Preoperative Prediction of Microvascular Invasion in Patients With Hepatocellular Carcinoma Based on Radiomics Nomogram Using Contrast-Enhanced Ultrasound

Abstract

Purpose: This study aimed to develop a radiomics nomogram based on contrast-enhanced ultrasound (CEUS) for preoperatively assessing microvascular invasion (MVI) in hepatocellular carcinoma (HCC) patients.

Methods: A retrospective dataset of 313 HCC patients who underwent CEUS between September 20, 2016 and March 20, 2020 was enrolled in our study. The study population was randomly grouped as a primary dataset of 192 patients and a validation dataset of 121 patients. Radiomics features were extracted from the B-mode (BM), artery phase (AP), portal venous phase (PVP), and delay phase (DP) images of preoperatively acquired CEUS of each patient. After feature selection, the BM, AP, PVP, and DP radiomics scores (Rad-score) were constructed from the primary dataset. The four radiomics scores and clinical factors were used for multivariate logistic regression analysis, and a radiomics nomogram was then developed. We also built a preoperative clinical prediction model for comparison. The performance of the radiomics nomogram was evaluated via calibration, discrimination, and clinical usefulness.

Results: Multivariate analysis indicated that the PVP and DP Rad-score, tumor size, and AFP (alpha-fetoprotein) level were independent risk predictors associated with MVI. The radiomics nomogram incorporating these four predictors revealed a superior discrimination to the clinical model (based on tumor size and AFP level) in the primary dataset (AUC: 0.849 vs. 0.690; p < 0.001) and validation dataset (AUC: 0.788 vs. 0.661; p = 0.008), with a good calibration. Decision curve analysis also confirmed that the radiomics nomogram was clinically useful. Furthermore, the significant improvement of net reclassification index (NRI) and integrated discriminatory improvement (IDI) implied that the PVP and DP radiomics signatures may be very useful biomarkers for MVI prediction in HCC.

Conclusion: The CEUS-based radiomics nomogram showed a favorable predictive value for the preoperative identification of MVI in HCC patients and could guide a more appropriate surgical planning.

Keywords: microvascular invasion; contrast-enhanced ultrasound; hepatocellular carcinoma; nomogram; radiomics.

Figure 1

Study flowchart of radiomics nomogram modeling for the MVI status prediction in patients with HCC and radiomics workflow. BM, B-mode; AP, arterial phase; PVP, portal venous phase; DP, delay phase; Rad-score, radiomics score; mRMR, minimum redundancy maximum relevance; LASSO, least absolute shrinkage and selection operator.

Figure 2

(A) A radiomics nomogram combining the AFP level, tumor size, PVP Rad-score, and DP Rad-score. (B) Calibration curves of the radiomics nomogram in the primary and validation datasets. AFP, α-fetoprotein; PVP, portal venous phase; DP, delay phase; Rad-score, radiomics score.

Figure 3

Performance of the radiomics nomogram in predicting the MVI status of patients with hepatocellular carcinoma. Differential diagnosis of MVI+ and MVI− groups with the cut-off value of Nom-score of 0.452 in the primary (A) and validation (C) datasets. Receiver operating characteristic curves of the radiomics nomogram and clinical model in the primary (B) and validation (D) datasets. MVI+, patients with microvascular invasion; MVI−, patients without microvascular invasion.

Figure 4

Decision curve analysis (DCA) of the radiomics nomogram and clinical model in predicting the MVI status for hepatocellular carcinoma derived from the all 313 patients.

Figure 5

The risk-classification performance of the radiomics nomogram in all 313 patients. MVI+, patients with microvascular invasion; MVI−, patients without microvascular invasion.