An FDG PET/CT metabolic parameter-based nomogram for predicting the early recurrence of hepatocellular carcinoma after 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.

Metabolic Tumor Volume from 18F-FDG PET/CT in Combination with Radiologic Measurements to Predict Long-Term Survival Following Transplantation for Colorectal Liver Metastases

The aim of the present study is to report on the ability of metabolic tumor volume (MTV) of liver metastases from pre-transplant 18F-FDG PET/CT in combination with conventional radiological measurements from CT scans to predict long-term disease-free survival (DFS), overall survival (OS), and survival after relapse (SAR) after liver transplantation for colorectal liver metastases. The total liver MTV was obtained from the 18F-FDG PET/CT, and the size of the largest metastasis and the total number of metastases were obtained from the CT. DFS, OS, and SAR for patients with a low and high MTV, in combination with a low and high size, number, and tumor burden score, were compared using the Kaplan-Meier method and log-rank test. Patients with a low number of metastases and low MTV had a significantly longer OS than those with a high MTV, with a median survival of 151 vs. 26 months (p = 0.010). Patients with a high number of metastases and low MTV had significantly longer DFS, OS, and SAR than patients with a high MTV (p = 0.034, 0.006, and 0.026). The tumor burden score of group/zone 3, in combination with a low MTV, had a significantly improved DFS, OS, and SAR compared to those with a high MTV (p = 0.034, <0.001, and 0.006). Patients with a low MTV of liver metastases had a long DFS, OS, and SAR despite a high number of liver metastases and a high tumor burden score.

Deep learning nomogram based on Gd-EOB-DTPA MRI for predicting early recurrence in hepatocellular carcinoma after hepatectomy

OBJECTIVES

The accurate prediction of post-hepatectomy early recurrence in patients with hepatocellular carcinoma (HCC) is crucial for decision-making regarding postoperative adjuvant treatment and monitoring. We aimed to explore the feasibility of deep learning (DL) features derived from gadoxetate disodium (Gd-EOB-DTPA) MRI, qualitative features, and clinical variables for predicting early recurrence.

METHODS

In this bicentric study, 285 patients with HCC who underwent Gd-EOB-DTPA MRI before resection were divided into training (n = 195) and validation (n = 90) sets. DL features were extracted from contrast-enhanced MRI images using VGGNet-19. Three feature selection methods and five classification methods were combined for DL signature construction. Subsequently, an mp-MR DL signature fused with multiphase DL signatures of contrast-enhanced images was constructed. Univariate and multivariate logistic regression analyses were used to identify early recurrence risk factors including mp-MR DL signature, microvascular invasion (MVI), and tumor number. A DL nomogram was built by incorporating deep features and significant clinical variables to achieve early recurrence prediction.

RESULTS

MVI (p = 0.039), tumor number (p = 0.001), and mp-MR DL signature (p < 0.001) were independent risk factors for early recurrence. The DL nomogram outperformed the clinical nomogram in the training set (AUC: 0.949 vs. 0.751; p < 0.001) and validation set (AUC: 0.909 vs. 0.715; p = 0.002). Excellent DL nomogram calibration was achieved in both training and validation sets. Decision curve analysis confirmed the clinical usefulness of DL nomogram.

CONCLUSION

The proposed DL nomogram was superior to the clinical nomogram in predicting early recurrence for HCC patients after hepatectomy.

KEY POINTS

• Deep learning signature based on Gd-EOB-DTPA MRI was the predominant independent predictor of early recurrence for hepatocellular carcinoma (HCC) after hepatectomy. • Deep learning nomogram based on clinical factors and Gd-EOB-DTPA MRI features is promising for predicting early recurrence of HCC. • Deep learning nomogram outperformed the conventional clinical nomogram in predicting early recurrence.

Metabolic tumor volume predicts long-term survival after transplantation for unresectable colorectal liver metastases: 15 years of experience from the SECA study

Objective

To report 15 years of experience with metabolic tumor volume (MTV) of liver metastases from the preoperative ¹⁸F-FDG PET/CT to predict long-term survival after liver transplantation (LT) for unresectable colorectal liver metastases (CRLM).

Methods

The preoperative ¹⁸F-FDG PET/CT from all SECA 1 and 2 patients was evaluated. MTV was obtained from all liver metastases. The patients were divided into one group with low MTV (< 70 cm³) and one group with high MTV (> 70 cm³) based on a receiver operating characteristic analysis. Overall survival (OS), disease-free survival (DFS) and post recurrence survival (PRS) for patients with low versus high MTV were compared using the Kaplan–Meier method and log rank test. Clinopathological features between the two groups were compared by a nonparametric Mann–Whitney U test for continuous and Fishers exact test for categorical data.

