and 79% had stage IVA and IVB, respectively. On the multivariable analyses, risk factors for early deaths in patients with stage IVA were age, tumor size, histological grade, alpha-fetoprotein (AFP), fibrosis score, tumor stage (T stage), surgery, radiotherapy, and chemotherapy, and that in stage IVB were age, histological grade, AFP, T stage, node stage (N stage), bone metastasis, lung metastasis, surgery, radiotherapy, and chemotherapy. The areas under the curves (AUCs) were 0.830 (95% CI 0.809-0.851) and 0.789 (95% CI 0.768-0.810) in stage IVA and IVB, respectively. Nomograms comprising risk factors with the concordance indexes (C-indexes) were 0.820 (95% CI 0.799-0.841) in stage IVA and 0.785 (95% CI 0.764-0.0.806) in stage IVB for internal validation (Bootstrapping, 1000re-samplings). The calibration plots of the nomograms show that the predicted early death was consistent with the actual value. The results of the DCA analysis show that the nomograms had a good clinical application. CONCLUSION The nomograms can be beneficial for clinicians in identifying the risk factors for early death of patients with advanced HCC and predicting the probability of early death, so as to allow for individualized treatment plans to be accurately selected.
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Affiliation(s)
- Haidong Zhang
- Medical School, Southeast University, Nanjing, China
| | - Xuanlong Du
- Medical School, Southeast University, Nanjing, China
| | - Hui Dong
- Medical School, Southeast University, Nanjing, China
| | - Wenjing Xu
- Medical School, Southeast University, Nanjing, China
| | | | - Shiwei Liu
- Medical School, Southeast University, Nanjing, China
| | - Xin Qing
- Medical School, Southeast University, Nanjing, China
| | - Yu Zhang
- Medical School, Southeast University, Nanjing, China
| | - Meng Yang
- Department of Ultrasound, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
| | - Yewei Zhang
- Hepatopancreatobiliary Center, The Second Affiliated Hospital of Nanjing Medical University, Nanjing, China.
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Yang D, Su Y, Zhao F, Chen C, Zhao K, Xiong X, Ding Y. A Practical Nomogram and Risk Stratification System Predicting the Cancer-Specific Survival for Patients With Advanced Hepatocellular Carcinoma. Front Oncol 2022; 12:914192. [PMID: 35903694 PMCID: PMC9316183 DOI: 10.3389/fonc.2022.914192] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Accepted: 06/06/2022] [Indexed: 11/30/2022] Open
Abstract
Background Hepatocellular carcinoma (HCC) has the highest cancer-related mortality rate. This study aims to create a nomogram to predict the cancer-specific survival (CSS) in patients with advanced hepatocellular carcinoma. Methods Patients diagnosed with advanced HCC (AJCC stage III and IV) during 1975 to 2018 were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. Qualified patents were randomized into training cohort and validation cohort at a ratio of 7:3. The results of univariate and multivariate Cox regression analyses were used to construct the nomogram. Consistency index (C-index), area under the time-dependent receiver operating characteristic (ROC) curve [time-dependent area under the curve (AUC)], and calibration plots were used to identify and calibrate the nomogram. The net reclassification index (NRI), integrated discrimination improvement (IDI), and C-index, and decision curve analysis DCA were adopted to compare the nomogram’s clinical utility with the AJCC criteria. Results The 3,103 patients with advanced hepatocellular carcinoma were selected (the training cohort: 2,175 patients and the validation cohort: 928 patients). The C-index in both training cohort and validation cohort were greater than 0.7. The AUC for ROC in the training cohort was 0.781, 0.771, and 0.791 at 1, 2, and 3 years CSS, respectively. Calibration plots showed good consistency between actual observations and the 1-, 2-, and 3-year CSS predicted by the nomogram. The 1-, 2-, and 3-year NRI were 0.77, 0.46, and 0.48, respectively. The 1-, 2-, and 3-year IDI values were 0.16, 0.15, and 0.12 (P < 0.001), respectively. DCA curves in both the training and validation cohorts demonstrated that the nomogram showed better predicted 1-, 2-, and 3-year CSS probabilities than AJCC criteria. Conclusions This study established a practical nomogram for predicting CSS in patients with advanced HCC and a risk stratification system that provided an applicable tool for clinical management.
