Multi-institutional validation of novel models for predicting the prognosis of patients with huge hepatocellular carcinoma

Interactive model for predicting the oncological outcome of patients with early-stage huge hepatocellular carcinoma after hepatectomy: a multicenter population-based study

An interactive model for predicting the oncological outcome of patients with early-stage huge hepatocellular carcinoma (ES-HHCC) after hepatectomy is still lacking. This study was aimed at exploring the independent risk parameters and developing an interactive model for predicting the cancer-specific survival (CSS) of ES-HHCC. Data from patients with ES-HHCC who underwent hepatectomy were collected. The dimensionality of the clinical features was reduced by least absolute shrinkage and selection operator regression and further screened as predictors of CSS by Cox regression. Then, an interactive prediction model was developed and validated. Among the 514 screened patients, 311 and 203 of them were assigned into the training and validation cohort, respectively. Six independent variables, including alpha-fetoprotein, cirrhosis, microvascular invasion, satellite, tumor morphology, and tumor diameter, were identified and incorporated into the prediction model for CSS. The model achieved C-indices of 0.724 and 0.711 in the training and validation cohorts, respectively. Calibration curves showed general consistency in both cohorts. Compared with single predictor, the model had a better performance and greater benefit according to the time-independent receiver operating characteristic curve and decision curve analysis (P < 0.05). The calculator owned satisfactory accuracy and flexible operability for predicting the CSS of ES-HHCC, which could serve as a practical tool to stratify patients with different risks, and guide decision-making.

A Retrospective Study on Predicting Recurrence of Intermediate-Stage Hepatocellular Carcinoma After Radical Therapy

Purpose

This study aimed to investigate the potential benefits of radical therapy in patients with stage B disease.

Patients and Methods

A retrospective analysis was conducted on a cohort of 437 patients diagnosed with stage B hepatocellular carcinoma, who underwent either hepatic resection (HR) or radiofrequency ablation (RFA) at the Cancer Institute and Hospital of Tianjin Medical University from May 2011 to May 2022. Multivariate COX regression analysis was performed to identify the independent prognostic factors related to recurrence-free survival (RFS). The performance of the developed nomogram was evaluated using various statistical measures, including the concordance index (C-index), receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA).

Results

Multivariate analysis revealed that tumor diameter, number of tumors, number of involved liver segments, alpha-fetoprotein (AFP), carbohydrate antigen 19-9 (CA19-9), lactate dehydrogenase (LDH), and systemic immune inflammation index (SII) were independent prognostic factors influencing patients' RFS, and these factors were incorporated into the nomogram. The C-index of the nomogram in the training cohort was 0.721, and the AUC at 2 and 3 years was 0.772 and 0.790, respectively. These values were appreciably higher than commonly used clinic staging systems and other predictive models. The calibration curve and DCA demonstrated good calibration and net benefit. Survival analysis comparing stage B patients who received radical treatment with stage A patients with multiple lesions did not reveal a significant difference in Kaplan-Meier survival curves (P=0.91).

Conclusion

The nomogram provided a precise prediction of the recurrence for stage B hepatocellular carcinoma patients undergoing radical treatment. Furthermore, certain stage B patients may benefit from radical treatment.

Effect of transarterial chemoembolization as postoperative adjuvant therapy for intermediate-stage hepatocellular carcinoma with microvascular invasion: a multicenter cohort study

BACKGROUND

Intermediate-stage hepatocellular carcinoma (HCC) with microvascular invasion (MVI) is associated with high recurrence rates and poor survival outcomes after surgery. This study aimed to evaluate the efficacy of postoperative transarterial chemoembolization (TACE) on prognosis of intermediate-stage HCC patients with MVI after curative liver resection (LR).

MATERIALS AND METHODS

Patients who had intermediate-stage HCC with MVI and underwent curative LR between January 2013 and December 2019 at three institutions in China were identified for further analysis. Overall survival (OS) and recurrence-free survival (RFS) were compared between patients treated with and without postoperative TACE by propensity score-matching.

RESULTS

A total of 246 intermediate-stage HCC patients with MVI were enrolled, 137 entered into the LR group and 109 entered into the LR+TACE group. The 1-year, 3-year, and 5-year RFS rates were 42.0, 27.2, and 17.8% in LR+TACE group, and 31.8, 18.2, and 8.7% in LR group. The 1-year, 3-year, and 5-year OS rates were 81.7, 47.2, and 26.1% in the LR+TACE group, and 67.3, 35.6, and 18.5% in the LR group. Compared with LR alone, LR+TACE was associated with significantly better RFS [hazard ratio (HR), 1.443; 95% CI: 1.089-1.914; P =0.009] and OS (HR, 1.438; 95% CI: 1.049-1.972; P =0.023). No difference was observed with RFS and OS in single TACE and multiple TACE in the matched cohort.

CONCLUSION

Postoperative adjuvant TACE could be beneficial for intermediate-stage HCC patients with MVI.

