Inversed albumin-to-globulin ratio and underlying liver disease severity as a prognostic factor for survival in hepatocellular carcinoma patients undergoing transarterial chemoembolization

Li, Jinlong; Li, Zhi; Hao, Shirui; Wang, Jitao; Chen, Wei; Dai, Shoufang; Hou, Zhenguo; Chen, Borun; Zhang, Yewei; Liu, Dengxiang

doi:10.5152/dir.2022.211166

Cited by 5 publications

(4 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Various proinflammatory proteins, including immunoglobulins, C-reactive protein, α2 macroglobulin, and fibrinogen are globulins ( 32 , 33 ). Since human immunoglobulins are mainly metabolized by the liver, patients with severe hepatic dysfunction have a reduced ability to clear immunoglobulins, causing hyperglobulinemia ( 34 , 35 ). Bile acid synthesis occurs in liver cells and is the end product of cholesterol metabolism ( 36 ).…”

Section: Discussionmentioning

confidence: 99%

Construction and validation of a machine learning-based nomogram to predict the prognosis of HBV associated hepatocellular carcinoma patients with high levels of hepatitis B surface antigen in primary local treatment: a multicenter study

Xiong,

Qiao,

Wang

et al. 2024

Front. Immunol.

View full text Add to dashboard Cite

BackgroundHepatitis B surface antigen (HBsAg) clearance is associated with improved long-term outcomes and reduced risk of complications. The aim of our study was to identify the effects of levels of HBsAg in HCC patients undergoing TACE and sequential ablation. In addition, we created a nomogram to predict the prognosis of HCC patients with high levels of HBsAg (≥1000U/L) after local treatment.MethodThis study retrospectively evaluated 1008 HBV-HCC patients who underwent TACE combined with ablation at Beijing Youan Hospital and Beijing Ditan Hospital from January 2014 to December 2021, including 334 patients with low HBsAg levels and 674 patients with high HBsAg levels. The high HBsAg group was divided into the training cohort (N=385), internal validation cohort (N=168), and external validation cohort (N=121). The clinical and pathological features of patients were collected, and independent risk factors were identified using Lasso-Cox regression analysis for developing a nomogram. The performance of the nomogram was evaluated by C-index, receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) curves in the training and validation cohorts. Patients were classified into high-risk and low-risk groups based on the risk scores of the nomogram.ResultAfter PSM, mRFS was 28.4 months (22.1-34.7 months) and 21.9 months (18.5-25.4 months) in the low HBsAg level and high HBsAg level groups (P<0.001). The content of the nomogram includes age, BCLC stage, tumor size, globulin, GGT, and bile acids. The C-index (0.682, 0.666, and 0.740) and 1-, 3-, and 5-year AUCs of the training, internal validation, and external validation cohorts proved good discrimination of the nomogram. Calibration curves and DCA curves suggested accuracy and net clinical benefit rates. The nomogram enabled to classification of patients with high HBsAg levels into low-risk and high-risk groups according to the risk of recurrence. There was a statistically significant difference in RFS between the two groups in the training, internal validation, and external validation cohorts (P<0.001).ConclusionHigh levels of HBsAg were associated with tumor progression. The nomogram developed and validated in the study had good predictive ability for patients with high HBsAg levels.

show abstract

Section: Discussionmentioning

confidence: 99%

Construction and validation of a machine learning-based nomogram to predict the prognosis of HBV associated hepatocellular carcinoma patients with high levels of hepatitis B surface antigen in primary local treatment: a multicenter study

Xiong,

Qiao,

Wang

et al. 2024

Front. Immunol.

View full text Add to dashboard Cite

show abstract

“…Inflammation can alter tumor cell biology and disrupt immune function, leading to poor clinical prognosis in patients with malignant tumors. 29 , 30 …”

Section: Discussionmentioning

confidence: 99%

“…Inflammation can alter tumor cell biology and disrupt immune function, leading to poor clinical prognosis in patients with malignant tumors. 29,30 CECT is a means of improving diagnostic accuracy by focusing on suspicious areas found based on CT scans after intravenous injection of contrast agent. CECT has significant advantages over NECT in the following three situations.…”

Section: Discussionmentioning

confidence: 99%

Development and Validation of a Nomogram to Predict the Recurrence of HCC Patients Undergoing CECT After Ablation

