Albumin-to-alkaline phosphatase ratio as a predictor of tumor recurrence and prognosis in patients with early-stage hepatocellular carcinoma undergoing radiofrequency ablation as initial therapy

Albumin-to-alkaline phosphatase ratio as a novel prognostic indicator in patients undergoing peritoneal dialysis: a propensity score matching analysis

Background

Though the albumin-to-alkaline phosphatase ratio (AAPR) is used as a biomarker in various diseases, little is known about its effect on outcomes after peritoneal dialysis (PD).

Methods

This multicenter retrospective study comprised 357 incident PD patients stratified according to the AAPR. Propensity score matching (PSM) was performed to identify 85 patients for a well-matched comparison of all-cause and cardiovascular mortality. Using Cox regression, we performed univariate and multivariate analyses to investigate the prognostic value of the AAPR and established a Kaplan-Meier curve-predicted nomogram to estimate expected overall survival (OS). We assessed the predictive accuracy using the concordance index (c-index).

Results

We found that the optimal cut-off of the AAPR to predict mortality was 0.36. In the present cohort of patients undergoing PD, a low AAPR strongly correlated with worse OS. In the multivariate analysis, the AAPR was shown to be an independent marker predicting reduced OS both before [hazard ratio (HR) 1.68, 95% confidence interval (CI) 1.08-2.60, P = 0.020] and after PSM (HR 1.96, 95% CI 1.06-3.62, P = 0.020). We also observed significant differences in OS in several subgroups, but not the group of patients with comorbidities. A nomogram was established to predict overall survival, with a c-index for prediction accuracy was 0.71 after PSM.

Conclusion

AAPR has potential as an independent prognostic biomarker in patients undergoing PD.

A Transformer-Based microvascular invasion classifier enhances prognostic stratification in HCC following radiofrequency ablation

BACKGROUND & AIMS

We aimed to develop a Transformer-based deep learning (DL) network for prognostic stratification in hepatocellular carcinoma (HCC) patients undergoing RFA.

METHODS

A Swin Transformer DL network was trained to establish associations between magnetic resonance imaging (MRI) datasets and the ground truth of microvascular invasion (MVI) based on 696 surgical resection (SR) patients with solitary HCC ≤3 cm, and was validated in an external cohort (n = 180). The multiphase MRI-based DL risk outputs using an optimal threshold of .5 was employed as a MVI classifier for prognosis stratification in the RFA cohort (n = 180).

RESULTS

Over 90% of all enrolled patients exhibited hepatitis B virus infection. Liver cirrhosis was significantly more prevalent in the RFA cohort compared to the SR cohort (72.2% vs. 44.1%, p < .001). The MVI risk outputs exhibited good performance (area under the curve values = .938 and .883) for predicting MVI in the training and validation cohort, respectively. The RFA patients at high risk of MVI classified by the MVI classifier demonstrated significantly lower recurrence-free survival (RFS) and overall survival rates at 1, 3 and 5 years compared to those classified as low risk (p < .001). Multivariate cox regression modelling of a-fetoprotein > 20 ng/mL [hazard ratio (HR) = 1.53; 95% confidence interval (95% CI): 1.02-2.33, p = .047], high risk of MVI (HR = 3.76; 95% CI: 2.40-5.88, p < .001) and unfavourable tumour location (HR = 2.15; 95% CI: 1.40-3.29, p = .001) yielded a c-index of .731 (bootstrapped 95% CI: .667-.778) for evaluating RFS after RFA. Among the three risk factors, MVI was the most powerful predictor for intrahepatic distance recurrence.

CONCLUSIONS

The proposed MVI classifier can serve as a valuable imaging biomarker for prognostic stratification in early-stage HCC patients undergoing RFA.

Predicting the unpredictable: a robust nomogram for predicting recurrence in patients with ampullary carcinoma

OBJECTIVE

To screen the risk factors affecting the recurrence risk of patients with ampullary carcinoma (AC)after radical resection, and then to construct a model for risk prediction based on Lasso-Cox regression and visualize it.

