Quantitative analysis of multiphase magnetic resonance images may assist prediction of histopathological grade of small hepatocellular carcinoma

Development and Comparison of Prediction Models Based on Sonovue- and Sonazoid-Enhanced Ultrasound for Pathologic Grade and Microvascular Invasion in Hepatocellular Carcinoma

OBJECTIVE

This study was aimed at developing and comparing prediction models based on Sonovue and Sonazoid contrast-enhanced ultrasound (CEUS) in predicting pathologic grade and microvascular invasion (MVI) of hepatocellular carcinoma (HCC). Also investigated was whether Kupffer phase images have additional predictive value for the above pathologic features.

METHODS

Ninety patients diagnosed with primary HCC who had undergone curative hepatectomy were prospectively enrolled. All patients underwent conventional ultrasound (CUS), Sonovue-CEUS and Sonazoid-CEUS examinations pre-operatively. Clinical, radiologic and pathologic features including pathologic grade, MVI and CD68 expression were collected. We developed prediction models comprising clinical, CUS and CEUS (Sonovue and Sonazoid, respectively) features for pathologic grade and MVI with both the logistic regression and machine learning (ML) methods.

RESULTS

Forty-one patients (45.6%) had poorly differentiated HCC (p-HCC) and 37 (41.1%) were MVI positive. For pathologic grade, the logistic model based on Sonazoid-CEUS had significantly better performance than that based on Sonovue-CEUS (area under the curve [AUC], 0.929 vs. 0.848, p = 0.035), whereas for MVI, these two models had similar accuracy (AUC, 0.810 vs. 0.786, p = 0.068). Meanwhile, we found that well-differentiated HCC tended to have a higher enhancement ratio in 6-12 min during the Kupffer phase of Sonazoid-CEUS, as well as higher CD68 expression compared with p-HCC. In addition, all of these models can effectively predict the risk of recurrence (p < 0.05).

CONCLUSION

Sonovue-CEUS and Sonazoid-CEUS were comparably excellent in predicting MVI, while Sonazoid-CEUS was superior to Sonovue-CEUS in predicting pathologic grade because of the Kupffer phase. The enhancement ratio in the Kupffer phase has additional predictive value for pathologic grade prediction.

Hepatocellular carcinoma pathologic grade prediction using radiomics and machine learning models of gadoxetic acid-enhanced MRI: a two-center study

PURPOSE

To develop a radiomics-based hepatocellular carcinoma (HCC) grade classifier model based on data from gadoxetic acid-enhanced MRI.

METHODS

This retrospective study included 137 patients who underwent hepatectomy for a single HCC and gadoxetic acid-enhanced MRI within 60 days before surgery. HCC grade was categorized as low or high (modified Edmondson-Steiner grade I-II vs. III-IV). We used the hepatobiliary phase (HBP), portal venous phase, T2-weighted image(T2WI), and T1-weighted image(T1WI). From the volume of interest in HCC, 833 radiomic features were extracted. Radiomic and clinical features were selected using a random forest regressor, and the classification model was trained and validated using a random forest classifier and tenfold stratified cross-validation. Eight models were developed using the radiomic features alone or by combining the radiomic and clinical features. Models were validated with internal enrolled data (internal validation) and a dataset (28 patients) at a separate institution (external validation). The area under the curve (AUC) of the validation results was compared using the DeLong test.

RESULTS

In internal and external validation, the HBP radiomics-only model showed the highest AUC (internal 0.80 ± 0.09, external 0.70 ± 0.09). In external validation, all models showed lower AUC than those for internal validation, while the T2WI and T1WI models failed to predict the HCC grade (AUC 0.30-0.58) in contrast to the internal validation results (AUC 0.67-0.78).

CONCLUSION

The radiomics-based machine learning model from gadoxetic acid-enhanced liver MRI could distinguish between low- and high-grade HCCs. The radiomics-only HBP model showed the best AUC among the eight models, good performance in internal validation, and fair performance in external validation.

Prediction for Aggressiveness and Postoperative Recurrence of Hepatocellular Carcinoma Using Gadoxetic Acid-Enhanced Magnetic Resonance Imaging

RATIONALE AND OBJECTIVES

To investigate the predictive value of gadoxetic acid-enhanced magnetic resonance imaging (MRI) features on the pathologic grade, microvascular invasion (MVI), and cytokeratin-19 (CK19) expression in hepatocellular carcinomas (HCC), and to evaluate their association with postoperative recurrence of HCC.

MATERIALS AND METHODS

This retrospective study included 147 patients with surgically confirmed HCCs who underwent gadoxetic-enhanced MRI. The lesions were evaluated quantitatively in terms of the relative enhancement ratio (RER), and qualitatively based on imaging features and clinical parameters. Logistic regression analyses were performed to investigate the value of these parameters in predicting the pathologic grade, MVI, and CK19 in HCC. Predictive factors for postoperative recurrence were determined using a Cox proportional hazards model.

RESULTS

Peritumoral enhancement (odds ratio [OR], 3.396; p = 0.025) was an independent predictor of high pathologic grades. Serum protein induced by vitamin K absence or antagonist (PIVKA) level > 40 mAU/mL (OR, 3.763; p = 0.018) and peritumoral hypointensity (OR, 4.343; p = 0.003) were independent predictors of MVI. Predictors of CK19 included serum alpha-fetoprotein (AFP) level > 400 ng/mL (OR, 4.576; p = 0.005), rim enhancement (OR, 5.493; p = 0.024), and lower RER (OR, 0.013; p = 0.011). Peritumoral hypointensity (hazard ratio [HR], 1.957; p = 0.027) and poor pathologic grades (HR, 2.339; p = 0.043) were independent predictors of recurrence.

CONCLUSION

We demonstrated the value of preoperative gadoxetic-enhanced MRI in predicting aggressive pathological features of HCC. Poor pathologic grades and peritumoral hypointensity may independently predict the recurrence of HCC.