Development of preoperative and postoperative machine learning models to predict the recurrence of huge hepatocellular carcinoma following surgical resection

Resection has been commonly utilized for treating huge hepatocellular carcinoma (HCC) with a diameter of ≥10 cm; however, a high rate of mortality is reported due to recurrence. The present study was designed to predict the recurrence following resection based on preoperative and postoperative machine learning models. In total, 1,082 patients with HCC who underwent liver resection in the Eastern Hepatobiliary Surgery Hospital cohort between January 2008 and December 2016 were divided into a training cohort and an internal validation cohort. In addition, 164 patients from Mengchao Hepatobiliary Hospital cohort between January 2014 and December 2016 served as an external validation cohort. The demographic information, and serological, MRI, and pathological data were obtained from each patient prior to and following surgery, followed by evaluating the model performance using the concordance index, time-dependent receiver operating characteristic curves, prediction error cures, and a calibration curve. A preoperative random survival forest (RSF) model and a postoperative RSF model were constructed based on the training set, which outperformed the conventional models, such as the Barcelona Clinic Liver Cancer (BCLC), the 8th edition of the American Joint Committee on Cancer (AJCC 8th) staging systems, and the Chinese stage systems. In addition, the preoperative and postoperative RSF models could also re-stratify patients with BCLC stage A/B/C or AJCC 8th stage IB/II/IIIA/IIIB or Chinese stage IB/IIA/IIB/IIIA into low-risk, intermediate-risk, and high-risk groups in the training and the two validation cohorts. The preoperative and postoperative RSF models were effective for predicting recurrence in patients with huge HCC following hepatectomy.

Diagnostic and Prognostic Nomograms for Hepatocellular Carcinoma Based on PIVKA-II and Serum Biomarkers

BACKGROUND

The aim of the present study was to develop an improved diagnostic and prognostic model for HBV-associated HCC by combining AFP with PIVKA-II and other potential serum/plasma protein biomarkers.

METHODS

A total of 578 patients, including 352 patients with HBV-related HCC, 102 patients with HBV-associated liver cirrhosis (LC), 124 patients with chronic HBV, and 127 healthy subjects (HS), were enrolled in the study. The serum levels of AFP, PIVKA-II, and other laboratory parameters were collected. Univariate and multivariate logistic regression and Cox regression analyses were performed to identify independent diagnostic and prognostic factors, respectively. The diagnostic efficacy of the nomogram was evaluated using receiver operator curve (ROC) analysis and the prognostic performance was measured by Harrell's concordance index (C-index).

RESULTS

AFP and PIVKA-II levels were significantly increased in HBV-related HCC, compared with those in HBV-associated LC and chronic HBV participants (p < 0.05 and p < 0.001, respectively). The diagnostic nomogram, which included age, gender, AFP, PIVKA-II, prothrombin time (PT), and total protein (TP), discriminated patients with HBV-HCC from those with HBV-LC or chronic HBV with an AUC of 0.970. In addition, based on the univariate and multivariate Cox regression analysis, PIVKA-II, γ-glutamyl transpeptidase, and albumin were found to be significantly associated with the prognosis of HBV-related HCC and were incorporated into a nomogram. The C-index of the nomogram for predicting 3-year survival in the training and validation groups was 0.75 and 0.78, respectively. The calibration curves for the probability of 3-year OS showed good agreement between the nomogram prediction and the actual observation in the training and the validation groups. Furthermore, the nomogram had a higher C-index (0.74) than that of the Child-Pugh grade (0.62), the albumin-bilirubin (ALBI) score (0.64), and Barcelona Clinic Liver Cancer (0.56) in all follow-up cases.

CONCLUSION

Our study suggests that the nomograms based on AFP, PIVKA-II, and potential serum protein biomarkers showed a better performance in the diagnosis and prognosis of HCC, which may help to guide therapeutic strategies and assess the prognosis of HCC.

Development and validation of a nomogram for predicting the cancer-specific survival of fibrolamellar hepatocellular carcinoma patients

Fibrolamellar hepatocellular carcinoma (FLC) is a rare subtype of hepatocellular carcinoma. Our study aimed to construct a nomogram to predict the cancer-specific survival (CSS) of FLC. Data of 200 FLC patients enrolled in the Surveillance, Epidemiology, and End Results (SEER) database were divided into the training group and the validation group. Prognostic factors identified in the univariate and multivariate Cox regression analyses were used to construct the nomogram. The concordance index (C-index), calibration curves, time-dependent receiver operating characteristic curve (ROC), and decision curve analysis (DCA) were used to evaluate the performance of the nomogram. As a result, age ≥ 59, N1 stage, M1 stage, tumor size ≤ 2.0 cm, and no surgery were significantly associated with lower CSS in multivariate Cox regression analysis. The calibration plot showed good consistency of the nomogram between predicted and observed outcomes in the training and validation groups. Compared with the TNM staging system, the prognostic evaluation model (PEM) showed a higher C-index (0.823 vs 0.656). The PEM also showed better predictive performance, with areas under the curve of 0.909 and 0.890 for predicting the 1- and 5-year survival. The AUCs of the TNM stage model for predicting 1- and 5-year survival were 0.629 and 0.787, respectively. In addition, the DCA curve showed that the nomogram had better clinical utility. Finally, we concluded that Age, N stage, M stage, tumor size, and surgery are independent prognostic factors for FLC. PEM established based on these five prognostic indicators can help predict the CSS of patients with FLC.

