Radiomic Analysis Applied to Pretreatment Gd-EOB-DTPA-enhanced MR Imaging Predicts Response to Selective
Yukichi Tanahashi1, Takanobu Ikeda1, Koh Kubota1
1Department of Radiology, Hamamatsu University School of Medicine.
Purpose:
To evaluate the efficacy of radiomic analysis applied to pretreatment gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced MRI (Gd-EOB-DTPA-MRI) for predicting the response to transcatheter arterial chemoembolization (TACE) for hepatocellular carcinoma.
Methods:
Data and images from 40 consecutive patients (28 men, 12 women) who underwent pretreatment Gd-EOB-DTPA-MRI and a total of 52 TACE procedures for 75 non-treated hepatocellular carcinomas were retrospectively analyzed. Two radiologists manually outlined lesions on pretreatment arterial- and hepatobiliary-phase hepatic images to extract radiomic features. The radiomics data from one observer were randomly divided into a training dataset and a validation dataset in the ratio of 7:3. Radiomic features extracted using least absolute shrinkage and selection operator (LASSO) binomial regression applied to the training dataset and that showed intraclass correlation coefficients (ICC) >0.7 were used to construct a radiomic model. The predictive performance of the model was evaluated using receiver operating characteristics curves. Lesions classified as showing a complete or partial response according to the modified RECIST criteria were allocated to a response group.
Results:
There was no significant difference in Child-Pugh score, tumor marker values, or TACE procedure between response and non-response groups. Six radiomic features were selected using the LASSO binomial regression and 5 of them showing an ICC >0.7 were used to establish the radiomic model. The area under the curve of the radiomic model was 0.89 for the training dataset, 0.83 for the validation dataset, and 0.83 for the other observer's data. The sensitivity and specificity for the prediction of tumor response to TACE were 78% and 92% for the training dataset; 71% and 50% for the validation dataset; and 75% and 79% for the other observer's data.
Conclusion:
The pretreatment Gd-EOB-DTPA-MRI-based radiomic model is useful for predicting the response to TACE of hepatocellular carcinoma.
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