METHODS

Clinical data were collected from 162 patients that received pancreaticoduodenectomy treatment in Hebei Provincial Cancer Hospital from January 2011 to January 2022. Lasso regression was used in the training group to screen the risk factors for recurrence. The Lasso-Cox regression and Random Survival Forest (RSF) models were compared using Delong test to determine the optimum model based on the risk factors. Finally, the selected model was validated using clinical data from the validation group.

RESULTS

The patients were split into two groups, with a 7:3 ratio for training and validation. The variables screened by Lasso regression, such as CA19-9/GGT, AJCC 8th edition TNM staging, Lymph node invasion, Differentiation, Tumor size, CA19-9, Gender, GPR, PLR, Drinking history, and Complications, were used in modeling with the Lasso-Cox regression model (C-index = 0.845) and RSF model (C-index = 0.719) in the training group. According to the Delong test we chose the Lasso-Cox regression model (P = 0.019) and validated its performance with time-dependent receiver operating characteristics curves(tdROC), calibration curves, and decision curve analysis (DCA). The areas under the tdROC curves for 1, 3, and 5 years were 0.855, 0.888, and 0.924 in the training group and 0.841, 0.871, and 0.901 in the validation group, respectively. The calibration curves performed well, as well as the DCA showed higher net returns and a broader range of threshold probabilities using the predictive model. A nomogram visualization is used to display the results of the selected model.

CONCLUSION

The study established a nomogram based on the Lasso-Cox regression model for predicting recurrence in AC patients. Compared to a nomogram built via other methods, this one is more robust and accurate.

C-reactive protein to albumin ratio predict responses to programmed cell death-1 inhibitors in hepatocellular carcinoma patients

BACKGROUND

Over the years, programmed cell death-1 (PD-1) inhibitors have been routinely used for hepatocellular carcinoma (HCC) treatment and yielded improved survival outcomes. Nonetheless, significant heterogeneity surrounds the outcomes of most studies. Therefore, it is critical to search for biomarkers that predict the efficacy of PD-1 inhibitors in patients with HCC.

AIM

To investigate the role of the C-reactive protein to albumin ratio (CAR) in evaluating the efficacy of PD-1 inhibitors for HCC.

METHODS

The clinical data of 160 patients with HCC treated with PD-1 inhibitors from January 2018 to November 2022 at the First Affiliated Hospital of Guangxi Medical University were retrospectively analyzed.

RESULTS

The optimal cut-off value for CAR based on progression-free survival (PFS) was determined to be 1.20 using x-tile software. Cox proportional risk model was used to determine the factors affecting prognosis. Eastern Cooperative Oncology Group performance status [hazard ratio (HR) = 1.754, 95% confidence interval (95%CI) = 1.045-2.944, P = 0.033], CAR (HR = 2.118, 95%CI = 1.057-4.243, P = 0.034) and tumor number (HR = 2.932, 95%CI = 1.246-6.897, P = 0.014) were independent prognostic factors for overall survival. CAR (HR = 2.730, 95%CI = 1.502-4.961, P = 0.001), tumor number (HR = 1.584, 95%CI = 1.003-2.500, P = 0.048) and neutrophil to lymphocyte ratio (HR = 1.120, 95%CI = 1.022-1.228, P = 0.015) were independent prognostic factors for PFS. Two nomograms were constructed based on independent prognostic factors. The C-index index and calibration plots confirmed that the nomogram is a reliable risk prediction tool. The ROC curve and decision curve analysis confirmed that the nomogram has a good predictive effect as well as a net clinical benefit.

CONCLUSION

Overall, we reveal that the CAR is a potential predictor of short- and long-term prognosis in patients with HCC treated with PD-1 inhibitors. If further verified, CAR-based nomogram may increase the number of markers that predict individualized prognosis.

