A-G Score Associated With Outcomes in Solitary Hepatocellular Carcinoma Patients After Hepatectomy

Predictive nomograms based on gamma-glutamyl transpeptidase to prealbumin ratio for prognosis of hepatocellular carcinoma patients without microvascular invasion

BACKGROUND

Hepatocellular carcinoma (HCC) presents a significant threat to individuals and healthcare systems due to its high recurrence rate. Accurate prognostic models are essential for improving patient outcomes. Gamma-glutamyl transpeptidase (GGT) and prealbumin (PA) are biomarkers closely related to HCC. This study aimed to investigate the predictive value of the GGT to PA ratio (GPR) and to construct prognostic nomograms for HCC patients without microvascular invasion.

METHODS

We retrospectively analyzed data from 355 HCC patients who underwent radical hepatectomy at Shengjing Hospital of China Medical University between December 2012 and January 2021. Patients were randomly assigned to a training cohort (n = 267) and a validation cohort (n = 88). The linearity of GPR was assessed using restricted cubic spline (RCS) analysis, and the optimal cut-off value was determined by X-tile. Kaplan-Meier survival curves and log-rank tests were used to investigate the associations between GPR and both progression-free survival (PFS) and overall survival (OS). Cox multivariate regression analysis identified independent risk factors, enabling the construction of nomograms. Time-dependent receiver operating characteristic (ROC) and calibration curves were used to evaluate the accuracy of the nomograms. Decision curve analysis (DCA) assessed the predictive value of the models.

RESULTS

Patients were categorized into GPR-low and GPR-high groups based on a GPR value of 333.33. Significant differences in PFS and OS were observed between the two groups (both P < 0.001). Cox multivariate analysis identified GPR as an independent risk factor for both PFS (OR = 1.80, 95% CI: 1.24-2.60, P = 0.002) and OS (OR = 1.87, 95% CI: 1.07-3.26, P = 0.029). The nomograms demonstrated good predictive performance, with C-index values of 0.69 for PFS and 0.76 for OS. Time-dependent ROC curves and calibration curves revealed the accuracy of the models in both the training and validation cohorts, with DCA results indicating notable clinical value.

CONCLUSIONS

GPR emerged as an independent risk factor for both OS and PFS in HCC patients without microvascular invasion. The nomograms based on GPR demonstrated relatively robust predictive efficiency for prognosis.

Preoperative alpha fetoprotein, total bilirubin, fibrinogen, albumin, and lymphocytes predict postoperative survival in hepatocellular carcinoma

AIMS

Our study focused on exploring the feasible prognostic laboratory parameters of HCC and establishing a score model to estimate individualized overall survival (OS) in HCC after resection.

METHODS

Four hundred and sixty-one patients with HCC who underwent hepatectomy between January 2010 and December 2017 was enrolled in this investigation. Cox proportional hazards model was conducted to analyze the prognostic value of laboratory parameters. The score model construction was based on the forest plot results. Overall survival was evaluated by Kaplan-Meier method and the log-rank test. The novel score model was validated in an external validation cohort from a different medical institution.

RESULTS

We identified that alpha fetoprotein (AFP), total bilirubin (TB), fibrinogen (FIB), albumin (ALB), and lymphocyte (LY) were independent prognostic factors. High AFP, TB, FIB (HR > 1, p < 0.05), and low ALB, LY (HR < 1, p < 0.05) were associated with the survival of HCC. The novel score model of OS based on these five independent prognostic factors achieved high C-index of 0.773 (95% confidence interval [CI]: 0.738-0.808), which was significantly higher than those of the single five independent factors (0.572-0.738). The score model was validated in the external cohort whose C-index was 0.7268 (95% CI: 0.6744-0.7792).

CONCLUSION

The novel score model we established was an easy-to-use tool which could enable individualized estimation of OS in patients with HCC who underwent curative hepatectomy.

