Radiomics Analysis of MR Imaging with Gd-EOB-DTPA for Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma: Investigation and Comparison of Different Hepatobiliary Phase Delay Times

Abstract

Purpose. To investigate whether the radiomics analysis of MR imaging in the hepatobiliary phase (HBP) can be used to predict microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC). Method. A total of 130 patients with HCC, including 80 MVI-positive patients and 50 MVI-negative patients, who underwent MR imaging with Gd-EOB-DTPA were enrolled. Least absolute shrinkage and selection operator (LASSO) regression was applied to select radiomics parameters derived from MR images obtained in the HBP 5 min, 10 min, and 15 min images. The selected features at each phase were adopted into support vector machine (SVM) classifiers to establish models. Multiple comparisons of the AUCs at each phase were performed by the Delong test. The decision curve analysis (DCA) was used to analyze the classification of MVI-positive and MVI-negative patients. Results. The most predictive features between MVI-positive and MVI-negative patients included 9, 8, and 14 radiomics parameters on HBP 5 min, 10 min, and 15 min images, respectively. A model incorporating the selected features produced an AUC of 0.685, 0.718, and 0.795 on HBP 5 min, 10 min, and 15 min images, respectively. The predictive model for HBP 5 min, 10 min and 15 min showed no significant difference by the Delong test. DCA indicated that the predictive model for HBP 15 min outperformed the models for HBP 5 min and 10 min. Conclusions. Radiomics parameters in the HBP can be used to predict MVI, with the HBP 15 min model having the best differential diagnosis ability.

Radiomics Models for Predicting Microvascular Invasion in Hepatocellular Carcinoma: A Systematic Review and Radiomics Quality Score Assessment

“…The systematic literature search initially yielded 188 records from the four databases. After removing 82 duplicates, 50 inappropriate types of publications, and 34 ineligible studies, a total of 22 studies were included in this systematic review [ 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 ] ( Figure 2 ).…”

“…It is worth mentioning that the reproducibility evaluation of imaging features can also be used for feature selection. Among the 10 studies that performed interclass correlation coefficient (ICC) analysis, 4 of them set a threshold of 0.8 for robust features and selected those for further analysis [ 32 , 38 , 40 , 41 ].…”

Peritumoral Dilation Radiomics of Gadoxetate Disodium-Enhanced MRI Excellently Predicts Early Recurrence of Hepatocellular Carcinoma without Macrovascular Invasion After Hepatectomy

“…Although the integrated nomogram (Figure 5; AUC 0.914, 95% CI 0.880-0.947) slightly excelled the radiomics algorithm (AUC 0.909, 95% CI 0.875-0.943) in all population, we still regarded the peritumoral dilation radiomics as the ultimate prediction model of early recurrence. Apart from our radiomics findings, Table 4 also summarizes the comparison of previous radiomics studies based on Gd-EOB-DTPA MRI 15,[27][28][29][43][44][45][46] for predicting MVI or prognosis, as well as that of peritumoral dilation radiomics researches conducted by CT or MRI without hepatobiliary-specific agent for stratifying outcomes.…”

A Radiomics Model Based on Gd-EOB-DTPA-Enhanced MRI for the Prediction of Microvascular Invasion in Solitary Hepatocellular Carcinoma ≤ 5 cm

“…extracted imaging features only from the HBP of Gd-EOB-DTPA-enhanced MRI and constructed a radiomics model showing an AUC of 0.83 in the test cohort, higher than ours (0.83 vs. 0.62) ( 25 ). Another research also explored radiomics features on solely HBP images and constructed a prediction model with an AUC of 0.8 ( 26 ).…”