Prediction Model and Nomogram of Early Recurrence of Hepatocellular Carcinoma after Radiofrequency Ablation Based on Logistic Regression Analysis

Ni, ZiHao; Wu, Bolin; Li, Meng; Han, Xue; Hao, XiaoWen; Zhang, Yue; Cheng, Wen; Guo, CunLi

doi:10.1016/j.ultrasmedbio.2022.04.217

Cited by 8 publications

(3 citation statements)

References 46 publications

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“…Another highlight of this study is the combination of Lasso regression and Cox regression analysis. Cancer research has been a focus of the medical field globally, and to better understand and predict the occurrence of cancer as well as to understand the prognosis of patients, many research teams have established cancer risk scoring systems that combine demographic and clinical data (15)(16)(17)(18)(19). However, most of the parameter screening within these still relies on univariate and multivariate analyses, and these traditional methods, when dealing with high-dimensional data, especially when faced with the problem of multicollinearity problems among multiple variables, clearly have their inherent limitations.…”

Section: Introductionmentioning

confidence: 99%

Nomogram built based on machine learning to predict recurrence in early-stage hepatocellular carcinoma patients treated with ablation

Zhang,

Sheng,

Qiao

et al. 2024

Front. Oncol.

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IntroductionTo analyze the risk factors affecting recurrence in early-stage hepatocellular carcinoma (HCC) patients treated with ablation and then establish a nomogram to provide a clear and accessible representation of the patients’ recurrence risk.MethodsCollect demographic and clinical data of 898 early-stage HCC patients who underwent ablation treatment at Beijing You’an Hospital, affiliated with Capital Medical University from January 2014 to December 2022. Patients admitted from 2014 to 2018 were included in the training cohort, while 2019 to 2022 were in the validation cohort. Lasso and Cox regression was used to screen independent risk factors for HCC patients recurrence, and a nomogram was then constructed based on the screened factors.ResultsAge, gender, Barcelona Clinic Liver Cancer (BCLC) stage, tumor size, globulin (Glob) and γ-glutamyl transpeptidase (γ-GT) were finally incorporated in the nomogram for predicting the recurrence-free survival (RFS) of patients. We further confirmed that the nomogram has optimal discrimination, consistency and clinical utility by the C-index, Receiver Operating Characteristic Curve (ROC), calibration curve and Decision Curve Analysis (DCA). Moreover, we divided the patients into different risk groups and found that the nomogram can effectively identify the high recurrence risk patients by the Kaplan-Meier curves.ConclusionThis study developed a nomogram using Lasso-Cox regression to predict RFS in early-stage HCC patients following ablation, aiding clinicians in identifying high-risk groups for personalized follow-up treatments.

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Section: Introductionmentioning

confidence: 99%

Nomogram built based on machine learning to predict recurrence in early-stage hepatocellular carcinoma patients treated with ablation

Zhang,

Sheng,

Qiao

et al. 2024

Front. Oncol.

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“…[ 28 , 29 ]. Nomogram prediction models have been widely used in the prognosis and risk assessment of various tumors such as gastric, colorectal, prostate, breast, and liver cancers [ 30 , 31 , 32 , 33 , 34 ]; its visual interface and web-based model facilitate practical use by clinicians and patients, facilitating individualized patient treatment [ 35 ]. Currently, no nomogram model exists to anticipate the risk and prognosis in PCLM patients.…”

Section: Introductionmentioning

confidence: 99%

Nomograms for Predicting the Risk and Prognosis of Liver Metastases in Pancreatic Cancer: A Population-Based Analysis

