Albumin-to-Alkaline Phosphatase Ratio is an Independent Prognostic Indicator in Combined Hepatocellular and Cholangiocarcinoma

Construction and validation of a novel tumor morphology immune inflammatory nutritional score (TIIN score) for intrahepatic cholangiocarcinoma: a multicenter study

BACKGROUND

Tumor morphology, immune function, inflammatory levels, and nutritional status play critical roles in the progression of intrahepatic cholangiocarcinoma (ICC). This multicenter study aimed to investigate the association between markers related to tumor morphology, immune function, inflammatory levels, and nutritional status with the prognosis of ICC patients. Additionally, a novel tumor morphology immune inflammatory nutritional score (TIIN score), integrating these factors was constructed.

METHODS

A retrospective analysis was performed on 418 patients who underwent radical surgical resection and had postoperative pathological confirmation of ICC between January 2016 and January 2020 at three medical centers. The cohort was divided into a training set (n = 272) and a validation set (n = 146). The prognostic significance of 16 relevant markers was assessed, and the TIIN score was derived using LASSO regression. Subsequently, the TIIN-nomogram models for OS and RFS were developed based on the TIIN score and the results of multivariate analysis. The predictive performance of the TIIN-nomogram models was evaluated using ROC survival curves, calibration curves, and clinical decision curve analysis (DCA).

RESULTS

The TIIN score, derived from albumin-to-alkaline phosphatase ratio (AAPR), albumin-globulin ratio (AGR), monocyte-to-lymphocyte ratio (MLR), and tumor burden score (TBS), effectively categorized patients into high-risk and low-risk groups using the optimal cutoff value. Compared to individual metrics, the TIIN score demonstrated superior predictive value for both OS and RFS. Furthermore, the TIIN score exhibited strong associations with clinical indicators including obstructive jaundice, CEA, CA19-9, Child-pugh grade, perineural invasion, and 8th edition AJCC N stage. Univariate and multivariate analysis confirmed the TIIN score as an independent risk factor for postoperative OS and RFS in ICC patients (p < 0.05). Notably, the TIIN-nomogram models for OS and RFS, constructed based on the multivariate analysis and incorporating the TIIN score, demonstrated excellent predictive ability for postoperative survival in ICC patients.

CONCLUSION

The development and validation of the TIIN score, a comprehensive composite index incorporating tumor morphology, immune function, inflammatory level, and nutritional status, significantly contribute to the prognostic assessment of ICC patients. Furthermore, the successful application of the TIIN-nomogram prediction model underscores its potential as a valuable tool in guiding individualized treatment strategies for ICC patients. These findings emphasize the importance of personalized approaches in improving the clinical management and outcomes of ICC.

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.

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.

Prognostic Significance of Serum Albumin Level and Albumin-Based Mono- and Combination Biomarkers in Patients with Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the predominant form of primary liver cancer. Although many surgical and nonsurgical therapeutic options have been established for treating HCC, the overall prognosis for HCC patients receiving different treatment modalities remains inadequate, which causes HCC to remain among the most life-threatening human cancers worldwide. Therefore, it is vitally important and urgently needed to develop valuable and independent prognostic biomarkers for the early prediction of poor prognosis in HCC patients, allowing more time for more timely and appropriate treatment to improve the survival of patients. As the most abundant protein in plasma, human serum albumin (ALB) is predominantly expressed by the liver and exhibits a wide variety of essential biological functions. It has been well recognized that serum ALB level is a significant independent biomarker for a broad spectrum of human diseases including cancer. Moreover, ALB has been commonly used as a potent biomaterial and therapeutic agent in clinical settings for the treatment of various human diseases. This review provides a comprehensive summary of the evidence from the up-to-date published literature to underscore the prognostic significance of serum ALB level and various ALB-based mono- and combination biomarkers in the prediction of the prognosis of HCC patients after treatment with different surgical, locoregional, and systemic therapies.