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Affiliation(s)
- Dashuai Yang
- Department of Hepatobiliary Surgery, Renmin Hospital of Wuhan University, Wuhan, China
| | - Yang Su
- Department of Hepatobiliary Surgery, Renmin Hospital of Wuhan University, Wuhan, China
| | - Fangrui Zhao
- Department of Oncology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Chen Chen
- Department of Hepatobiliary Surgery, Renmin Hospital of Wuhan University, Wuhan, China
- *Correspondence: Youming Ding, ; Chen Chen,
| | - Kailiang Zhao
- Department of Hepatobiliary Surgery, Renmin Hospital of Wuhan University, Wuhan, China
| | - Xiangyun Xiong
- Department of Hepatobiliary Surgery, Renmin Hospital of Wuhan University, Wuhan, China
| | - Youming Ding
- Department of Hepatobiliary Surgery, Renmin Hospital of Wuhan University, Wuhan, China
- *Correspondence: Youming Ding, ; Chen Chen,
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Wen C, Tang J, Luo H. Development and Validation of a Nomogram to Predict Cancer-Specific Survival for Middle-Aged Patients With Early-Stage Hepatocellular Carcinoma. Front Public Health 2022; 10:848716. [PMID: 35296046 PMCID: PMC8918547 DOI: 10.3389/fpubh.2022.848716] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Accepted: 02/07/2022] [Indexed: 01/09/2023] Open
Abstract
Background Hepatocellular carcinoma is a common cause of death in middle-aged patients. We aimed to construct a new nomogram to predict cancer-specific survival (CSS) in middle-aged patients with hepatocellular carcinoma at an early stage. Method We collected clinicopathological information on early middle-aged patients with hepatocellular carcinoma from the SEER database. Univariate and multivariate Cox regression models were used to screen the independent risk factors for prognosis. These risk factors were used to construct predictions of CSS in patients with hepatocellular carcinoma. Consistency index (C- index), calibration curve, area under the receiver operating curve (AUC) were used. A decision analysis curve (DCA) was used to evaluate the clinical utility of the predictive model. Results A total of 6,286 patients with hepatocellular carcinoma in early middle age were enrolled. Univariate and multivariate Cox regression analysis showed that sex, marriage, race, histological tumor grade, T stage, surgery, chemotherapy, AFP, and tumor size were independent risk factors for prognosis. All independent risk factors were included in the nomogram to predict CSS at 1-, 3-, and 5-years in early middle age patients with hepatocellular carcinoma. In the training cohort and validation cohort, the C-index of the prediction model was 0.728 (95%CI: 0.716–0.740) and 0.733 (95%CI: 0.715–0.751), respectively. The calibration curve showed that the predicted value of the prediction model is highly consistent with the observed value. AUC also suggested that the model has good discrimination. DCA suggested that the nomogram had better predictive power than T staging. Conclusion We constructed a new nomogram to predict CSS in middle-aged patients with early-stage hepatocellular carcinoma. This prediction model has good accuracy and reliability, which can help patients and doctors to judge prognosis and make clinical decisions.
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Affiliation(s)
- Chong Wen
- General Surgery Center, The General Hospital of Western Theater, Chengdu, China
- College of Medicine, Southwest Jiaotong University, Chengdu, China
| | - Jie Tang
- Department of Biostatistics and Epidemiology, School of Public Health, Shenyang Medical College, Shenyang, China
| | - Hao Luo
- General Surgery Center, The General Hospital of Western Theater, Chengdu, China
- *Correspondence: Hao Luo
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He T, Chen T, Liu X, Zhang B, Yue S, Cao J, Zhang G. A Web-Based Prediction Model for Cancer-Specific Survival of Elderly Patients With Early Hepatocellular Carcinoma: A Study Based on SEER Database. Front Public Health 2022; 9:789026. [PMID: 35096742 PMCID: PMC8792840 DOI: 10.3389/fpubh.2021.789026] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Accepted: 12/14/2021] [Indexed: 12/29/2022] Open
Abstract
Background: Primary liver cancer is a common malignant tumor primarily represented by hepatocellular carcinoma (HCC). The number of elderly patients with early HCC is increasing, and older age is related to a worse prognosis. However, an accurate predictive model for the prognosis of these patients is still lacking. Methods: Data of eligible elderly patients with early HCC in Surveillance, Epidemiology, and End Results database from 2010 to 2016 were downloaded. Patients from 2010 to 2015 were randomly assigned to the training cohort (n = 1093) and validation cohort (n = 461). Patients' data in 2016 (n = 431) was used for external validation. Independent prognostic factors were obtained using univariate and multivariate analyses. Based on these factors, a cancer-specific survival (CSS) nomogram was constructed. The predictive performance and clinical practicability of our nomogram were validated. According to the risk scores of our nomogram, patients were divided into low-, intermediate-, and high-risk groups. A survival analysis was performed using Kaplan–Meier curves and log-rank tests. Results: Age, race, T stage, histological grade, surgery, radiotherapy, and chemotherapy were independent predictors for CSS and thus were included in our nomogram. In the training cohort and validation cohort, the concordance indices (C-indices) of our nomogram were 0.739 (95% CI: 0.714–0.764) and 0.756 (95% CI: 0.719–0.793), respectively. The 1-, 3-, and 5-year areas under receiver operating characteristic curves (AUCs) showed similar results. Calibration curves revealed high consistency between observations and predictions. In external validation cohort, C-index (0.802, 95%CI: 0.778–0.826) and calibration curves also revealed high consistency between observations and predictions. Compared with the TNM stage, nomogram-related decision curve analysis (DCA) curves indicated better clinical practicability. Kaplan–Meier curves revealed that CSS significantly differed among the three different risk groups. In addition, an online prediction tool for CSS was developed. Conclusions: A web-based prediction model for CSS of elderly patients with early HCC was constructed and validated, and it may be helpful for the prognostic evaluation, therapeutic strategy selection, and follow-up management of these patients.