Shi

Zhou

et al. 2023

JPM

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The liver is the most prevalent location of distant metastasis for pancreatic cancer (PC), which is highly aggressive. Pancreatic cancer with liver metastases (PCLM) patients have a poor prognosis. Furthermore, there is a lack of effective predictive tools for anticipating the diagnostic and prognostic techniques that are needed for the PCLM patients in current clinical work. Therefore, we aimed to construct two nomogram predictive models incorporating common clinical indicators to anticipate the risk factors and prognosis for PCLM patients. Clinicopathological information on pancreatic cancer that referred to patients who had been diagnosed between the years of 2004 and 2015 was extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate logistic regression analyses and a Cox regression analysis were utilized to recognize the independent risk variables and independent predictive factors for the PCLM patients, respectively. Using the independent risk as well as prognostic factors derived from the multivariate regression analysis, we constructed two novel nomogram models for predicting the risk and prognosis of PCLM patients. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve, the consistency index (C-index), and the calibration curve were then utilized to establish the accuracy of the nomograms’ predictions and their discriminability between groups. Using a decision curve analysis (DCA), the clinical values of the two predictors were examined. Finally, we utilized Kaplan–Meier curves to examine the effects of different factors on the prognostic overall survival (OS). As many as 1898 PCLM patients were screened. The patient’s sex, primary site, histopathological type, grade, T stage, N stage, bone metastases, lung metastases, tumor size, surgical resection, radiotherapy, and chemotherapy were all found to be independent risks variables for PCLM in a multivariate logistic regression analysis. Using a multivariate Cox regression analysis, we discovered that age, histopathological type, grade, bone metastasis, lung metastasis, tumor size, and surgery were all independent prognostic variables for PCLM. According to these factors, two nomogram models were developed to anticipate the prognostic OS as well as the risk variables for the progression of PCLM in PCLM patients, and a web-based version of the prediction model was constructed. The diagnostic nomogram model had a C-index of 0.884 (95% CI: 0.876–0.892); the prognostic model had a C-index of 0.686 (95% CI: 0.648–0.722) in the training cohort and a C-index of 0.705 (95% CI: 0.647–0.758) in the validation cohort. Subsequent AUC, calibration curve, and DCA analyses revealed that the risk and predictive model of PCLM had high accuracy as well as efficacy for clinical application. The nomograms constructed can effectively predict risk and prognosis factors in PCLM patients, which facilitates personalized clinical decision-making for patients.

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“…Logistic regression model is a generalized linear regression analysis model, which has good prediction effect and applicability, and is widely used in disease prediction models. Ni et al constructed the prediction model of early recurrence of liver cancer after radiofrequency ablation by logistic regression analysis, and screened the risk factors leading to early recurrence of liver cancer after radiofrequency ablation [13]. Jawa et al used logistic regression analysis model to determine the in uence of family isolation on individual psychological stability during COVID-19 and its relationship with various factors [14].…”

Section: Introductionmentioning

confidence: 99%

Divergent indicator changes in different exercise states in non-hypertensive individuals and patients with hypertension

Zhang¹,

Xu²,

Gao³

et al. 2023

Preprint

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Background Hypertension is a kind of clinical syndrome, characterized by the increase of systemic arterial pressure. There is a lack of identifiable triggers and predictors of hypertensive disease in response to treatment at rest and during exercise. In this study, a mathematical model was used to screen and compare the indicators and related changes at rest and during exercise between normotensive and hypertensive individuals. Methods Blood pressure and ultrasound-related indicators, blood biochemical indicators and metabolic compounds were collected and logistic regression model and Principal component analysis (PCA) were used to explore the differences of indexes at rest and in different exercise states in healthy and hypertensive patients. An indicator change map for hypertension is established. Results The results reveal that hypertension is not only related to oxidative stress, inflammatory reaction and fatty acid oxidation, but also involves various amino acid metabolism. The defined mathematical models and indicators changes during exercise might be helpful for early screening of hypertension and future studies are needed to explore their value on prevention and control of hypertension. Conclusion The research shows that the main regulation indicators at different exercise states differ significantly in the normal group and the hypertensive group. The key indicators of the normal group are blood pressure and ultrasound related indicators, while those of the hypertensive group are metabolites related to lactic acid metabolism, glycolysis, aerobic oxidation and lipid metabolism.

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Prediction Model and Nomogram of Early Recurrence of Hepatocellular Carcinoma after Radiofrequency Ablation Based on Logistic Regression Analysis

Cited by 8 publications

References 46 publications

Nomogram built based on machine learning to predict recurrence in early-stage hepatocellular carcinoma patients treated with ablation

Nomogram built based on machine learning to predict recurrence in early-stage hepatocellular carcinoma patients treated with ablation

Nomograms for Predicting the Risk and Prognosis of Liver Metastases in Pancreatic Cancer: A Population-Based Analysis

Divergent indicator changes in different exercise states in non-hypertensive individuals and patients with hypertension

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