Development and validation of a nomogram prediction model based on albumin-to-alkaline phosphatase ratio for predicting the prognosis of gallbladder carcinoma

Gallbladder carcinoma (GBC) is a rare biliary tract cancer with a high recurrence rate and a poor prognosis. Albumin-alkaline phosphatase ratio (AAPR) has been demonstrated to be a prognostic predictor for several cancers, but its predictive value for GBC patients remains unknown. The aim of this study was to investigate the predictive role of AAPR in GBC patients and to develop a novel nomogram prediction model for GBC patients. We retrospectively collected data from 80 patients who underwent surgery at the Hospital of 81st Group Army PLA as a training cohort. Data were collected from 70 patients with the same diagnosis who underwent surgery at the First Affiliated Hospital of Hebei North University as an external verification cohort. The optimal cut-off value of AAPR was determined using X-tile software. A nomogram for the overall survival (OS) based on multivariate Cox regression analysis was developed and validated using calibration curves, Harrell's concordance index, the receiver operating characteristic curves, and decisive curve analyses. The optimal cut-off value of AAPR was .20. Univariate and multivariate Cox regression analyses demonstrated that BMI (p = .043), R0 resection (p = .001), TNM stage (p = .005), and AAPR (p = .017) were independent risk factors for GBC patients. In terms of consistency, discrimination, and net benefit, the nomogram incorporating these four independent risk factors performed admirably. AAPR is an independent predictor of GBC patients undergoing surgery, and a novel nomogram prediction model based on AAPR showed superior predictive ability.

Prognostic risk factor analysis and nomogram construction for primary liver cancer in elderly patients based on SEER database

OBJECTIVE

To evaluate the risk factors and construct a nomogram model for the prognosis of primary liver cancer in the elderly based on the data from the US SEER database.

METHODS

The latest data of patients with primary liver cancer were extracted from the SEER database using SEER*STAT software, and the required variables were included. The data were screened and then divided into a training cohort and a validation cohort. A nomogram model was constructed by screening the variables through univariate and multivariate Cox analysis. The C-Index, ROC and calibration curves were used for model evaluation.

RESULTS

A total of 10 824 eligible cases from 2004 to 2017 were extracted, among which, 7757 cases were included in the training cohort and 3247 in the validation cohort. The C-Index of the model was 0.747 (in the training cohort) and 0.773 (in the validation cohort). The 3-year area under the curve (AUCs) of the training and the validation cohorts were 0.760 and 0.750, and the 5-year AUCs of the two cohorts were 0.761 and 0.748. The calibration curves showed an ideal calibration of the constructed model.

CONCLUSIONS

The nomogram model constructed followed by Cox regression analysis showed moderate calibration and discrimination property, and can provide reference to a certain extent for furture clinical application of primary liver cancer in the elderly.

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.

Prognostic significance of albumin to alkaline phosphatase ratio in critically ill patients with acute kidney injury

OBJECTIVE

No epidemiological evidence has investigated the effect of albumin to alkaline phosphatase ratio (AAPR) on the prognosis among critically ill patients with acute kidney injury (AKI). We aimed to explore the prognostic value of AAPR in these patients.

METHODS

We extracted all clinical data from MIMIC III. ROC curve analysis was used to evaluate the discrimination of AAPR for predicting in-hospital mortality. A generalized additive model was applied to identify a nonlinear association between AAPR and in-hospital mortality. The Cox proportional hazards models were used to determine the association between AAPR and in-hospital and 30-day mortality.

RESULTS

A total of 6894 eligible subjects were enrolled in this study. The relationship between AAPR and in-hospital mortality was nonlinear. Multivariate analysis demonstrated that lower AAPR (AAPR < 0.35) was an independent predictor of in-hospital and 30-day mortality after adjusting for potential confounders (HR 1.74, 95% CI 1.72-2.20, P < 0.001; HR 1.89, 95% CI 1.66-2.14, P < 0.001, respectively).

CONCLUSIONS

AAPR may serve as a potential prognostic biomarker in critically ill patients with AKI and lower AAPR was associated with increased risk of in-hospital and 30-day mortality among these patients.

Prognostic Significance of the Albumin to Fibrinogen Ratio in Peritoneal Dialysis Patients

Background

Albumin to fibrinogen ratio (AFR) is a demonstrated predictor of mortality in various diseases. The aim of this study was to evaluate the prognostic value of AFR to predict mortality in peritoneal dialysis (PD) patients.

Methods

We retrospectively analyzed 212 incident PD patients from January 2010 to December 2017 and followed them until December 2019. We used receiver operating curve (ROC) analysis to determine the optimal cut-off point for AFR at baseline to predict overall and cardiovascular mortality during the follow-up period. Kaplan-Meier curve and Cox regression analysis were applied to evaluate the association between AFR and all-cause and cardiovascular mortality.