Prognostic effect of albumin-to-alkaline phosphatase ratio on patients with hepatocellular carcinoma: a systematic review and meta-analysis

The prognostic value of albumin-to-alkaline phosphatase ratio (AAPR) in patients with hepatocellular carcinoma (HCC) remains controversial. This meta-analysis aims to evaluate the prognostic role of AAPR in patients with HCC. The databases of Web of Science, Embase, Cochrane Library and PubMed were comprehensively searched from inception to April 25, 2022. Pooled hazard ratio (HR) and 95% confidence interval (CI) were calculated with Stata 16.0 software for the assessment of the relationship between AAPR and overall survival (OS) as well as recurrence-free survival (RFS) in patients with HCC. A total of 2634 patients from 12 cohorts were included in this meta-analysis. The pooled results showed that lower AAPR predicted poorer OS (HR 2.02, 95% CI 1.78-2.30). Similarly, pooled results demonstrated that lower AAPR also predicted poorer RFS (HR 1.88, 95% CI 1.37-2.57). The heterogeneity for RFS by multivariate analytic results and the publication bias for OS existed, however, the subgroup analysis, meta-regression analysis as well as adjustment using trim-and-fill analysis confirmed an association between AAPR and OS as well as RFS. This meta-analysis proves that lower AAPR in patients with HCC predicted inferior survival outcomes, and AAPR might be a promising indicator for the prognosis of HCC.

Nomogram established on account of Lasso-Cox regression for predicting recurrence in patients with early-stage hepatocellular carcinoma

Purpose

To investigate the risk factors for recurrence in patients with early-stage hepatocellular carcinoma (HCC) after minimally invasive treatment with curative intent, then to construct a prediction model based on Lasso-Cox regression and visualize the model built.

Methods

Clinical data were collected from 547 patients that received minimally invasive treatment in our hospital from January 1, 2012, to December 31, 2016. Lasso regression was used to screen risk factors for recurrence. Then we established Cox proportional hazard regression model and random survival forest model including several parameters screened by Lasso regression. An optimal model was selected by comparing the values of C-index, then the model was visualized and the nomogram was finally plotted.

Results

The variables screened by Lasso regression including age, gender, cirrhosis, tumor number, tumor size, platelet-albumin-bilirubin index (PALBI), and viral load were incorporated in the Cox model and random survival forest model (P<0.05). The C-index of these two models in the training sets was 0.729 and 0.708, and was 0.726 and 0.700 in the validation sets, respectively. So we finally chose Lasso-Cox regression model, and the calibration curve in the validation set performed well, indicating that the model built has a better predictive ability. And then a nomogram was plotted based on the model chosen to visualize the results.

Conclusions

The present study established a nomogram for predicting recurrence in patients with early-stage HCC based on the Lasso-Cox regression model. This nomogram was of some guiding significance for screening populations at high risk of recurrence after treatment, by which doctors can formulate individualized follow-up strategies or treatment protocols according to the predicted risk of relapse for patients to improve the long-term prognosis.

Preoperative prognostic value of alfa-fetoprotein density in patients with hepatocellular carcinoma undergoing radiofrequency ablation

OBJECTIVES

To examine the prognostic value of preoperative alfa-fetoprotein (AFP) density and other clinical factors in patients undergoing percutaneous radiofrequency ablation (RFA) of hepatocellular carcinoma (HCC).

METHODS

From January 2010 to December 2018, a total of 543 patients undergoing RFA for HCC meeting the Milan criteria were included at our institution. AFP density was calculated as absolute AFP pre-ablation divided by the total volume of all HCC lesions. The survival rates according to AFP density were estimated using the Kaplan-Meier method and compared using the log-rank test. Univariate and multivariate Cox proportional-hazards regression analyses were used to assess predictors of overall survival (OS) and progression-free survival (PFS).

RESULTS

The Kaplan-Meier 1-, 3-, and 5-year OS rates were 98.8%, 88.5%, and 70.4%, respectively, for the low AFP density group, and 98.3%, 74.9%, and 49.4%, respectively, for the high AFP density group. The corresponding PFS rates were 78.9%, 56.7%, and 40.9% (low AFP density group), and 63.6%, 40.8%, and 27.5% (high AFP density group). High AFP density was associated with significantly reduced PFS and OS (both p < 0.001). Multivariate analysis suggested that AFP density was a predictor of OS and PFS.

CONCLUSIONS

Serum AFP density may serve as a promising predictor of survival in patients with HCC undergoing RFA. High AFP density could identify patients who might be prone to recurrence or progression and need close surveillance.

