Inflammatory Scores: Comparison and Utility in HCC Patients Undergoing Transarterial Chemoembolization in a North American Cohort

Which is the best TACE agent for patients with different NLR hepatocellular carcinomas? A systematic review and network meta-analysis

Background

Transarterial chemoembolization (TACE) is a common treatment for hepatocellular carcinoma (HCC), but the best therapeutic agent for TACE treatment has not been determined. The neutrophil/lymphocyte ratio (NLR) is a systemic immune system marker; however, the ability of the NLR to predict the prognosis of patients with HCC is unknown, and no studies have been conducted to determine the most appropriate TACE regimen for HCC patients with different NLRs.

Methods

The PubMed, Embase, Web of Science, and CNKI databases were searched through May 28, 2023. Comparisons of overall survival (OS) among cohort studies with different NLRs and different TACE treatment regimens were performed with a random effects model.

Findings

Thirty-five studies involving 9210 patients were included in this meta-analysis. The results showed that Group 3-4 (NLR<2.5) patients had a significantly longer OS than Group 1-2 (NLR 2.5-5.0). Among the patients, Group 1-3 (NLR 2.0-5.0) patients had the best survival after treatment with adriamycin (lnHR (95 % CI = 0.48 [0.31, 0.75] and lnHR (95 % CI = 0.41 [0.19, 0.91]). Among the Group 4 patients (NLR<2.0), the best outcome was obtained with platinum + adriamycin (lnHR (95 % CI = 0.59 [0.45, 0.78]), followed by adriamycin. A subgroup analysis of TACE combined with other treatments showed that adriamycin combined with sorafenib was the most effective and superior to the other treatment agents.

Interpretation

The NLR can be used to predict the prognosis of HCC patients treated with TACE; the higher the NLR is, the worse the prognosis. Adriamycin may be the best therapeutic agent for HCC patients treated with TACE.

Construction and Validation of a Novel Nomogram Predicting Recurrence in Alpha-Fetoprotein-Negative Hepatocellular Carcinoma Post-Surgery Using an Innovative Liver Function-Nutrition-Inflammation-Immune (LFNII) Score: A Bicentric Investigation

Purpose

We developed a nomogram based on the liver function, nutrition, inflammation, and immunity (LFNII) score to predict recurrence-free survival (RFS) post-resection in patients with hepatocellular carcinoma (HCC) exhibiting alpha-fetoprotein (AFP) negativity (AFP ≤20 ng/mL).

Patients and Methods

Clinical data of 661 patients diagnosed with alpha-fetoprotein-negative hepatocellular carcinoma (AFP-NHCC) who underwent surgical resection at two medical centers between 2012 and 2021 were collected. A total of 462 and 199 patients served as the training and validation sets, respectively. Pre-operative blood markers were collected and analyzed for LFNII. The LFNII score was formulated using the least absolute shrinkage and selection operator Cox regression model. A nomogram model was developed using the training set to incorporate other relevant clinicopathological indicators and predict postoperative recurrence. Model discrimination was assessed using the receiver operating characteristic curve, calibration was evaluated using a calibration curve, and clinical applicability was assessed using clinical decision curve analysis. A comparison with liver cancer staging was performed using the nomogram model. Finally, a cohort study was conducted to validate our findings.

Results

We derived the LFNII scores from nine indicators. Elevated LFNII scores correlated with unfavorable clinicopathological features. The LFNII score area under the curve revealed superior predictive efficacy at 1-, 2-, and 5-year RFS intervals, with values of 0.675, 0.658, and 0.633, respectively. Multivariate Cox analysis revealed that a high LFNII score independently increased RFS risk in patients with AFP-NHCC. The C-index of the LFNII-nomogram model was 0.686 (95% confidence interval [CI], 0.651-0.721). The nomogram model's clinical application value surpassed that of standard HCC staging systems.

Conclusion

The LFNII score-derived nomogram effectively predicted the RFS of patients with AFP-NHCC after curative resection.

