Dataset Information

Advanced lung cancer inflammation index predicts overall survival of hepatocellular carcinoma after hepatectomy.

ABSTRACT:

Aim

Limited data are available regarding ALI's clinical relevance and prognostic value in patients with hepatocellular carcinoma (HCC) after hepatectomy.

Materials and methods

HCC patients who received hepatectomy at the Meizhou People's Hospital from May 2011 to February 2022 were enrolled in the study cohort. The ALI was calculated as follows: ALI = BMI (kg/m²) × ALB (g/dL)/(absolute neutrophil count/absolute lymphocyte count). The primary outcome was overall survival (OS). The secondary outcome was cancer-specific survival (CSS). Univariate and multivariate Cox regression analyses were performed, followed by nomogram construction and decision curve analysis (DCA).

Results

425 HCC patients were enrolled for analyses. Lower preoperative ALI was significantly correlated with incomplete tumor capsule and advanced tumor stage. Lower preoperative ALI was an adverse independent prognostic factor for OS (HR: 1.512, 95% CI: 1.122-2.039, P 0.007) and CSS (HR: 1.754, 95% CI: 1.262-2.438, P <0.001) in HCC patients. The nomogram plot was built based on three (including age, TNM stage, and ALI) and two (including TNM stage and ALI) independent prognostic factors for OS and CSS, respectively. Further analyses indicated that the nomogram had better predictive value and some net benefit than the traditional TNM stage alone, especially in long-term OS.

Conclusions

Our study further indicated that ALI could be a prognostic marker for OS and CSS in HCC patients after hepatectomy, especially in long-term OS.

SUBMITTER: Wen YZ

PROVIDER: S-EPMC10882069 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature

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Publications

Advanced lung cancer inflammation index predicts overall survival of hepatocellular carcinoma after hepatectomy.

Wen Yuan-Zhang YZ Liu Gao-Min GM Liao Jia-Peng JP Xu Ji-Wei JW

Frontiers in oncology 20240208

<h4>Aim</h4>Limited data are available regarding ALI's clinical relevance and prognostic value in patients with hepatocellular carcinoma (HCC) after hepatectomy.<h4>Materials and methods</h4>HCC patients who received hepatectomy at the Meizhou People's Hospital from May 2011 to February 2022 were enrolled in the study cohort. The ALI was calculated as follows: ALI = BMI (kg/m<sup>2</sup>) × ALB (g/dL)/(absolute neutrophil count/absolute lymphocyte count). The primary outcome was overall survival ...[more]

PMID: 38390261

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Project description:BackgroundsMalnutrition and systemic inflammatory responses are associated with poor overall survival (OS) in lung cancer patients, but it remains unclear which biomarkers are better for predicting their prognosis. This study tried to determine the best one among the existing common nutrition/inflammation-based indicators of OS for patients with lung cancer.Materials and methodsThere were 16 nutrition or systemic inflammation-based indicators included in this study. The cut-off points for the indicators were calculated using maximally selected rank statistics. The OS was evaluated using the Kaplan-Meier estimator, and univariate and multivariate Cox proportional hazard models were used to determine the relationship between the indicators and OS. A time-dependent receiver operating characteristic curves (time-ROC) and C-index were calculated to assess the predictive ability of the different indicators.ResultsThere were 1772 patients with lung cancer included in this study. In univariate analysis, all 16 indicators were significantly associated with OS of the patients (all P < 0.001). Except for platelet-to-lymphocyte ratio, all other indicators were independent predictors of OS in multivariate analysis (all P < 0.05). Low advanced lung cancer inflammation index (ALI) was associated with higher mortality risk of lung cancer [hazard ratio, 1.30; 95% confidence interval (CI), 1.13-1.49]. The results of the time-AUC and C-index analyses indicated that the ALI (C-index: 0.611) had the best predictive ability on the OS in patients with lung cancer. In different sub-groups, the ALI was the best indicator for predicting the OS of lung cancer patients regardless of sex (C-index, 0.609 for men and 0.613 for women) or smoking status (C-index, 0.629 for non-smoker and 0.601 for smoker) and in patients aged <65 years (C-index, 0.613). However, the modified Glasgow prognostic score was superior to the other indicators in non-small cell lung cancer patients (C-index, 0.639) or patients aged ≥65 years (C-index, 0.610), and the glucose-to-lymphocyte ratio performed better prognostic ability in patients with small cell lung cancer (C-index, 0.601).ConclusionsThe prognostic ability of the ALI is superior to the other inflammation/nutrition-based indicators for all patients with lung cancer.

Project description:BackgroundHepatocellular carcinoma (HCC) is associated with a dismal prognosis, and prediction of the prognosis of HCC can assist in therapeutic decision-makings. An increasing number of studies have shown that the texture parameters of images can reflect the heterogeneity of tumors, and may have the potential to predict the prognosis of patients with HCC after surgical resection. The aim of this study was to investigate the prognostic value of computed tomography (CT) texture parameters in patients with HCC after hepatectomy and to develop a radiomics nomogram by combining clinicopathological factors and the radiomics signature.MethodsIn all, 544 eligible patients were enrolled in this retrospective study and were randomly divided into the training cohort (n = 381) and the validation cohort (n = 163). The tumor regions of interest (ROIs) were delineated, and the corresponding texture parameters were extracted. The texture parameters were selected by using the least absolute shrinkage and selection operator (LASSO) Cox model in the training cohort, and a radiomics signature was established. Then, the radiomics signature was further validated as an independent risk factor for overall survival (OS). The radiomics nomogram was established based on the Cox regression model. The concordance index (C-index), calibration plot and decision curve analysis (DCA) were used to evaluate the performance of the radiomics nomogram.ResultsThe radiomics signature was formulated based on 7 OS-related texture parameters, which were selected in the training cohort. In addition, the radiomics nomogram was developed based on the following five variables: α-fetoprotein (AFP), platelet-to-lymphocyte ratio (PLR), largest tumor size, microvascular invasion (MVI) and radiomics score (Rad-score). The nomogram displayed good accuracy in predicting OS (C-index = 0.747) in the training cohort and was confirmed in the validation cohort (C-index = 0.777). The calibration plots also showed excellent agreement between the actual and predicted survival probabilities. The DCA indicated that the radiomics nomogram showed better clinical utility than the clinicopathologic nomogram.ConclusionThe radiomics signature is a potential prognostic biomarker of HCC after hepatectomy. The radiomics nomogram that integrated the radiomics signature can provide a more accurate estimation of OS than the clinicopathologic nomogram for HCC patients after hepatectomy.

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Dataset Information

Advanced lung cancer inflammation index predicts overall survival of hepatocellular carcinoma after hepatectomy.

Aim

Materials and methods

Results

Conclusions

Publications

Advanced lung cancer inflammation index predicts overall survival of hepatocellular carcinoma after hepatectomy.

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