A Practical Nomogram and Risk Stratification System Predicting the Cancer-Specific Survival for Patients With Advanced Hepatocellular Carcinoma

Yang, Dashuai; Su, Yang; Zhao, Fangrui; Chen, Chen; Zhao, Kailiang; Xiong, Xiangyun; Ding, Youming

doi:10.3389/fonc.2022.914192

Cited by 6 publications

(2 citation statements)

References 29 publications

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“…[26,27] A number of previous studies have shown that age, gender and marital status are all prognostic factors affecting survival in patients with hepatocellular carcinoma. [28][29][30] In our study model, however, these factors were not associated with early death in patients with metastatic HCC.…”

Section: Discussioncontrasting

confidence: 64%

The novel predictive nomograms for early death in metastatic hepatocellular carcinoma: A large cohort study

Wang,

Ge,

Cai

2024

Medicine

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Metastatic hepatocellular carcinoma (HCC) is an aggressive disease which usually have a poor prognosis. Early mortality and risk factors in patients with metastatic HCC are poorly understood. Our study sought to identify associated risk factors and develop the nomograms for predicting early death in metastatic HCC patients. The patients diagnosed with metastatic HCC were chosen from the surveillance, epidemiology, and end results database between 2010 and 2015. To identify significant independent risk factors for early death, both univariate and multivariate logistic regression models were used. We constructed a pragmatic nomogram and then evaluated by using receiver operating characteristic curves, calibration plots, and decision curve analysis. The prediction model included 2587 patients with metastatic HCC. Among them, 1550 experienced early death (died within 3 months of initial diagnosis) and 1437 died from cancer-specific causes. Multivariate logistic regression analysis found that grade, surgery, radiation, chemotherapy, alpha-fetoprotein levels, and lung metastasis were independent risk factors for both all-cause early death and cancer-specific early death. In addition, bone metastasis were independent risk factors for all-cause early death, T-stage and brain metastasis were also independent risk factors for cancer-specific early death. Then we used the relevant risk factors to developed the practical nomograms of all-cause and cancer-specific early deaths. The nomograms demonstrated good predictive power and clinical utility under receiver operating characteristic curves and decision curve analysis. We developed 2 novel comprehensive nomograms to predict early death among metastatic HCC patients. Nomograms may help oncologists develop better treatment strategies and implementation of individualized treatment plans.

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Section: Discussioncontrasting

confidence: 64%

The novel predictive nomograms for early death in metastatic hepatocellular carcinoma: A large cohort study

Wang,

Ge,

Cai

2024

Medicine

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“…We used univariate and multivariate Cox regression analyses to evaluate the independent prognostic value of the risk score. In the training set, a nomogram was developed based on independent risk factors, and risk scores were calculated for each patient [ 18 , 19 ]. Training and validation sets were used to estimate the accuracy of the nomogram model.…”

Section: Methodsmentioning

confidence: 99%

A nomogram for predicting prognosis of multiple myeloma patients based on a ubiquitin-proteasome gene signature

Liu

Sun

et al. 2022

Aging

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Background: Multiple myeloma (MM) is a malignant hematopoietic disease that is usually incurable. However, the ubiquitin-proteasome system (UPS) genes have not yet been established as a prognostic predictor for MM, despite their potential applications in other cancers. Methods: RNA sequencing data and corresponding clinical information were acquired from Multiple Myeloma Research Foundation (MMRF)-COMMPASS and served as a training set (n=787). Validation of the prediction signature were conducted by the Gene Expression Omnibus (GEO) databases (n=1040). To develop a prognostic signature for overall survival (OS), least absolute shrinkage and selection operator regressions, along with Cox regressions, were used. Results: A six-gene signature, including KCTD12, SIAH1, TRIM58, TRIM47, UBE2S, and UBE2T, was established. Kaplan-Meier survival analysis of the training and validation cohorts revealed that patients with high-risk conditions had a significantly worse prognosis than those with low-risk conditions. Furthermore, UPS-related signature is associated with a positive immune response. For predicting survival, a simple to use nomogram and the corresponding web-based calculator (https://jiangyanxiamm.shinyapps.io/MMprognosis/) were built based on the UPS signature and its clinical features. Analyses of calibration plots and decision curves showed clinical utility for both training and validation datasets. Conclusions: As a result of these results, we established a genetic signature for MM based on UPS. This genetic signature could contribute to improving individualized survival prediction, thereby facilitating clinical decisions in patients with MM.

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Development and evaluation of nomograms and risk stratification systems to predict the overall survival and cancer-specific survival of patients with hepatocellular carcinoma

Kang,

Liu,

et al. 2024

Clin Exp Med

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Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer, and patients with HCC have a poor prognosis and low survival rates. Establishing a prognostic nomogram is important for predicting the survival of patients with HCC, as it helps to improve the patient’s prognosis. This study aimed to develop and evaluate nomograms and risk stratification to predict overall survival (OS) and cancer-specific survival (CSS) in HCC patients. Data from 10,302 patients with initially diagnosed HCC were extracted from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2017. Patients were randomly divided into the training and validation set. Kaplan–Meier survival, LASSO regression, and Cox regression analysis were conducted to select the predictors of OS. Competing risk analysis, LASSO regression, and Cox regression analysis were conducted to select the predictors of CSS. The validation of the nomograms was performed using the concordance index (C-index), the Akaike information criterion (AIC), the Bayesian information criterion (BIC), Net Reclassification Index (NRI), Discrimination Improvement (IDI), the receiver operating characteristic (ROC) curve, calibration curves, and decision curve analyses (DCAs). The results indicated that factors including age, grade, T stage, N stage, M stage, surgery, surgery to lymph node (LN), Alpha-Fetal Protein (AFP), and tumor size were independent predictors of OS, whereas grade, T stage, surgery, AFP, tumor size, and distant lymph node metastasis were independent predictors of CSS. Based on these factors, predictive models were built and virtualized by nomograms. The C-index for predicting 1-, 3-, and 5-year OS were 0.788, 0.792, and 0.790. The C-index for predicting 1-, 3-, and 5-year CSS were 0.803, 0.808, and 0.806. AIC, BIC, NRI, and IDI suggested that nomograms had an excellent predictive performance with no significant overfitting. The calibration curves showed good consistency of OS and CSS between the actual observation and nomograms prediction, and the DCA showed great clinical usefulness of the nomograms. The risk stratification of OS and CSS was built that could perfectly classify HCC patients into three risk groups. Our study developed nomograms and a corresponding risk stratification system predicting the OS and CSS of HCC patients. These tools can assist in patient counseling and guiding treatment decision making.

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A Practical Nomogram and Risk Stratification System Predicting the Cancer-Specific Survival for Patients With Advanced Hepatocellular Carcinoma

Cited by 6 publications

References 29 publications

The novel predictive nomograms for early death in metastatic hepatocellular carcinoma: A large cohort study

The novel predictive nomograms for early death in metastatic hepatocellular carcinoma: A large cohort study

A nomogram for predicting prognosis of multiple myeloma patients based on a ubiquitin-proteasome gene signature

Development and evaluation of nomograms and risk stratification systems to predict the overall survival and cancer-specific survival of patients with hepatocellular carcinoma

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