Development of a magnetic resonance imaging-derived radiomics model to predict microvascular invasion in patients with hepatocellular carcinoma

Abstract

Background Hepatocellular carcinoma (HCC) with microvascular invasion (MVI) has a poor prognosis, is prone to recurrence and metastasis, and requires more complex surgical techniques. Radiomics is expected to enhance the discriminative performance for identifying HCC, but the current radiomics models are becoming increasingly complex, tedious, and difficult to integrate into clinical practice. The purpose of this study was to investigate whether a simple prediction model using noncontrast-enhanced T2-weighted magnetic resonance imaging (MRI) could preoperatively predict MVI in HCC. Methods A total of 104 patients with pathologically confirmed HCC (training cohort, n=72; test cohort, n=32; ratio, about 7:3) who underwent liver MRI within 2 months prior to surgery were retrospectively included. A total of 851 tumor-specific radiomic features were extracted on T2-weighted imaging (T2WI) for each patient using AK software (Artificial Intelligence Kit Version; V. 3.2.0R, GE Healthcare). Univariate logistic regression and least absolute shrinkage and selection operator (LASSO) regression were used in the training cohort for feature selection. The selected features were incorporated into a multivariate logistic regression model to predict MVI, which was validated in the test cohort. The model’s effectiveness was evaluated using the receiver operating characteristic and calibration curves in the test cohort. Results Eight radiomic features were identified to establish a prediction model. In the training cohort, the area under the curve, accuracy, specificity, sensitivity, and positive and negative predictive values of the model for predicting MVI were 0.867, 72.7%, 84.2%, 64.7%, 72.7%, and 78.6%, respectively; while in the test cohort, they were 0.820, 75%, 70.6%, 73.3%, 75%, and 68.8%, respectively. The calibration curves displayed good consistency between the prediction of MVI by the model and actual pathological results in both the training and validation cohorts. Conclusions A prediction model using radiomic features from single T2WI can predict MVI in HCC. This model has the potential to be a simple and fast method to provide objective information for decision-making during clinical treatment.

Prediction of glypican-3 expression in hepatocellular carcinoma using multisequence magnetic resonance imaging-based histology nomograms

“…Radiomics is beneficial for addressing tumor heterogeneity, as it can enhance traditional qualitative diagnosis in conventional imaging by compiling pixel-level data profiles from disease images. This allows for the mapping of the differential information that is imperceptible to the human eye ( 24 - 26 ). Most of the features in the prediction model that we constructed were processed using a local binary mode, with which each voxel was compared with its neighboring voxels, and the resulting comparison was saved as a binary number for the quantification of tumor heterogeneity characteristics.…”

Combination treatment of transcatheter arterial chemoembolization, intensity-modulated radiotherapy, and sorafenib for hepatocellular carcinoma with macrovascular invasion

“…This includes remarkable remission rates, durable local control, and extended survival times, whether applied alone or in combination with systemic treatments. [8][9][10][11][12][13] Specifically, unresectable HCC patients with MVI, possessing good liver function and well-developed periportal collateral circulation, may achieve substantial clinical benefits from TACE or combined TACE plus RT treatment. Moreover, the administration of sorafenib in conjunction with TACE and/or RT has demonstrated encouraging clinical outcomes for patients with locally advanced HCC.…”