Performance of Radiomics in Microvascular Invasion Risk Stratification and Prognostic Assessment in Hepatocellular Carcinoma: A Meta-Analysis

Abstract

Background: Primary liver cancer is the sixth most commonly diagnosed cancer and the third leading cause of cancer death. Advances in phenomenal imaging are paving the way for application in diagnosis and research. The poor prognosis of advanced HCC warrants a personalized approach. The objective was to assess the value of imaging phenomics for risk stratification and prognostication of HCC.

Methods: We performed a meta-analysis of manuscripts published to January 2023 on MEDLINE addressing the value of imaging phenomics for HCC risk stratification and prognostication. Publication information for each were collected using a standardized data extraction form.

Results: Twenty-seven articles were analyzed. Our study shows the importance of imaging phenomics in HCC MVI prediction. When the training and validation datasets were analyzed separately by the random-effects model, in the training datasets, radiomics had good MVI prediction (AUC of 0.81 (95% CI 0.76-0.86)). Similar results were found in the validation datasets (AUC of 0.79 (95% CI 0.72-0.85)). Using the fixed effects model, the mean AUC of all datasets was 0.80 (95% CI 0.76-0.84).

Conclusions: Imaging phenomics is an effective solution to predict microvascular invasion risk, prognosis, and treatment response in patients with HCC.

Keywords: hepatocellular carcinoma; imaging phenomics; radiomics; risk stratification and prognostication.

Figure 1

Flow diagram of the study selection process.

Figure 2

Funnel plots: Individual residual post-modeling on the x-axis against the corresponding standard errors (y-axis, in decreasing order) (A) and sampling variance or standard error (B). NB. In the absence of publication bias and heterogeneity, the points form a funnel shape, with the majority of the points falling inside of the pseudo-confidence region with bounds θ ± 1.96SE, where θ is the estimated effect or outcome based on the fixed-effects model and SE is the standard error value from the y-axis.

Figure 3

Plot of the influence diagnostics. (A) Plot of the externally standardized residuals as a function of each of the studies. Potential influence point. (B) Plot of the leave-one-out estimates of the amount of heterogeneity (a) and leave-one-out values of the test statistics for heterogeneity (b) as a function of each of the studies (optimal value = 100). Potential influence point.

Figure 4

QQ plots for the effect normality assumption (mixed-effects models).

Figure 5

Radiomics prediction of microvascular invasion using mixed-effects modeling [13,33,35,36,37,42,43].