Machine Learning Models in Prediction of Treatment Response After Chemoembolization with MRI Clinicoradiomics Features

PURPOSE

To evaluate machine learning models, created with radiomics and clinicoradiomics features, ability to predict local response after TACE.

MATERIALS AND METHODS

188 treatment-naïve patients (150 responders, 38 non-responders) with HCC who underwent TACE were included in this retrospective study. Laboratory, clinical and procedural information were recorded. Local response was evaluated by European Association for the Study of the Liver criteria at 3-months. Radiomics features were extracted from pretreatment pre-contrast enhanced T1 (T1WI) and late arterial-phase contrast-enhanced T1 (CE-T1) MRI images. After data augmentation, data were split into training and test sets (70/30). Intra-class correlations, Pearson's correlation coefficients were analyzed and followed by a sequential-feature-selection (SFS) algorithm for feature selection. Support-vector-machine (SVM) models were trained with radiomics and clinicoradiomics features of T1WI, CE-T1 and the combination of both datasets, respectively. Performance metrics were calculated with the test sets. Models' performances were compared with Delong's test.

RESULTS

1128 features were extracted. In feature selection, SFS algorithm selected 18, 12, 24 and 8 features in T1WI, CE-T1, combined datasets and clinical features, respectively. The SVM models area-under-curve was 0.86 and 0.88 in T1WI; 0.76, 0.71 in CE-T1 and 0.82, 0.91 in the combined dataset, with and without clinical features, respectively. The only significant change was observed after inclusion of clinical features in the combined dataset (p = 0.001). Higher WBC and neutrophil levels were significantly associated with lower treatment response in univariant analysis (p = 0.02, for both).

CONCLUSION

Machine learning models created with clinical and MRI radiomics features, may have promise in predicting local response after TACE.

LEVEL OF EVIDENCE

Level 4, Case-control study.

Systemic immune-inflammation index and the survival of hepatocellular carcinoma patients after transarterial chemoembolization: a meta-analysis

The systemic immune-inflammation index (SII), derived from neutrophil, platelet, and lymphocyte counts, has been associated with prognosis of patients with cancer. We performed a meta-analysis to evaluate the association between pretreatment SII and survival of patients with hepatocellular carcinoma (HCC) after transarterial chemoembolization (TACE). Cohort studies were identified by search of PubMed, Embase, Web of Science, CNKI, and Wanfang databases. Pooling the results was achieved with a random-effect model that incorporates potential heterogeneity between studies. Nine studies including 3557 patients with HCC contributed to the meta-analysis. Compared to patients with a lower SII, HCC patients with a higher pretreatment SII had poor overall survival (OS, hazard ratio [HR] 1.66, 95% confidence interval [CI] 1.25-2.21, p < 0.001; I2 = 80%) and poor progression-free survival (PFS, HR 1.28, 95% CI 1.05-1.56, p = 0.01; I2 = 0%) after TACE treatment. Further subgroup analyses confirmed a significant association between a high pretreatment SII and poor OS after TACE, which was not significantly affected by study country, sample size, age of the patients, cutoff values for SII, and adjustment of Child-Pugh score or alpha fetoprotein (p for subgroup effect all < 0.05). However, a higher SII was associated with poor OS in studies with follow-up duration ≤ 24 months (HR 1.94, 95% CI 1.39-2.72, p < 0.001), but the association was not statistically significant in studies with follow-up duration > 24 months (HR 1.27, 95% CI: 0.96-1.68, p = 0.09). A higher pretreatment SII was correlated with poor survival of HCC patients after TACE. A preliminary measurement of SII may be valuable for the prediction of the prognosis in HCC patients after TACE.

