Radiomics model based on contrast-enhanced computed tomography to predict early recurrence in patients with hepatocellular carcinoma after radical resection.

BACKGROUND: Radical resection remains an effective strategy for patients with hepatocellular carcinoma (HCC). Unfortunately, the postoperative early recurrence (recurrence within 2 years) rate is still high. AIM: To develop a radiomics model based on preoperative contrast-enhanced computed tomography (CECT) to evaluate early recurrence in HCC patients with a single tumour. METHODS: We enrolled a total of 402 HCC patients from two centres who were diagnosed with a single tumour and underwent radical resection. First, the features from the portal venous and arterial phases of CECT were extracted based on the region of interest, and the early recurrence-related radiomics features were selected via the least absolute shrinkage and selection operator proportional hazards model (LASSO Cox) to determine radiomics scores for each patient. Then, the clinicopathologic data were combined to develop a model to predict early recurrence by Cox regression. Finally, we evaluated the prediction performance of this model by multiple methods. RESULTS: A total of 1915 radiomics features were extracted from CECT images, and 31 of them were used to determine the radiomics scores, which showed a significant difference between the early recurrence and nonearly recurrence groups. Univariate and multivariate Cox regression analyses showed that radiomics scores and serum alpha-fetoprotein were independent indicators, and they were used to develop a combined model to predict early recurrence. The area under the receiver operating characteristic curve values for the training and validation cohorts were 0.77 and 0.74, respectively, while the C-indices were 0.712 and 0.674, respectively. The calibration curves and decision curve analysis showed satisfactory accuracy and clinical utilities. Kaplan-Meier curves based on recurrence-free survival and overall survival showed significant differences. CONCLUSION: The preoperative radiomics model was shown to be effective for predicting early recurrence among HCC patients with a single tumour.

Novel index for the prediction of significant liver fibrosis and cirrhosis in chronic hepatitis B patients in China.

BACKGROUND: Noninvasive, practical, and convenient means of detection for the prediction of liver fibrosis and cirrhosis in China are greatly needed. AIM: To develop a precise noninvasive test to stage liver fibrosis and cirrhosis. METHODS: With liver biopsy as the gold standard, we established a new index, [alkaline phosphatase (U/L) + gamma-glutamyl transpeptidase (U/L)/platelet (109/L) (AGPR)], to predict liver fibrosis and cirrhosis. In addition, we compared the area under the receiver operating characteristic curve (AUROC) of AGPR, gamma-glutamyl transpeptidase to platelet ratio, aspartate transaminase to platelet ratio index, and FIB-4 and evaluated the accuracy of these routine laboratory indices in predicting liver fibrosis and cirrhosis. RESULTS: Correlation analysis revealed a significant positive correlation between AGPR and liver fibrosis stage (P < 0.001). In the training cohort, the AUROC of AGPR was 0.83 (95%CI: 0.78-0.87) for predicting fibrosis (≥ F2), 0.84 (95%CI: 0.79-0.88) for predicting extensive fibrosis (≥ F3), and 0.87 (95%CI: 0.83-0.91) for predicting cirrhosis (F4). In the validation cohort, the AUROCs of AGPR to predict ≥ F2, ≥ F3 and F4 were 0.83 (95%CI: 0.77-0.88), 0.83 (95%CI: 0.77-0.89), and 0.84 (95%CI: 0.78-0.89), respectively. CONCLUSION: The AGPR index should become a new, simple, accurate, and noninvasive marker to predict liver fibrosis and cirrhosis in chronic hepatitis B patients.

Preoperative contrast-enhanced computed tomography-based radiomics model for overall survival prediction in hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) is the most common primary liver malignancy with a rising incidence worldwide. The prognosis of HCC patients after radical resection remains poor. Radiomics is a novel machine learning method that extracts quantitative features from medical images and provides predictive information of cancer, which can assist with cancer diagnosis, therapeutic decision-making and prognosis improvement. AIM: To develop and validate a contrast-enhanced computed tomography-based radiomics model for predicting the overall survival (OS) of HCC patients after radical hepatectomy. METHODS: A total of 150 HCC patients were randomly divided into a training cohort (n = 107) and a validation cohort (n = 43). Radiomics features were extracted from the entire tumour lesion. The least absolute shrinkage and selection operator algorithm was applied for the selection of radiomics features and the construction of the radiomics signature. Univariate and multivariate Cox regression analyses were used to identify the independent prognostic factors and develop the predictive nomogram, incorporating clinicopathological characteristics and the radiomics signature. The accuracy of the nomogram was assessed with the concordance index, receiver operating characteristic (ROC) curve and calibration curve. The clinical utility was evaluated by decision curve analysis (DCA). Kaplan-Meier methodology was used to compare the survival between the low- and high-risk subgroups. RESULTS: In total, seven radiomics features were selected to construct the radiomics signature. According to the results of univariate and multivariate Cox regression analyses, alpha-fetoprotein (AFP), neutrophil-to-lymphocyte ratio (NLR) and radiomics signature were included to build the nomogram. The C-indices of the nomogram in the training and validation cohorts were 0.736 and 0.774, respectively. ROC curve analysis for predicting 1-, 3-, and 5-year OS confirmed satisfactory accuracy [training cohort, area under the curve (AUC) = 0.850, 0.791 and 0.823, respectively; validation cohort, AUC = 0.905, 0.884 and 0.911, respectively]. The calibration curve analysis indicated a good agreement between the nomogram-prediction and actual survival. DCA curves suggested that the nomogram had more benefit than traditional staging system models. Kaplan-Meier survival analysis indicated that patients in the low-risk group had longer OS and disease-free survival (all P < 0.0001). CONCLUSION: The nomogram containing the radiomics signature, NLR and AFP is a reliable tool for predicting the OS of HCC patients.