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Affiliation(s)
- Taiyu He
- Key Laboratory of Molecular Biology for Infectious Diseases, Ministry of Education, Chongqing Medical University, Chongqing, China
- Institute for Viral Hepatitis, Chongqing Medical University, Chongqing, China
- Department of Infectious Diseases, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Tianyao Chen
- College of Medical Informatics, Chongqing Medical University, Chongqing, China
| | - Xiaozhu Liu
- Department of Cardiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Biqiong Zhang
- Key Laboratory of Molecular Biology for Infectious Diseases, Ministry of Education, Chongqing Medical University, Chongqing, China
- Institute for Viral Hepatitis, Chongqing Medical University, Chongqing, China
- Department of Infectious Diseases, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Song Yue
- Department of Gynecology and Obstetrics, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Junyi Cao
- Department of Record Room, Zigong First People's Hospital, Zigong, China
| | - Gaoli Zhang
- Key Laboratory of Molecular Biology for Infectious Diseases, Ministry of Education, Chongqing Medical University, Chongqing, China
- Institute for Viral Hepatitis, Chongqing Medical University, Chongqing, China
- Department of Infectious Diseases, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
- *Correspondence: Gaoli Zhang
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Liu YY, Xu BS, Pan QZ, Weng DS, Zhang X, Peng RQ. New nomograms to predict overall and cancer-specific survival of angiosarcoma. Cancer Med 2021; 11:74-85. [PMID: 34786885 PMCID: PMC8704180 DOI: 10.1002/cam4.4425] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2021] [Revised: 10/12/2021] [Accepted: 10/14/2021] [Indexed: 12/25/2022] Open
Abstract
Objective This study was designed to establish and validate promising and reliable nomograms for predicting the survival of angiosarcoma (AS) patients. Methods The Surveillance, Epidemiology, and End Results database was queried to collect the clinical information of 785 AS patients between 2004 and 2015. Data were split into a training cohort (n = 549) and a validation cohort (n = 236) without any preference. Univariate Cox and multivariate Cox regression analyses were performed to analyze the clinical parameters. Independent prognostic factors were then identified. Two nomograms were constructed to predict overall survival (OS) and cancer‐specific survival (CSS) at 3 and 5 years. Finally, the models were evaluated using concordance indices (C‐indices), calibration plots, and decision curve analysis (DCA). Results Based on the inclusion and exclusion criteria, 785 individuals were included in this analysis. Univariate and multivariate Cox regression analyses revealed that age, tumor size, and stage were prognostic factors independently associated with the OS of AS. Tumor site, tumor size, and stage were associated with the CSS of AS. Based on the statistical results and clinical significance of variables, nomograms were built. The nomograms for OS and CSS had C‐indices of 0.666 and 0.654, respectively. The calibration curves showed good agreement between the predictive values and the actual values. DCA also indicated that the nomograms were clinically useful. Conclusion We established nomograms with good predictive ability that could provide clinicians with better predictions about the clinical outcomes of AS patients.
