The Significance of Gamma-Glutamyl Transpeptidase to Lymphocyte Count Ratio in the Early Postoperative Recurrence Monitoring and Prognosis Prediction of AFP-Negative Hepatocellular Carcinoma

Li, Siming; Xu, Wentao; Liao, Minjun; Zhou, Yuanping; Weng, Jun; Ren, Liying; Yu, Junxiong; Liao, Weijia; Huang, Zhichuan

doi:10.2147/jhc.s286213

Cited by 21 publications

(26 citation statements)

References 38 publications

(46 reference statements)

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“…Many indicators have been explored or developed to predict the prognosis of patients with HCC. Moreover, we have verified several indexes in our previous work [ 12 – 14 ]. Since primary liver cancer is considered to be an immunosuppressive tumor so that the indicators that reflect immune response or liver function have always been focused on by researchers [ 15 ].…”

Section: Introductionsupporting

confidence: 80%

Predictive potential of Nomogram based on GMWG for patients with hepatocellular carcinoma after radical resection

Ren

Chen

et al. 2021

BMC Cancer

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Background Since it’s a challenging task to precisely predict the prognosis of patients with hepatocellular carcinoma (HCC). We developed a nomogram based on a novel indicator GMWG [(Geometric Mean of gamma-glutamyltranspeptidase (GGT) and white blood cell (WBC)] and explored its potential in the prognosis for HCC patients. Methods The patients enrolled in this study were randomly assigned to training and validation cohorts. And we performed the Least Absolute Shrinkage and Selection Operator proportional hazards model (LASSO Cox) model with clinical characteristics, serum indexes, and novel GMWG. Multivariate analysis was performed to build a nomogram. The performance of the nomogram was evaluated by C-index, the area under the receiver operating characteristic curve (AUC), and the calibration curve. Kaplan-Meier curves showed discrimination of the nomogram. Clinical utility was assessed by decision curve analysis (DCA). The discrimination ability of the nomogram was determined by the net reclassification index (NRI). Results The geometric mean of GGT and white WBC count (GMWG), neutrophil to lymphocyte ratio (NLR), and tumor size were significantly associated with the overall survival (OS). The variables above were used to develop the nomogram. The indexes of nomogram were 0.70 and 071 in the training or validation cohort, respectively. AUC of 1-, 3- and 5-year OS showed satisfactory accuracy as well. The calibration curve showed agreement between the ideal and predicted values. Kaplan-Meier curves based on the overall survival (OS) and disease-free survival (DFS) showed significant differences between nomogram predictive low and high groups. DCA showed clinical utilities while NRI showed discrimination ability in both training or validation cohort. Conclusions GMWG might be a potential prognostic indicator for patients with HCC. The nomogram containing GMWG also showed satisfaction prediction capacity.

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Section: Introductionsupporting

confidence: 80%

Predictive potential of Nomogram based on GMWG for patients with hepatocellular carcinoma after radical resection

Ren

Chen

et al. 2021

BMC Cancer

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“…The previous study found that in HCC patients with AFP-negative after radical resection, GLR had fair accuracy in predicting the early-recurrence, and our ndings persisted [13].…”

Section: Declarations Ethical Approval and Consent To Participatesupporting

confidence: 72%

“…Recently, some researchers have proposed that the aspartate aminotransferase to neutrophil ratio index (ANRI), aspartate aminotransferase to platelet ratio index (APRI), platelet to albumin ratio (PAR) and neutrophil times γglutamyl transpeptidase to lymphocyte ratio (NγLR) were independent effective predictors of the prognosis of patients with HCC [8][9][10][11]. Similarly, the preoperative gamma-glutamyl transferase to lymphocyte ratio (GLR) has been found to have signi cant prognostic value after resection in HCC patients with single tumor size ≤ 5 cm and with AFP-negative [12][13][14]. However, the prognostic value of GLR has not been reported in the monitoring of the recurrence of HCC patients with staging I-II following curative resection.…”

Section: Introductionmentioning

confidence: 99%

Non-Linear Relationship of Gamma-Glutamyl Transpeptidase To Lymphocyte Count Ratio With The Recurrence of Hepatocellular Carcinoma with Staging I-II: A Retrospective Cohort Study

