Integrated Liver Inflammatory Score Predicts the Therapeutic Outcome of Patients with Hepatocellular Carcinoma after Transarterial Chemoembolization

An Integrated Liver Function, Systemic Inflammation, and Tumor Characteristic Score Predicts Prognosis in Hepatocellular Carcinoma After Curative Resection

BACKGROUND

Several scoring systems are currently used to predict prognosis of hepatocellular carcinoma (HCC), but none of them integrates liver function, systemic inflammation, and tumor characteristics in a unified model. The current study aimed to develop and validate a novel prognostic score that integrates liver function, systemic inflammation, and tumor characteristics in a unified model to predict the prognosis of HCC after curative resection.

METHODS

Patients with HCC who underwent curative liver resection were included in a training set (n = 1027). Multivariate Cox regression was performed to determine the risk factors for a poor prognosis. A prognostic score was developed by assigning points for risk factors in proportion to beta coefficients in a Cox multivariable model. Predictive performance and distinction ability of the prognostic score were further evaluated in two independent validation cohorts treated with either curative resection (n = 281) or transarterial chemoembolization (TACE) (n = 404) and compared with 16 other models.

RESULTS

The prognostic predictive system, named the function-inflammation-burden-alpha-fetoprotein (FIBA) score, was derived by assigning points for six independent predictors including albumin, total bilirubin, lymphocyte count, diameter of the largest tumor, number of tumors, and alpha-fetoprotein (AFP). The FIBA score showed an outperformed predictive value compared with other systems in both training and validation cohorts by giving the highest C-index, likelihood ratio chi-square values, and Wald test values as well as the lowest Akaike information criterion.

CONCLUSION

The FIBA score can be used to stratify HCC patients treated with curative resection. Meanwhile, the FIBA score performs well against other prognostic scoring systems and is potentially broadly applicable to a TACE-treated patient cohort.

Tumor enhancement ratio with unenhanced imaging is an independent prognostic factor for patients with hepatocellular carcinoma after transarterial chemoembolization

Objective

To investigative whether the odds tumor enhancement ratio (OTER) on cross-sectional imaging is a prognostic factor for hepatocellular carcinoma after transarterial chemoembolization (TACE).

Methods

This study involved 126 patients who underwent TACE from May 2015 to March 2019. The signal intensity/Hounsfield units (HU) was measured by placing regions of interest on the tumor and surrounding liver in unenhanced and arterial-phase contrast-enhanced cross-sectional images. The OTER was calculated as follows: OTER = (HU_TUMORart − HU_TUMORun)/ (HU_LIVERart − HU_LIVERun). Univariate analysis was performed to determine the factors associated with overall survival (OS). Variables with a P value of <0.10 were included in the multivariate Cox regression analysis.

Results

The median OS was 757 days. Tumors with a peripheral location, small size, and low OTER had better OS than those with a central location, large size, and high OTER. OS did not differ according to the extent of tumor involvement or tumor enhancement pattern. The OTER, tumor location, and size were included in the multivariate Cox regression analysis. A low OTER was the predictor of better OS.

Conclusion

A high OTER is a risk factor for poor OS in patients undergoing TACE. This should be taken into consideration before the procedure.

Predicting early refractoriness of transarterial chemoembolization in patients with hepatocellular carcinoma using a random forest algorithm: A pilot study

Purpose: To develop and validate a random forest (RF) based predictive model of early refractoriness to transarterial chemoembolization (TACE) in patients with unresectable hepatocellular carcinoma (HCC).

Methods: A total of 227 patients with unresectable HCC who initially treated with TACE from three independent institutions were retrospectively included. Following a random split, 158 patients (70%) were assigned to a training cohort and the remaining 69 patients (30%) were assigned to a validation cohort. The process of variables selection was based on the importance variable scores generated by RF algorithm. A RF predictive model incorporating the selected variables was developed, and five-fold cross-validation was performed. The discrimination and calibration of the RF model were measured by a receiver operating characteristic (ROC) curve and the Hosmer-Lemeshow test.

Results: The potential variables selected by RF algorithm for developing predictive model of early TACE refractoriness included patients' age, number of tumors, tumor distribution, platelet count (PLT), and neutrophil-to-lymphocyte ratio (NLR). The results showed that the RF predictive model had good discrimination ability, with an area under curve (AUC) of 0.863 in the training cohort and 0.767 in the validation cohort, respectively. In Hosmer-Lemeshow test, the RF model had a satisfactory calibration with P values of 0.538 and 0.068 in training cohort and validation cohort, respectively.

Conclusion: The RF algorithm-based model has a good predictive performance in the prediction of early TACE refractoriness, which may easily be deployed in clinical routine and help to determine the optimal patient of care.