Qiao,

Fan,

Wang

et al. 2024

JHC

View full text Add to dashboard Cite

Purpose We first aimed to compare the prognostic difference between the application of Contrast-enhanced computed tomography (CECT) and Non-enhanced computed tomography (NECT) in hepatocellular carcinoma(HCC) patients with early-stage immediately after ablation. We secondly propose to explore the risk factors for recurrence in patients undergoing CECT, and then develop a nomogram. Patients and Methods Clinical data were collected from 711 patients who received TACE combined with ablation from January 1, 2015, to December 31, 2022, at Beijing Youan Hospital. According to the imaging methods applied after ablation, patients were categorized into the CECT group and the NECT group and then were compared by Kaplan-Meier (KM) curves. Lasso regression is used to screen risk factors for recurrence and the nomogram was plotted. Finally, discrimination, calibration plot, and decision curve analysis (DCA) were used to measure the performance of the nomogram. Results The KM curve indicates that recurrence-free survival (RFS) was longer in the CECT group than in the NECT group (HR =0.759, 95% CI 0.606–0.951, P=0.016). Six variables were selected to construct the nomogram. 1-, 3-, and 5-year area under the curves (AUCs) (0.867, 0.731, 0.773 and 0.896, 0.784, 0.773) of the training and validation cohorts proved the good predictive performance of the nomogram. Calibration curves and DCA curves suggested accuracy and net clinical benefit rates. The nomogram enabled to classify of patients into three groups according to the risk of recurrence: low risk, intermediate risk, and high risk. There was a statistically significant difference in RFS between the two groups in the training and validation cohorts (P<0.001). Conclusion We demonstrated that HCC patients who underwent CECT evaluation after ablation had a better prognosis, making this evaluation method highly recommended for guiding clinical management.

show abstract

“…In this context, elevated globulin levels denote sustained immune system activation and ongoing inflammatory processes, potentially playing a role in the unfavorable prognosis linked with HCC. [31][32][33] Moreover, the immune response linked with globulin, plays a paradoxical role-while an activated immune system is essential for combating cancer, it can also be compromised, rendering it ineffective in eradicating cancer cells and thereby facilitating disease progression. [34][35][36] Prealbumin, a carrier protein produced by the liver, whose concentration changes are determined by the function of liver cells, and its low levels may reflect impaired liver function.…”

Section: Dovepressmentioning

confidence: 99%

Machine Learning-Based Nomogram for Predicting Overall Survival in Elderly Patients with Cirrhotic Hepatocellular Carcinoma Undergoing Ablation Therapy

Qiao,

Sheng,

et al. 2024

JHC

View full text Add to dashboard Cite

Purpose The aim of the study is to identify and evaluate multifaceted factors impacting the survival of elderly cirrhotic HCC patients following ablation therapy, with the goal of constructing a nomogram to predict their 3-, 5-, and 8-year overall survival (OS). Patients and Methods A retrospective analysis was conducted on 736 elderly cirrhotic HCC patients who underwent ablation therapy between 2014 and 2022. LASSO regression, random survival forest (RSF), and multivariate Cox analyses were employed to identify independent prognostic factors for OS, followed by the development and validation of a predictive nomogram. Harrell’s concordance index (C-index), calibration plot and decision curve analysis (DCA) were used to assess the performance of the nomogram. The nomogram was finally utilized to stratify patients into low-, intermediate-, and high-risk groups, aiming to assess its efficacy in precisely discerning individuals with diverse overall survival outcomes. Results Alcohol drinking, tumor number, globulin (Glob) and prealbumin (Palb) were identified and integrated to establish a novel prognostic nomogram. The nomogram exhibited strong discriminative ability with C-indices of 0.723 (training cohort) and 0.693 (validation cohort), along with significant Area Under the Curve (AUC) values for 3-year, 5-year, and 8-year OS in both cohorts (0.758, 0.770, and 0.811 for training cohort; 0.744, 0.699 and 0.737 for validation cohort). Calibration plots substantiated its consistency, while DCA curves corroborated its clinical utility. The nomogram further demonstrated exceptional effectiveness in discerning distinct risk populations, highlighting its robust applicability for prognostic stratification. Conclusion Our study successfully developed and validated a robust nomogram model based on four key clinical parameters for predicting 3-, 5- and 8-year OS among elderly cirrhotic HCC patients following ablation therapy. The nomogram exhibited a remarkable capability in identifying high-risk patients, furnishing clinicians with invaluable insights for postoperative surveillance and tailored therapeutic interventions.

show abstract

Inversed albumin-to-globulin ratio and underlying liver disease severity as a prognostic factor for survival in hepatocellular carcinoma patients undergoing transarterial chemoembolization

Abstract: to-globulin ratio and underlying liver disease severity as a prognostic factor for survival in hepatocellular carcinoma patients undergoing transarterial chemoembolization. Diagn Interv Radiol.

Cited by 5 publications

References 38 publications

Construction and validation of a machine learning-based nomogram to predict the prognosis of HBV associated hepatocellular carcinoma patients with high levels of hepatitis B surface antigen in primary local treatment: a multicenter study

Construction and validation of a machine learning-based nomogram to predict the prognosis of HBV associated hepatocellular carcinoma patients with high levels of hepatitis B surface antigen in primary local treatment: a multicenter study

Development and Validation of a Nomogram to Predict the Recurrence of HCC Patients Undergoing CECT After Ablation

Machine Learning-Based Nomogram for Predicting Overall Survival in Elderly Patients with Cirrhotic Hepatocellular Carcinoma Undergoing Ablation Therapy

Contact Info

Product

Resources

About