Does Chronic Kidney Disease Really Affect the Complications and Prognosis After Liver Resection for Hepatocellular Carcinoma? A Meta-Analysis

Purpose

The purpose of this meta-analysis was to analyze whether chronic kidney disease (CKD) affected the complications and prognosis after liver resection for hepatocellular carcinoma.

Methods

The PubMed, Embase, and Cochrane Library databases were searched from inception to 22 February 2022 to find eligible studies. Complications, overall survival (OS), and disease-free survival (DFS) were collected, and this meta-analysis was performed with RevMan 5.3.

Results

A total of nine studies including 6,541 patients were included in this meta-analysis. After pooling all baseline information, the CKD group had a higher rate of Child-Pugh grade B than the Non-CKD group (OR = 1.58, 95% CI = 1.3 to 1.93, P < 0.00001). As for surgery-related information, the CKD group had larger blood loss (MD = −404.79, 95% CI = −509.70 to −299.88, P < 0.00001), and higher rate of blood transfusion (OR = 2.47, 95% CI = 1.85 to 3.3, P < 0.00001). In terms of complications, the CKD group had a higher rate of overall complications (OR = 2.1, 95% CI = 1.57 to 2.81, P < 0.00001) and a higher rate of ≥ grade III complications (OR = 2.04, 95% CI = 1.57 to 2.81, P = 0.0002). The CKD group had poor OS compared with the non-CKD group (HR = 1.28, 95% CI = 1.1 to 1.49, P = 0.001). However, in terms of DFS, no significant difference was found (HR = 1.11, 95% CI = 0.96 to 1.28, P = 0.16).

Conclusion

Preexisting CKD was associated with higher ratio of complications and poor OS.

Prognostic Significance of Tumor Growth Rate (TGR) in Patients with Huge Hepatocellular Carcinoma Undergoing Transcatheter Arterial Chemoembolization

The prognostic value of the tumor growth rate (TGR) in huge hepatocellular carcinoma (HHCC) patients treated with transcatheter arterial chemoembolization (TACE) as an initial treatment remains unclear. This two-center retrospective study was conducted in 97 patients suffering from HHCC. Demographic characteristics, oncology characteristics, and some serological markers were collected for analysis. The TGR was significantly linear and associated with the risk of death when applied to restricted cubic splines. The optimal cut-off value of TGR was −8.6%/month, and patients were divided into two groups according to TGR. Kaplan–Meier analysis showed that the high-TGR group had a poorer prognosis. TGR (hazard ratio (HR), 2.06; 95% confidence interval (CI), 1.23–3.43; p = 0.006), presence of portal vein tumor thrombus (PVTT) (HR, 1.93; 95% CI, 1.13–3.27; p = 0.016), and subsequent combination therapy (HR, 0.59; 95% CI, 0.35–0.99; p = 0.047) were independent predictors of OS in the multivariate analysis. The model with TGR was superior to the model without TGR in the DCA analysis. Patients who underwent subsequent combination therapy showed a longer survival in the high-TGR group. This study demonstrated that higher TGR was associated with a worse prognosis in patients with HHCC. These findings will distinguish patients who demand more personalized combination therapy and rigorous surveillance.

A Nomogram for Predicting Event-Free Survival in Childhood Acute Lymphoblastic Leukemia: A Multicenter Retrospective Study

OBJECTIVE

Even though childhood acute lymphoblastic leukemia (ALL) has an encouraging survival rate in recent years, some patients are still at risk of relapse or even death. Therefore, we aimed to construct a nomogram to predict event-free survival (EFS) in patients with ALL.

METHOD

Children with newly diagnosed ALL between October 2016 and July 2021 from 18 hospitals participating in the South China children's leukemia Group (SCCLG) were recruited and randomly classified into two subsets in a 7:3 ratio (training set, n=1187; validation set, n=506). Least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis were adopted to screen independent prognostic factors. Then, a nomogram can be build based on these prognostic factors to predict 1-, 2-, and 3-year EFS. Concordance index (C-index), area under the curve (AUC), calibration curve, and decision curve analysis (DCA) were used to evaluate the performance and clinical utility of nomogram.

RESULT

The parameters that predicted EFS were age at diagnosis, white blood cell at diagnosis, immunophenotype, ETV6-RUNX1/TEL-AML1 gene fusion, bone marrow remission at day 15, and minimal residual disease at day 15. The nomogram incorporated the six factors and provided C-index values of 0.811 [95% confidence interval (CI) = 0.792-0.830] and 0.797 (95% CI = 0.769-0.825) in the training and validation set, respectively. The calibration curve and AUC revealed that the nomogram had good ability to predict 1-, 2-, and 3-year EFS. DCA also indicated that our nomogram had good clinical utility. Kaplan-Meier analysis showed that EFS in the different risk groups stratified by the nomogram scores was significant differentiated.

CONCLUSION

The nomogram for predicting EFS of children with ALL has good performance and clinical utility. The model could help clinical decision-making.