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Affiliation(s)
- Yuan-Yuan Liu
- Melanoma and Sarcoma Medical Oncology Unit, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China.,State Key Laboratory of Oncology in South China, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Bu-Shu Xu
- Melanoma and Sarcoma Medical Oncology Unit, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China.,State Key Laboratory of Oncology in South China, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Qiu-Zhong Pan
- Melanoma and Sarcoma Medical Oncology Unit, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China.,State Key Laboratory of Oncology in South China, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - De-Sheng Weng
- Melanoma and Sarcoma Medical Oncology Unit, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China.,State Key Laboratory of Oncology in South China, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Xing Zhang
- Melanoma and Sarcoma Medical Oncology Unit, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China.,State Key Laboratory of Oncology in South China, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Rui-Qing Peng
- Melanoma and Sarcoma Medical Oncology Unit, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China.,State Key Laboratory of Oncology in South China, Sun Yat-Sen University Cancer Center, Guangzhou, China
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Li Z, Wei J, Gan X, Song M, Zhang Y, Cao H, Jin Y, Yang J. Construction, validation and, visualization of a web-based nomogram for predicting the overall survival and cancer-specific survival of leiomyosarcoma patients with lung metastasis. J Thorac Dis 2021; 13:3076-3092. [PMID: 34164199 PMCID: PMC8182497 DOI: 10.21037/jtd-21-598] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Background This study sought to assess the prognostic factors for leiomyosarcoma (LMS) patients with lung metastasis and construct web-based nomograms to predict overall survival (OS) and cancer-specific survival (CSS). Method Patients diagnosed with LMS combined with lung metastasis between 2010 and 2016 were identified in the Surveillance, Epidemiology, and End Results (SEER) database. The patients were randomly divided into a training set and a testing set. The X-tile analysis provides the best age and tumor size cut-off point, and changes continuous variables into categorical variables. The independent prognostic factors were determined by Cox regression analysis, and 2 nomograms were established. Receiver operating characteristic curves and calibration curves were used to evaluate the nomograms. Based on the nomograms, 2 web-based nomograms were established. Results Two hundred and twenty-eight cases were included in the OS nomogram construction, and were randomly divided into a training set (n=160) and a validation set (n=68). Age, T stage, bone metastasis, surgery, chemotherapy, marital status, tumor size, and tumor site were found to be correlated with OS. One hundred and eighty-three cases were enrolled in the CSS nomogram construction, and randomly divided into a training set (n=129) and a validation set (n=54). Age, bone metastasis, surgery, chemotherapy, tumor size, and tumor site were found to be correlated with CSS. Two nomograms were established to predict OS and CSS. In the training set, the areas under the curve of the nomogram for predicting 1-, 2-, and 3-year OS were 0.783, 0.830, and 0.832, respectively, and those for predicting 1-, 2-, and 3-year CSS were 0.889, 0.777, and 0.884, respectively. Two web-based nomograms were established to predict OS (https://wenn23.shinyapps.io/lmslmosapp/), and CSS (https://wenn23.shinyapps.io/lmslmcssapp/). Conclusion The developed web-based nomogram is a useful tool for accurately analyzing the prognosis of LMS patients with lung metastasis, and could help clinical doctors to make personalized clinical decisions.
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Affiliation(s)
- Zhehong Li
- Postgraduate Medical School, Chengde Medical College, Chengde, China
| | - Junqiang Wei
- Department of Bone and Soft Tissue Tumor, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.,National Clinical Research Centre for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Centre for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.,Department of Orthopedics, Affiliated Hospital of Chengde Medical College, Chengde, China
| | - Xintian Gan
- Postgraduate Medical School, Chengde Medical College, Chengde, China
| | - Mingze Song
- Postgraduate Medical School, Chengde Medical College, Chengde, China
| | - Yafang Zhang
- Postgraduate Medical School, Chengde Medical College, Chengde, China
| | - Haiying Cao
- Department of Orthopedics, Affiliated Hospital of Chengde Medical College, Chengde, China
| | - Yu Jin
- Department of Orthopedics, Affiliated Hospital of Chengde Medical College, Chengde, China
| | - Jilong Yang
- Department of Bone and Soft Tissue Tumor, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.,National Clinical Research Centre for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Centre for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
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Li X, Xu H, Yan L, Gao J, Zhu L. 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. Front Oncol 2021; 11:670644. [PMID: 33959514 PMCID: PMC8093627 DOI: 10.3389/fonc.2021.670644] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Accepted: 03/30/2021] [Indexed: 12/18/2022] Open
Abstract
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.
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Affiliation(s)
- Xianli Li
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Haoya Xu
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Limei Yan
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Jian Gao
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Liancheng Zhu
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China
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