Development of a predictive nomogram for early recurrence of hepatocellular carcinoma in patients undergoing liver transplantation

Development of a nomogram for predicting liver transplantation prognosis in hepatocellular carcinoma

BACKGROUND

At present, liver transplantation (LT) is one of the best treatments for hepatocellular carcinoma (HCC). Accurately predicting the survival status after LT can significantly improve the survival rate after LT, and ensure the best way to make rational use of liver organs.

AIM

To develop a model for predicting prognosis after LT in patients with HCC.

METHODS

Clinical data and follow-up information of 160 patients with HCC who underwent LT were collected and evaluated. The expression levels of alpha-fetoprotein (AFP), des-gamma-carboxy prothrombin, Golgi protein 73, cytokeratin-18 epitopes M30 and M65 were measured using a fully automated chemiluminescence analyzer. The best cutoff value of biomarkers was determined using the Youden index. Cox regression analysis was used to identify the independent risk factors. A forest model was constructed using the random forest method. We evaluated the accuracy of the nomogram using the area under the curve, using the calibration curve to assess consistency. A decision curve analysis (DCA) was used to evaluate the clinical utility of the nomograms.

RESULTS

The total tumor diameter (TTD), vascular invasion (VI), AFP, and cytokeratin-18 epitopes M30 (CK18-M30) were identified as important risk factors for outcome after LT. The nomogram had a higher predictive accuracy than the Milan, University of California, San Francisco, and Hangzhou criteria. The calibration curve analyses indicated a good fit. The survival and recurrence-free survival (RFS) of high-risk groups were significantly lower than those of low- and middle-risk groups (P < 0.001). The DCA shows that the model has better clinical practicability.

CONCLUSION

The study developed a predictive nomogram based on TTD, VI, AFP, and CK18-M30 that could accurately predict overall survival and RFS after LT. It can screen for patients with better postoperative prognosis, and improve long-term survival for LT patients.

A novel nomogram to predict the recurrence of hepatocellular carcinoma after liver transplantation using extended selection criteria

BACKGROUND

Liver transplantations (LTs) with extended criteria have produced surgical results comparable to those obtained with traditional standards. However, it is not sufficient to predict hepatocellular carcinoma (HCC) recurrence after LT according to morphological criteria alone. The present study aimed to construct a nomogram for predicting HCC recurrence after LT using extended selection criteria.

METHODS

Retrospective data on patients with HCC, including pathology, serological markers and follow-up data, were collected from January 2015 to April 2020 at Huashan Hospital, Fudan University, Shanghai, China. Logistic least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression analyses were performed to identify and construct the prognostic nomogram. Receiver operating characteristic (ROC) curves, Kaplan-Meier curves, decision curve analyses (DCAs), calibration diagrams, net reclassification indices (NRIs) and integrated discrimination improvement (IDI) values were used to assess the prognostic capacity of the nomogram.

RESULTS

A total of 301 patients with HCC who underwent LT were enrolled in the study. The nomogram was constructed, and the ROC curve showed good performance in predicting survival in both the development set (2/3) and the validation set (1/3) (the area under the curve reached 0.748 and 0.716, respectively). According to the median value of the risk score, the patients were categorized into the high- and low-risk groups, which had significantly different recurrence-free survival (RFS) rates (P < 0.01). Compared with the Milan criteria and University of California San Francisco (UCSF) criteria, DCA revealed that the new nomogram model had the best net benefit in predicting 1-, 3- and 5-year RFS. The nomogram performed well for calibration, NRI and IDI improvement.

CONCLUSIONS

The nomogram, based on the Milan criteria and serological markers, showed good accuracy in predicting the recurrence of HCC after LT using extended selection criteria.

Contrast enhanced ultrasound combined with serology predicts hepatocellular carcinoma recurrence: a retrospective observation cohort study

Objectives

To construct a novel model based on contrast-enhanced ultrasound (CEUS) and serological biomarkers to predict the early recurrence (ER) of primary hepatocellular carcinoma within 2 years after hepatectomy.