Results

The optimal threshold for AFR to predict mortality was 8.48. A low AFR was strongly correlated with worse all-cause and cardiovascular mortality in PD patients. Multivariate analysis revealed that elevated AFR was an independent marker predicting reduced all-cause and cardiovascular mortality (HR 2.41, 95% CI 1.11–5.22, P = 0.026; and HR 2.18, 95% CI 1.21–3.95, P = 0.010, respectively).

Conclusions

Patients with a high AFR had reduced all-cause and cardiovascular mortality. AFR is a potential prognostic biomarker in PD patients.

Pretreatment serum albumin-to-alkaline phosphatase ratio is an independent prognosticator of survival in patients with metastatic gastric cancer

BACKGROUND

Previous studies have suggested that a low albumin-to-alkaline phosphatase ratio (AAPR) is associated with a lower survival rate in patients with various malignancies. However, the relationship between pretreatment AAPR and the prognosis of patients with gastric cancer (GC) remains unclear.

AIM

To investigate the prognostic value of AAPR in distant metastatic GC.

METHODS

A total of 191 patients with distant metastatic cancer from a single institute were enrolled in this study. Pretreatment clinical data, including serum albumin and alkaline phosphatase levels, were collected. A chi-square test or Fisher’s exact test was applied to evaluate the correlations between AAPR and various clinical parameters in GC patients. The Kaplan–Meier method and Cox proportional hazards regression model were used to evaluate the prognostic efficacy of AAPR in metastatic GC patients. A two-sided P value lower than 0.05 was considered statistically significant.

RESULTS

A receiver operating characteristic curve indicated that 0.48 was the optimal threshold value for AAPR. AAPR ≤ 0.48 was significantly associated with bone (P < 0.05) and liver metastasis (P < 0.05). Patients with high levels of AAPR had better survival in terms of overall survival (OS) and progression-free survival (PFS), regardless of the presence of liver/bone metastasis. Pretreatment AAPR was found to be a favorable predictor of OS and PFS based on a multivariate cox regression model. AAPR-M system, constructed based on AAPR and number of metastatic sites, showed superior predictive ability relative to the number of metastatic sites for predicting survival.

CONCLUSION

Pretreatment AAPR may serve as an independent prognostic factor for predicting PFS and OS in patients with metastatic GC. Furthermore, AAPR may assist clinicians with individualizing treatment.

A multidimensional nomogram combining imaging features and clinical factors to predict the invasiveness and metastasis of combined hepatocellular cholangiocarcinoma

Background

Combined hepatocellular cholangiocarcinoma (CHCC-CCA) is a rare type of primary liver cancer having aggressive behavior. Few studies have investigated the prognostic factors of CHCC-CCA. Therefore, this study aimed to establish a nomogram to evaluate the risk of microvascular invasion (MVI) and the presence of satellite nodules and lymph node metastasis (LNM), which are associated with prognosis.

Methods

One hundred and seventy-one patients pathologically diagnosed with CHCC-CCA were divided into a training set (n=116) and validation set (n=55). Logistic regression analysis was used to assess the relative value of clinical factors associated with the presence of MVI and satellite nodules. The least absolute shrinkage and selection operator (LASSO) algorithm was used to establish the imaging model of all outcomes, and to build clinical model of LNM. Nomograms were constructed by incorporating clinical risk factors and imaging features. The model performance was evaluated on the training and validation sets to determine its discrimination ability, calibration, and clinical utility. Kaplan Meier analysis and time dependent receiver operating characteristic (ROC) were displayed to evaluate the prognosis value of the predicted nomograms of MVI and satellite nodule.

Results

A nomogram comprising the platelet to lymphocyte ratio (PLR), albumin-to-alkaline phosphatase ratio (AAPR) and imaging model was established for the prediction of MVI. Carcinoembryonic antigen (CEA) level and size were combined with the imaging model to establish a nomogram for the prediction of the presence of satellite nodules. Favorable calibration and discrimination were observed in the training and validation sets for the MVI nomogram (C-indexes of 0.857 and 0.795), the nomogram for predicting satellite nodules (C-indexes of 0.919 and 0.883) and the LNM nomogram (C-indexes of 0.872 and 0.666). Decision curve analysis (DCA) further confirmed the clinical utility of the nomograms. The preoperatively predicted MVI and satellite nodules by the combined nomograms achieved satisfactory performance in recurrence-free survival (RFS) and overall survival (OS) prediction.