Development and Validation of Nomograms to Predict the Prognosis of Patients With Unresectable Hepatocellular Carcinoma Receiving Transarterial Chemoembolization

Background

Recent studies have shown that inflammatory indicators are closely related to the prognosis of patients with hepatocellular carcinoma, and they can serve as powerful indices for predicting recurrence and survival time after treatment. However, the predictive ability of inflammatory indicators has not been systematically studied in patients receiving transarterial chemoembolization (TACE). Therefore, the objective of this research was to determine the predictive value of preoperative inflammatory indicators for unresectable hepatocellular carcinoma treated with TACE.

Methods

Our retrospective research involved 381 treatment-naïve patients in 3 institutions, including the First Affiliated Hospital of Soochow University, Nantong First People's Hospital, and Nantong Tumor Hospital, from January 2007 to December 2020 that received TACE as initial treatment. Relevant data of patients were collected from the electronic medical record database, and the recurrence and survival time of patients after treatment were followed up. Least absolute shrinkage and selection operator (LASSO) algorithm was used to compress and screen the variables. We utilized Cox regression to determine the independent factors associated with patient outcomes and constructed a nomogram based on multivariate results. Finally, the nomogram was verified from discriminability, calibration ability, and practical applicability.

Results

Multivariate analysis revealed that the levels of aspartate aminotransferase-to-platelet ratio index (APRI) and lymphocyte count were independent influential indicators for overall survival (OS), whereas the levels of platelet-to-lymphocyte ratio (PLR) was an independent influential index for progression. Nomograms exhibited an excellent concordance index (C-index), in the nomogram of OS, the C-index was 0.753 and 0.755 in training and validation cohort, respectively; and in the nomogram of progression, the C-index was 0.781 and 0.700, respectively. The time-dependent C-index, time-dependent receiver operating characteristic (ROC), and time-dependent area under the curve (AUC) of the nomogram all exhibited ideal discrimination ability. Calibration curves significantly coincided with the standard lines, which indicated that the nomogram had high stability and low degree of over-fitting. Decision curve analysis revealed a wider range of threshold probabilities and could augment net benefits. The Kaplan-Meier curves for risk stratification indicated that the prognosis of patients varied significantly between risk categories (P < .0001).

Conclusions

The developed prognostic nomograms based on preoperative inflammatory indicators revealed high predictive accuracy for survival and recurrence. It can be a valuable clinical instrument for guiding individualized treatment and predicting prognosis.

A Novel Neutrophil-to-Lymphocyte Ratio and Sarcopenia Based TACE-Predict Model of Hepatocellular Carcinoma Patients

Purpose

Transarterial chemoembolization (TACE) was commonly applied in hepatocellular carcinoma (HCC) patients across BCLC A-C stages with heterogeneous outcomes in real-world practice. We aimed to develop a neutrophil-to-lymphocyte ratio (NLR) and sarcopenia-based prognostic nomogram to estimate the prognosis of HCC patients after TACE treatment.

Patients and Methods

Between June 2013 and December 2019, a total of 364 HCC patients who underwent TACE were included and randomly assigned to the training (n=255) and the validation cohort (n=109). Sarcopenia was diagnosed based on the third lumbar vertebra skeletal muscle mass index (L3-SMI). The multivariate Cox proportional risk model was used to generate a nomogram.

Results

NLR ≥4.0, sarcopenia, alpha-fetoprotein (AFP) ≥200 ng/mL, albumin-bilirubin (ALBI) grade 2 or 3, number of lesions (≥2), and maximum size of the lesion (≥5 cm) were independent predictors for overall survival (OS) (P < 0.05). The calibration curve shows that the predicted results agree well with the observed results. The time-dependent areas under the receiver-operating characteristic curves for OS at 1, 2, and 3 years predicted by the nomogram were 0.818/0.827, 0.742/0.823, and 0.748/0.836 in both training and validation cohorts. Nomogram can divide patients into low-, medium- and high-risk groups based on predictor factors. The C-indexes of the nomogram for OS were 0.782/0.728 in the training and validation cohorts, outperforming other currently available models.

Conclusion

A novel nomogram based on NLR and sarcopenia may be useful to predict the prognosis of HCC patients who underwent TACE across BCLC A-C stage patients.