Dual-Channel Recognition of Human Serum Albumin and Glutathione by Fluorescent Probes with Site-Dependent Responsive Features

Design of chemical probes with high specificity and responses are particularly intriguing. In this work, a fluorescent probe (M-OH-SO₃) with dual-channel spectral responses toward human serum albumin (HSA) is presented. By employing dinitrobenzenesulfonate as a recognition site as well as a fluorescence quencher, probe M-OH-SO₃ displayed weak fluorescence, which, nevertheless, exhibits extensive yellow (575 nm) and red (660 nm) fluorescence emissions toward HSA under excitations at 400 and 500 nm, respectively. Interestingly, M-OH-SO₃ displayed the best performance toward HSA with distinctly higher selectivity than that of its counterparts M-SO₃, M-H-SO₃, and M-F-SO₃, which were prepared simply by modulating the functional group at the ortho position of the dicyanoisophorone core. Molecular docking results revealed that M-OH-SO₃ possesses the lowest binding energy among the tested derivatives and accordingly the strongest binding affinity. Probe M-OH-SO₃ showed a good linear relationship toward HSA in a range of 0.5-18 μM with a limit of detection of 35 nM. Cell imaging results demonstrated that probe M-OH-SO₃ could visualize the variation HSA levels in hepatocarcinoma cells. In addition, probe M-OH-SO₃ could also be employed for the recognition of glutathione through the cleavage of the dinitrobenzenesulfonate group along with an enhancement of emission at 575 nm. The site-dependent properties inspired a novel paradigm for design of fluorescent probes with optimized selectivity and responses.

Lipid-mediated ex vivo cell surface engineering for augmented cellular functionalities

Once administrated, intercellular adhesion to recognize and/or arrest target cells is essential for specific treatments, especially for cancer or tumor. However, immune cells administrated into the tumor-microenvironment could lose their intrinsic functionalities such as target recognition ability, resulting in an ineffective cancer immunotherapy. Various manipulation techniques for decorating functional moieties onto cell surface and enhancing target recognition have been developed. A hydrophobic interaction-mediated ex-vivo cell surface engineering using lipid-based biomaterials could be a state-of-the-art engineering technique that could achieve high-efficiency cell surface modification by a single method without disturbance of intrinsic characteristics of cells. In this regard, this review provides design principles for the development of lipid-based biomaterials with a linear structure of lipid, polyethylene glycol, and functional group, strategies for the synthesis process, and their practical applications in biomedical engineering. Especially, we provide new insights into the development of a novel surface coating techniques for natural killer (NK) cells with engineering decoration of cancer targeting moieties on their cell surfaces. Among immune cells, NK cells are interesting cell population for substituting T cells because of their excellent safety and independent anticancer efficacy. Thus, optimal strategies to select cancer-type-specific targeting moieties and present them onto the surface of immune cells (especially, NK cells) using lipid-based biomaterials could provide additional tools to capture cancer cells for developing novel immune cell therapy products. Enhanced anticancer efficacies by surface-engineered NK cells have been demonstrated both in vitro and in vivo. Therefore, it could be speculated that recent progresses in cell surface modification technology via lipid-based biomaterials could strengthen immune surveillance and immune synapses for utilization in a next-generation cancer immunotherapy, beyond currently available genetic engineering tool such as chimeric antigen receptor-mediated immune cell modulation.

Prognostic role of pretreatment albumin-to-alkaline phosphatase ratio in locally advanced laryngeal and hypopharyngeal cancer: Retrospective cohort study

Background: This study was designed to assess the prognostic significance of pretreatment albumin-to-alkaline phosphatase ratio (AAPR) in locally advanced laryngeal and hypopharyngeal cancer (LA-LHC).

Materials and Methods: The clinical data of 341 patients with locally advanced laryngeal and hypopharyngeal cancer diagnosed between March 2007 and December 2018 were retrospectively collected and analyzed. The optimal cut-off value of AAPR for evaluating DFS was determined using the ROC curve, and 0.4912 was selected. Based on pretreatment AAPR values, patients were divided into two groups (low vs. high AAPR). Survival analysis was used to investigate the survival distribution between the groups. Univariate and multivariate analyses were performed to evaluate the prognostic value of AAPR. Based on the results of the multivariate analysis, we further developed models of DFS and OS. We assigned low AAPR, N1-3, age ≥65 years, and positive vascular invasion one score, respectively.