Results

At total of 40 patients were included. Patients with low MTV had significantly longer OS (p < 0.001), DFS (p < 0.001) and PRS (p = 0.006) compared to patients with high values. The patients with high MTV had higher CEA levels, number of liver metastases, size of the largest liver metastasis, N-stage, number of chemotherapy lines and more frequently progression of disease at LT compared to the patients with low MTV.

Conclusion

MTV of liver metastases is highly predictive of long-term OS, DFS and PRS after LT for unresectable CRLM and should be implemented in risk stratification prior to LT.

CT-Based Radiomics for the Recurrence Prediction of Hepatocellular Carcinoma After Surgical Resection

Purpose

To explore the effectiveness of radiomics signature in predicting the recurrence of hepatocellular carcinoma (HCC) and the benefit of postoperative adjuvant transcatheter arterial chemoembolization (PA-TACE).

Patients and Methods

In this multicenter retrospective study, 364 consecutive patients with multi-phase computed tomography (CT) images were included. Recurrence-related radiomics features of intra- and peritumoral regions were extracted from the pre-contrast, arterial and portal venous phase, respectively. The radiomics model was established in the training cohort (n = 187) using random survival forests analysis to output prediction probability as “Rad-score” and validated by the internal (n = 92) and external validation cohorts (n = 85). Besides, the Clinical nomogram was developed by clinical-radiologic-pathologic characteristics, and the Combined nomogram was further constructed to evaluate the added value of the Rad-score for individualized recurrence-free survival (RFS) prediction, which is our primary and only endpoint. The performance of the three models was assessed by the concordance index (C-index). Furthermore, all the patients were stratified into high- and low-risk groups of recurrence by the median value of the Rad-score to analyze the benefit of PA-TACE.

Results

The model built using radiomics signature demonstrated favorable prediction of HCC recurrence across all datasets, with C-index of 0.892, 0.812, 0.809, separately in the training, the internal and external validation cohorts. Univariate and multivariate analysis revealed that the Rad-score was an independent prognostic factor. Significant differences were found between the high- and low-risk group in RFS prediction in all three cohorts. Further analysis showed that compared with the low-risk group, patients with the high-risk received more benefits from PA-TACE.

Conclusion

The newly developed Rad-score was not only a powerful biomarker in predicting the RFS of HCC but also a strong stratification basis to explore the high-risk patients who could benefit from PA-TACE.

Predicting the Recurrence of Hepatocellular Carcinoma after Primary Living Donor Liver Transplantation Using Metabolic Parameters Obtained from 18F-FDG PET/CT

This study evaluated the prognostic value of metabolic parameters based on the standardized uptake value (SUV) normalized by total body weight (bwSUV) and by lean body mass (SUL) measured on ¹⁸F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) for predicting tumor recurrence after primary living donor liver transplantation (LDLT) in patients with hepatocellular carcinoma (HCC) without transplantation locoregional therapy. This retrospective study enrolled 49 patients with HCC. The maximum tumor bwSUV (T-bwSUVmax) and SUL (T-SULmax) were measured on PET. The maximum bwSUV (L-bwSUVmax), mean bwSUV (L-bwSUVmean), maximum SUL (L-SULmax), and mean SUL (L-SULmean) were measured in the liver. All metabolic parameters were evaluated using survival analyses and compared to clinicopathological factors. Tumor recurrence occurred in 16/49 patients. Kaplan–Meier analysis revealed that all metabolic parameters were significant (p < 0.05). Univariate analysis revealed that prothrombin-induced by vitamin K absence or antagonist-II; T-stage; tumor number; tumor size; microvascular invasion; the Milan criteria, University of California, San Francisco (UCSF), and up-to-seven criteria; T-bwSUVmax/L-bwSUVmean; T-SULmax; T-SULmax/L-SULmax; and T-SULmax/L-SULmean were significant predictors. Multivariate analysis revealed that the T-SULmax/L-SULmean (hazard ratio = 115.6; p = 0.001; cut-off, 1.81) and UCSF criteria (hazard ratio = 172.1; p = 0.010) were independent predictors of tumor recurrence. SUL-based metabolic parameters, especially T-SULmax/L-SULmean, were significant, independent predictors of HCC recurrence post-LDLT.