Conclusions

The proposed nomograms incorporating clinical risk factors and imaging features achieved satisfactory performance for individualized preoperative predictions of MVI, the presence of satellite nodules, and LNM. The prediction models were demonstrated to be good indicator for predicting the prognosis of CHCC-CCA, facilitating treatment strategy optimization for patients with CHCC-CCA.

Prediction of Survival and Analysis of Prognostic Factors for Patients With Combined Hepatocellular Carcinoma and Cholangiocarcinoma: A Population-Based Study

Background

Combined hepatocellular carcinoma and cholangiocarcinoma (CHC) is an uncommon subtype of primary liver cancer. Because of limited epidemiological data, prognostic risk factors and therapeutic strategies for patients with CHC tend to be individualized. This study aimed to identify independent prognostic factors and develop a nomogram-based model for predicting the overall survival (OS) of patients with CHC.

Methods

We recruited eligible individuals from the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2015 and randomly divided them into the training or verification cohort. Univariate and multivariate analyses were performed to identify independent variables associated with OS. Based on multivariate analysis, the nomogram was established, and its prediction performance was evaluated using the consistency index (C-index) and calibration curve.

Results

In total, 271 patients with CHC were included in our study. The median OS was 14 months, and the 1-, 3-, and 5-year OS rates were 52.3%, 27.1%, and 23.3%, respectively. In the training cohort, multivariate analysis showed that the pathological grade (hazard ratio [HR], 1.26; 95% confidence interval [CI]: 0.96–1.66), TNM stage (HR, 1.21; 95% CI: 1.02 - 1.44), and surgery (HR, 0.26; 95% CI: 0.17 - 0.40) were independent indicators of OS. The nomogram-based model related C-indexes were 0.76 (95% CI: 0.72 - 0.81) and 0.72 (95% CI: 0.66 - 0.79) in the training and validation cohorts, respectively. The calibration of the nomogram showed good consistency of 1-, 3-, and 5-year OS rates between the actual observed survival and predicted survival in both cohorts. The TNM stage (HR, 1.23; 95% CI: 1.01 - 1.49), and M stage (HR, 1.87; 95% CI: 1.14 3.05) were risk factors in the surgical treatment group. Surgical resection and liver transplantation could significantly prolong the survival, with no statistical difference observed.

Conclusions

The pathological grade, TNM stage, and surgery were independent prognostic factors for patients with CHC. We developed a nomogram model, in the form of a static nomogram or an online calculator, for predicting the OS of patients with CHC, with a good predictive performance.

Prognostic Significance of the Preoperative Albumin/Fibrinogen Ratio in Patients with Esophageal Squamous Cell Carcinoma after Surgical Resection

Purpose: The purpose of the present study was to investigate the prognostic value of inflammatory and nutritional-based scores, including the albumin/fibrinogen ratio (AFR) and albumin/globulin ratio (AGR), in patients with esophageal squamous cell carcinoma (ESCC).

Methods: The medical records of 641 patients with resectable ESCC from our institution were retrospectively analyzed. The preoperative AFR and AGR were investigated based on serum albumin, globulin and plasma fibrinogen levels. X-tile software, Kaplan-Meier survival curves and Cox proportional hazard models were performed to identify their prognostic value. The predictive accuracy was evaluated by the concordance index (C-index), calibration plots, and decision curve analysis (DCA).

Results: The optimal cutoff values were 15.3 and 1.8 for AFR and AGR, respectively. Univariate survival analysis identified age, smoking history, tumor size, pT status, pN status, NLR, PLR, fibrinogen, albumin, AFR, and AGR as factors associated with overall survival. Multivariate analysis indicated that preoperative AFR (HR: 0.690, 95% CI = 0.495~0.960, P = 0.028), rather than other inflammation- and nutrition-based scores, was an independent predictor of overall survival. The C-index of the predicted nomogram containing AFR (C-index = 0.677) was higher than that of the nomogram without AFR (C-index = 0.656). The calibration curves showed that the predictive abilities were consistent with the actual observation results. Moreover, compared with the traditional staging system, the results of DCA showed that the nomogram had superior predictive ability and higher clinical utility.

Conclusion: Our preliminary study suggested that a low preoperative AFR might be a novel and valuable predictor of poor prognosis in patients with ESCC, which may be helpful for prognosis assessment, patient counseling, and therapeutic modality selection.