Results: Survival analysis demonstrated that the survival of patients with low and high AAPR was significantly different (low vs. high AAPR: 5-year DFS, 46.0 vs. 71.9%, p<0.001; 5-year OS, 69.0 vs. 72.6%, p<0.001). Univariate and multivariate analyses further showed that pretreatment AAPR served as an independent indicator in LA-LHC. Moreover, survival analysis showed that patients with high model score had poorer DFS and OS (5-year DFS: 58.1, 42.7, 26.9 and 9.1% of score zero, one, two, and three respectively, p<0.001; 5-year OS: 63.0, 50.3, 34.1 and 28.6% of score zero, one, two, and three respectively, p<0.001).

Conclusion: Pretreatment AAPR could be an independent prognostic indicator in patients with LA-LHC. Incorporating AAPR into the risk stratification model might better categorize patients with worse oncological outcomes and support treatment strategy making.

Novel model combining contrast-enhanced ultrasound with serology predicts hepatocellular carcinoma recurrence after hepatectomy

BACKGROUND

Surgery is the primary curative option in patients with hepatocellular carcinoma (HCC). However, recurrence within 2 years is observed in 30%–50% of patients, being a major cause of mortality.

AIM

To construct and verify a non-invasive prediction model combining contrast-enhanced ultrasound (CEUS) with serology biomarkers to predict the early recurrence of HCC.

METHODS

Records of 744 consecutive patients undergoing first-line curative surgery for HCC in one institution from 2016–2018 were reviewed, and 292 local patients were selected for analysis. General characteristics including gender and age, CEUS liver imaging reporting and data system (LIRADS) parameters including wash-in time, wash-in type, wash-out time, and wash-out type, and serology biomarkers including alanine aminotransferase, aspartate aminotransferase, platelets, and alpha-fetoprotein (AFP) were collected. Univariate analysis and multivariate Cox proportional hazards regression model were used to evaluate the independent prognostic factors for tumor recurrence. Then a nomogram called CEUS model was constructed. The CEUS model was then used to predict recurrence at 6 mo, 12 mo, and 24 mo, the cut-off value was calculate by X-tile, and each C-index was calculated. Then Kaplan-Meier curve was compared by log-rank test. The calibration curves of each time were depicted.

RESULTS

A nomogram predicting early recurrence (ER), named CEUS model, was formulated based on the results of the multivariate Cox regression analysis. This nomogram incorporated tumor diameter, preoperative AFP level, and LIRADS, and the hazard ratio was 1.123 (95% confidence interval [CI]: 1.041-1.211), 1.547 (95%CI: 1.245-1.922), and 1.428 (95%CI: 1.059-1.925), respectively. The cut-off value at 6 mo, 12 mo, and 24 mo was 100, 80, and 50, and the C-index was 0.748 (95%CI: 0.683-0.813), 0.762 (95%CI: 0.704-0.820), and 0.762 (95%CI: 0.706-0.819), respectively. The model showed satisfactory results, and the calibration at 6 mo was desirable; however, the calibration at 12 and 24 mo should be improved.

CONCLUSION

The CEUS model enables the well-calibrated individualized prediction of ER before surgery and may represent a novel tool for biomarker research and individual counseling.

Radiofrequency ablation of hepatocellular carcinoma: Prognostic factors and recent advances

With the improvement of technology and diagnostic level, radiofrequency ablation (RFA) has made rapid progress in the treatment of primary hepatocellular carcinoma (HCC) in the past two decades. Especially, the overall survival after the treatment of small HCCs by RFA can be comparable to that achieved by hepatic resection. The 10-year survival rates of RFA for HCC were 27.3%-46.1%, and for solitary HCC less than 3 cm, the 10-year survival rate is about 74.0%. RFA combined with other therapies can expand the indications of RFA treatment and benefit the survival of patients with HCC. The prognostic model of RFA for HCC provides a powerful tool for individualized clinical diagnosis and treatment.