Development and Validation of Prognostic Nomograms Predicting Recurrence and Survival in Patients with Solitary Hepatocellular Carcinoma Following Curative Liver Resection

Background: This work was designed to establish and verify our nomograms integrating clinicopathological characteristics with hematological biomarkers to predict both disease-free survival (DFS) and overall survival (OS) in solitary hepatocellular carcinoma (HCC) patients following hepatectomy.Methods: We scrutinized the data retrospectively from 414 patients with a clinicopathological diagnosis of solitary HCC from Guangxi Medical University Cancer Hospital (Nanning, China) between January 2004 and December 2012. Following the random separation of the samples in a 7:3 ratio into the training set and validation set, the former set was assessed by Cox regression analysis to develop two nomograms to predict the 1-year and 3-year DFS and OS (3-years and 5-years). This was followed by discrimination and calibration estimation employing Harrell’s C-index (C-index) and calibration curves, while the internal validation was also assessed.Results: In the training cohort, the tumor diameter, tumor capsule, macrovascular invasion, and alpha-fetoprotein (AFP) were included in the DFS nomogram. Age, tumor diameter, tumor capsule, macrovascular invasion, microvascular invasion, and aspartate aminotransferase (AST) were included in the OS nomogram. The C-index was 0.691 (95% CI: 0.644-0.738) for the DFS-nomogram and 0.713 (95% CI: 0.670-0.756) for the OS-nomogram. The survival probability calibration curves displayed a fine agreement between the predicted and observed ranges in both data sets. Conclusion: Our nomograms combined clinicopathological features with hematological biomarkers to emerge effective in predicting the DFS and OS in solitary HCC patients following curative liver resection. Therefore, the potential utility of our nomograms for guiding individualized treatment clinically and monitor the recurrence monitoring in these patients.

Radiomic Features of Multi-ROI and Multi-Phase MRI for the Prediction of Microvascular Invasion in Solitary Hepatocellular Carcinoma

ObjectivesTo develop and validate an MR radiomics-based nomogram to predict the presence of MVI in patients with solitary HCC and further evaluate the performance of predictors for MVI in subgroups (HCC ≤ 3 cm and > 3 cm).Materials and MethodsBetween May 2015 and October 2020, 201 patients with solitary HCC were analysed. Radiomic features were extracted from precontrast T1WI, arterial phase, portal venous phase, delayed phase and hepatobiliary phase images in regions of the intratumoral, peritumoral and their combining areas. The mRMR and LASSO algorithms were used to select radiomic features related to MVI. Clinicoradiological factors were selected by using backward stepwise regression with AIC. A nomogram was developed by incorporating the clinicoradiological factors and radiomics signature. In addition, the radiomic features and clinicoradiological factors related to MVI were separately evaluated in the subgroups (HCC ≤ 3 cm and > 3 cm).ResultsHistopathological examinations confirmed MVI in 111 of the 201 patients (55.22%). The radiomics signature showed a favourable discriminatory ability for MVI in the training set (AUC, 0.896) and validation set (AUC, 0.788). The nomogram incorporating peritumoral enhancement, tumour growth type and radiomics signature showed good discrimination in the training (AUC, 0.932) and validation sets (AUC, 0.917) and achieved well-fitted calibration curves. Subgroup analysis showed that tumour growth type was a predictor for MVI in the HCC ≤ 3 cm cohort and peritumoral enhancement in the HCC > 3 cm cohort; radiomic features related to MVI varied between the HCC ≤ 3 cm and HCC > 3 cm cohort. The performance of the radiomics signature improved noticeably in both the HCC ≤ 3 cm (AUC, 0.953) and HCC > 3 cm cohorts (AUC, 0.993) compared to the original training set.ConclusionsThe preoperative nomogram integrating clinicoradiological risk factors and the MR radiomics signature showed favourable predictive efficiency for predicting MVI in patients with solitary HCC. The clinicoradiological factors and radiomic features related to MVI varied between subgroups (HCC ≤ 3 cm and > 3 cm). The performance of radiomics signature for MVI prediction was improved in both the subgroups.