Methods

A total of 466 patients who underwent CEUS and curative resection between 2016.1.1 and 2019.1.1 were retrospectively recruited from one institution. The training and testing cohorts comprised 326 and 140 patients, respectively. Data on general characteristics, CEUS Liver Imaging Reporting and Data System (LI-RADS) parameters, and serological were collected. Univariate analysis and multivariate Cox proportional hazards regression model were used to evaluate the independent prognostic factors for tumor recurrence, and the Contrast-enhanced Ultrasound Serological (CEUSS) model was constructed. Different models were compared using prediction error and time-dependent area under the receiver operating characteristic curve (AUC). The CEUSS model's performances in ER prediction were assessed.

Results

The baseline data of the training and testing cohorts were equal. LI-RADS category, α-fetoprotein level, tumor maximum diameter, total bilirubin level, starting time, iso-time, and enhancement pattern were independent hazards, and their hazards ratios were 1.417, 1.309, 1.133, 1.036, 0.883, 0.985, and 0.70, respectively. The AUCs of CEUSS, BCLC,TNM, and CNLC were 0.706, 0.641, 0.647, and 0.636, respectively, in the training cohort and 0.680, 0.583, 0.607, and 0.597, respectively, in the testing cohort. The prediction errors of CEUSS, BCLC, TNM, and CNLC were 0.202, 0.205, 0.205, and 0.200, respectively, in the training cohort and 0.204, 0.221, 0.219, and 0.211, respectively, in the testing cohort.

Conclusions

The CEUSS model can accurately and individually predict ER before surgery and may represent a new tool for individualized treatment.

A new model based on gamma-glutamyl transpeptidase to lymphocyte ratio and systemic immune-inflammation index can effectively predict the recurrence of hepatocellular carcinoma after liver transplantation

Background

Liver transplantation (LT) is one of the most effective treatment modalities for hepatocellular carcinoma (HCC), but patients with HCC recurrence after LT always have poor prognosis. This study aimed to evaluate the predictive value of the gamma-glutamyl transpeptidase-to-lymphocyte ratio (GLR) and systemic immune-inflammation index (SII) in terms of HCC recurrence after LT, based on which we developed a more effective predictive model.

Methods

The clinical data of 325 HCC patients who had undergone LT were collected and analyzed retrospectively. The patients were randomly divided into a development cohort (n = 215) and a validation cohort (n = 110). Cox regression analysis was used to screen the independent risk factors affecting postoperative recurrence in the development cohort, and a predictive model was established based on the results of the multivariate analysis. The predictive values of GLR, SII and the model were evaluated by receiver operating characteristic (ROC) curve analysis, which determined the cut-off value for indicating patients' risk levels. The Kaplan-Meier survival analysis and the competing-risk regression analysis were used to evaluate the predictive performance of the model, and the effectiveness of the model was verified further in the validation cohort.

Results

The recurrence-free survival of HCC patients after LT with high GLR and SII was significantly worse than that of patients with low GLR and SII (P<0.001). Multivariate Cox regression analysis identified GLR (HR:3.405; 95%CI:1.954-5.936; P<0.001), SII (HR: 2.285; 95%CI: 1.304-4.003; P=0.004), tumor number (HR:2.368; 95%CI:1.305-4.298; P=0.005), maximum tumor diameter (HR:1.906; 95%CI:1.121-3.242; P=0.017), alpha-fetoprotein level (HR:2.492; 95%CI:1.418-4.380; P=0.002) as independent risk factors for HCC recurrence after LT. The predictive model based on these risk factors had a good predictive performance in both the development and validation cohorts (area under the ROC curve=0.800, 0.791, respectively), and the performance of the new model was significantly better than that of single GLR and SII calculations (P<0.001). Survival analysis and competing-risk regression analysis showed that the predictive model could distinguish patients with varying levels of recurrence risk in both the development and validation cohorts.

