Background: High early recurrence (ER) of hepatocellular carcinoma (HCC) after microwave ablation (MWA) represents a sign of aggressive behavior and severely worsens prognosis. The aim of this study was to estimate the outcome of HCC following MWA and develop a response algorithmic strategy based on multiparametric MRI and clinical variables.

Methods: In this retrospective study, we reviewed the records of 339 patients (mean age, 62 ± 12 years; 106 men) treated with percutaneous MWA for HCC between January 2014 and December 2017 that were evaluated by multiparametric MRI. These patients were randomly split into a development and an internal validation group (3:1). Logistic regression analysis was used to screen imaging features. Multivariate Cox regression analysis was then performed to determine predictors of ER (within 2 years) of MWA. The response algorithmic strategy to predict ER was developed and validated using these data sets. ER rates were also evaluated by Kaplan-Meier analysis.

Results: Based on logistic regression analyses, we established an image response algorithm integrating ill-defined margins, lack of capsule enhancement, pre-ablative ADC, ΔADC, and EADC to calculate recurrence scores and define the risk of ER. In a multivariate Cox regression model, the independent risk factors of ER (p < 0.05) were minimal ablative margin (MAM) (HR 0.57; 95% CI 0.35 - 0.95; p < 0.001), the recurrence score (HR: 9.25; 95% CI 4.25 - 16.56; p = 0.021), and tumor size (HR 6.21; 95% CI 1.25 - 10.82; p = 0.014). Combining MAM and tumor size, the recurrence score calculated by the response algorithmic strategy provided predictive accuracy of 93.5%, with sensitivity of 92.3% and specificity of 83.1%. Kaplan-Meier estimates of the rates of ER in the low-risk and high-risk groups were 6.8% (95% CI 4.0 - 9.6) and 30.5% (95% CI 23.6 - 37.4), respectively.

Conclusion: A response algorithmic strategy based on multiparametric MRI and clinical variables was useful for predicting the ER of HCC after MWA.

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9429304PMC
http://dx.doi.org/10.1186/s40644-022-00471-5DOI Listing

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