Clinical outcomes and prognostic risk factors of Langerhans cell histiocytosis in children: Results from the BCH-LCH 2014 protocol study

Langerhans cell histiocytosis (LCH) is a rare myeloid neoplasm mainly affecting young children. This study aimed to evaluate the outcomes of 449 pediatric patients enrolled in the BCH-LCH 2014 study. 52.6% of patients were classified with single-system (SS) LCH, 28.1% with multisystem (MS) risk organ negative (RO-) LCH, and 19.4% with MS RO+ LCH. Three hundred ninety-six patients (88.2%) were initially treated with first-line therapy based on the vindesine-prednisone combination. One hundred thirty-nine patients who lacked a response to initial treatment were shifted to second-line therapy, 72 to intensive treatment Arm S1 (a combination of cytarabine, cladribine, vindesine, and dexamethasone), and 67 to Arm S2 (without cladribine). The 5-year overall survival (OS), progression-free survival (PFS), and relapse rates were 98.2% (median: 97.6 months), 54.6% (median: 58.3 months), and 29.9%, respectively. MS RO+ patients had the worst prognosis among the three clinical subtypes. For the patients initially treated with first-line therapy, the 5-year OS, PFS, and relapse rates were 99.2%, 54.5%, and 29.3%, respectively. Patients in Arm S1 had a significantly better prognosis than patients in Arm S2 (5-year PFS: 69.2% vs. 46.5%, p = .042; relapse rate: 23.4% vs. 44.2%, p = .031). Multivariate analysis revealed that early treatment response, the involvement of RO, skin, and oral mucosa, as well as laboratory parameters, including CRP and γ-GT, were independent risk factors for the PFS of LCH. Thus, the prognosis of LCH in children has been improved significantly with stratified chemotherapy, and progression and relapse remained the challenges, especially for RO+ patients.

Integrative Analysis of Peripheral Blood Indices for the Renal Sinus Invasion Prediction of T1 Renal Cell Carcinoma: An Ensemble Study Using Machine Learning-Assisted Decision-Support Models

Purpose

Renal sinus invasion is an attributive factor affecting the prognosis of renal cell carcinoma (RCC). This study aimed to construct a risk prediction model that could stratify patients with RCC and predict renal sinus invasion with the help of a machine learning (ML) algorithm.

Patients and Methods

We retrospectively recruited 1229 patients diagnosed with T1 stage RCC at the Baotou Cancer Hospital between November 2013 and August 2021. Iterative analysis was used to screen out predictors related to renal sinus invasion, after which ML-based models were developed to predict renal sinus invasion in patients with T1 stage RCC. The receiver operating characteristic curve (ROC), decision curve analysis (DCA), and clinical impact curve (CIC) were performed to evaluate the robustness and clinical practicability of each model.

Results

A total of 21 candidate variables were shortlisted for model building. Iterative analysis screened that neutrophil to albumin ratio (NAR), hemoglobin level * albumin level * lymphocyte count/platelet count ratio (HALP), prognostic nutrition index (PNI), body mass index*serum albumin/neutrophil-lymphocyte ratio (AKI), NAR, and fibrinogen (FIB) concentration (NARFIB), platelet to lymphocyte ratio (PLR), and R.E.N.A.L score was related to renal sinus invasion and contributed significantly to ML-based algorithm. The areas under the ROC curve (AUCs) of the random forest classifier (RFC) model, support vector machine (SVM), eXtreme gradient boosting (XGBoost), artificial neural network (ANN), and decision tree (DT) ranged from 0.797 to 0.924. The optimal risk probability of renal sinus invasion predicted was RFC (AUC = 0.924, 95% confidence interval [CI]: 0.414–1.434), which showed robust discrimination for identifying high-risk patients.

Conclusion

We successfully develop practical models for renal sinus invasion prediction, particularly the RFC, which could contribute to early detection via integrating systemic inflammatory factors and nutritional parameters.