Liang

Wen

et al. 2021

Preprint

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Background high recurrence rate was a major factor for poor postoperative prognosis of hepatocellular carcinoma (HCC) patients. The present study was intended to evaluate the association of gamma-glutamyl transpeptidase to lymphocyte count ratio (GLR) and the recurrence of HCC with staging I-II in Chinese. Methods the retrospective cohort data was derived from the First Affiliated Hospital of Zhengzhou University from January 2014 to December 2018 on 496 patients who underwent radical resection of HCC with staging I-II. Multivariable Cox regression models were used to determine hazard ratios (HR) and 95% confidence intervals (CIs) for the recurrence of HCC with staging I-II of each GLR tertile category. The restricted cubic spline model was used to find out the threshold effect. Results with the low tertile of GLR as the reference, multivariable-adjusted HRs and 95% CIs of the middle and high tertile categories were 1.748 (1.170, 2.612) and 2.078 (1.339, 3.227). In addition, there was a positive correlation (1.002 (1.001, 1.004)) and a non-liner relationship was found, whose point was 27.5. When the GLR was less than 27.5, the risk of recurrence increased, obviously with the increase in GLR levels (1.041 (1.014, 1.068)). Conclusions the GLR was independently associated with the recurrence of HCC patients with staging I-II. Furthermore, the relationship was positive and no-linear.

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“…For example, PSA-derived indices, such as free-to-total PSA (F/T) values and prostate-specific antigen density (PSAD), have been found to exhibit greater diagnostic accuracy than PSA alone ( 12 – 14 ). In addition, several inflammatory and neurotrophic markers, including neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, monocyte-to-lymphocyte ratio ( 15 , 16 ), gamma-glutamyl transpeptidase-to-lymphocyte count ratio ( 17 , 18 ), red cell distribution width-to-platelet ratio ( 19 , 20 ), and alanine transaminase-to-aspartate transaminase ratio ( 21 , 22 ), have previously been shown to have predictive value not only as inflammatory markers but also as indicators for the diagnosis and prognosis of malignancies. To date, studies focusing on the role of these composite indicators in the differential diagnosis of PCa are limited.…”

Section: Introductionmentioning

confidence: 99%

Machine learning model for the prediction of prostate cancer in patients with low prostate-specific antigen levels: A multicenter retrospective analysis

Deng

et al. 2022

Front. Oncol.

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ObjectiveThe aim of this study was to develop a predictive model to improve the accuracy of prostate cancer (PCa) detection in patients with prostate specific antigen (PSA) levels ≤20 ng/mL at the initial puncture biopsy.MethodsA total of 146 patients (46 with Pca, 31.5%) with PSA ≤20 ng/mL who had undergone transrectal ultrasound-guided 12+X prostate puncture biopsy with clear pathological results at the First Affiliated Hospital of Guangxi Medical University (November 2015 to December 2021) were retrospectively evaluated. The validation group was 116 patients drawn from Changhai Hospital(52 with Pca, 44.8%). Age, body mass index (BMI), serum PSA, PSA-derived indices, several peripheral blood biomarkers, and ultrasound findings were considered as predictive factors and were analyzed by logistic regression. Significant predictors (P < 0.05) were included in five machine learning algorithm models. The performance of the models was evaluated by receiver operating characteristic curves. Decision curve analysis (DCA) was performed to estimate the clinical utility of the models. Ten-fold cross-validation was applied in the training process.ResultsProstate-specific antigen density, alanine transaminase-to-aspartate transaminase ratio, BMI, and urine red blood cell levels were identified as independent predictors for the differential diagnosis of PCa according to multivariate logistic regression analysis. The RandomForest model exhibited the best predictive performance and had the highest net benefit when compared with the other algorithms, with an area under the curve of 0.871. In addition, DCA had the highest net benefit across the whole range of cut-off points examined.ConclusionThe RandomForest-based model generated showed good prediction ability for the risk of PCa. Thus, this model could help urologists in the treatment decision-making process.

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The Significance of Gamma-Glutamyl Transpeptidase to Lymphocyte Count Ratio in the Early Postoperative Recurrence Monitoring and Prognosis Prediction of AFP-Negative Hepatocellular Carcinoma

Cited by 21 publications

References 38 publications

Predictive potential of Nomogram based on GMWG for patients with hepatocellular carcinoma after radical resection

Predictive potential of Nomogram based on GMWG for patients with hepatocellular carcinoma after radical resection

Non-Linear Relationship of Gamma-Glutamyl Transpeptidase To Lymphocyte Count Ratio With The Recurrence of Hepatocellular Carcinoma with Staging I-II: A Retrospective Cohort Study

Machine learning model for the prediction of prostate cancer in patients with low prostate-specific antigen levels: A multicenter retrospective analysis

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