Conclusions

The GLR and SII are effective indicators for evaluating HCC recurrence after LT. The predictive model based on these indicators can accurately predict HCC recurrence after LT and is expected to guide preoperative patient selection and postoperative follow-up.

Different Models to Predict the Risk of Recurrent Hepatocellular Carcinoma in the Setting of Liver Transplantation

Simple Summary

Liver transplantation is considered the first-choice curative therapy for hepatocellular carcinoma in the early phase of the disease, when surgical resection is not possible. Even when implementing restrictive criteria to select patients for liver transplantation, there is a risk of recurrence in the transplanted liver, influencing the long-term outcome and prognosis. As it is challenging to predict the individual risk of recurrence, there is a need for validated and predictive scoring systems to use to stratify patients before and/or after liver transplantation. Most of the proposed scorings include biological markers for tumour behavior, in addition to the number and size of tumoral nodules. In this review, we discuss different published models to assess the risk of recurrent hepatocellular carcinoma after transplantation. Our aim is to refine clinical decisions about prioritization and listing for liver transplantation, to better inform patients and provide an appropriate surveillance strategy to influence their prognosis.

Abstract

Liver transplantation is the preferred therapeutic option for non-resectable hepatocellular carcinoma in early-stage disease. Taking into account the limited number of donor organs, liver transplantation is restricted to candidates with long-term outcomes comparable to benign indications on the waiting list. Introducing the morphometric Milan criteria as the gold standard for transplant eligibility reduced the recurrence rate. Even with strict patient selection, there is a risk of recurrence of between 8 and 20% in the transplanted liver, and this is of even greater importance when using more expanded criteria and downstaging protocols. Currently, it remains challenging to predict the risk of recurrence and the related prognosis for individual patients. In this review, the recurrence-risk-assessment scores proposed in the literature are discussed. Currently there is no consensus on the optimal model or the implications of risk stratification in clinical practice. The most recent scorings include additional biological markers for tumour behavior, such as alfa-foetoprotein, and the response to locoregional therapies, in addition to the number and diameter of tumoral nodules. The refinement of the prediction of recurrence is important to better inform patients, guide decisions about prioritization and listing and implement individualized surveillance strategies. In the future, this might also provide indications for tailored immunosuppressive therapy or inclusion in trials for adjuvant treatment.

Establishment and Validation of an Integrated Model to Predict Postoperative Recurrence in Patients With Atypical Meningioma

Background

This study aims to establish an integrated model based on clinical, laboratory, radiological, and pathological factors to predict the postoperative recurrence of atypical meningioma (AM).

Materials and Methods

A retrospective study of 183 patients with AM was conducted. Patients were randomly divided into a training cohort (n = 128) and an external validation cohort (n = 55). Univariable and multivariable Cox regression analyses, the least absolute shrinkage and selection operator (LASSO) regression analysis, time-dependent receiver operating characteristic (ROC) curve analysis, and evaluation of clinical usage were used to select variables for the final nomogram model.

Results

After multivariable Cox analysis, serum fibrinogen >2.95 g/L (hazard ratio (HR), 2.43; 95% confidence interval (CI), 1.05–5.63; p = 0.039), tumor located in skull base (HR, 6.59; 95% CI, 2.46-17.68; p < 0.001), Simpson grades III–IV (HR, 2.73; 95% CI, 1.01–7.34; p = 0.047), tumor diameter >4.91 cm (HR, 7.10; 95% CI, 2.52–19.95; p < 0.001), and mitotic level ≥4/high power field (HR, 2.80; 95% CI, 1.16–6.74; p = 0.021) were independently associated with AM recurrence. Mitotic level was excluded after LASSO analysis, and it did not improve the predictive performance and clinical usage of the model. Therefore, the other four factors were integrated into the nomogram model, which showed good discrimination abilities in training cohort (C-index, 0.822; 95% CI, 0.759–0.885) and validation cohort (C-index, 0.817; 95% CI, 0.716–0.918) and good match between the predicted and observed probability of recurrence-free survival.

Conclusion

Our study established an integrated model to predict the postoperative recurrence of AM.