Development Of A Radiomics Model Based On Machine Learning To Predict The Positive Expression Of Ck19 And Histopathologic Grade In Hepatocellular Carcinoma Using Preoperative Gadoxetic Acid-Enhanced MRI Images

Radiomics signature based on Gadoxetic acid-enhanced MRI: A potential imaging biomarker for identifying cytokeratin 19 positive hepatocellular carcinoma1.BackgroundThe outcomes of hepatocellular carcinoma(HCC)vary due to different molecular characteristics.One subtype of HCC,with cytokeratin 19 expression(CK19+)has shown to be more aggressive and have a poor prognosis.However,CK19+is determined by immunohistochemical examination using surgically resected specimen.This study aimed to establish a radiomics signature based on preoperative gadoxetic acid-enhanced MRI for predicting CK19 status in HCC.2.MethodsClinicopathological and imaging data were retrospectively collected from patients who underwent hepatectomy between February 2015 and December 2020.Patients who underwent gadoxetic acid-enhanced MRI and had CK19 results of histopathological examination were included.Radiomics features of a manually segmented lesion on the volume of interest during the arterial,portal venous and hepatobiliary phases were extracted.The 10 most reproducible and robust features at each phase were selected for construction of radiomics signatures and their performance were evaluated by analyzing the area under the curve(AUC).To balance the distribution of patient classes,the minority classification was increased by the synthetic minority oversampling technique(SMOTE).The goodness-of-fit of the model was assessed by Hosmer-Lemeshow test.3.ResultsA total of 110 patients were included.The incidence of CK19(+)HCC was 17%(19/110).Alpha fetoprotein(AFP)was the only significant clinicopathological variable different between CK19(-)and CK19(+)groups.A majority of the selected radiomics features were wavelet filter derived features.The AUCs of the three radiomics signatures based on arterial,portal venous and hepatobiliary phases were 0.70(95%CI:0.56-0.83),0.83(95%CI:0.73-0.92),and 0.89(95%CI:0.820.96)respectively.The three radiomics signatures were integrated and the fusion signature yielded an AUC of 0.92(95%CI:0.86-0.98)and was used as the final model for CK19(+)prediction.The sensitivity,specificity,accuracy,positive predictive value and negative predictive value of the fusion signature was 0.84,0.89,0.88,0.62,and 0.96,respectively.Hosmer-Lemeshow test showed a good fit of the fusion signature(p>0.05).4.ConclusionThe established radiomics signature based on preoperative gadoxetic acidenhanced MRI could be an accurate and potential imaging biomarker for HCC CK19(+)prediction.Development of a radiomics model derived from preoperative gadoxetic acid-enhanced MRI for predicting histopathologic grade of hepatocellular carcinoma1.BackgroundHistopathologic grade of hepatocellular carcinoma(HCC)is an important predictor of early recurrence and poor prognosis after curate treatments.This study aimed to develop a radiomics model based on preoperative gadoxetic acid-enhanced MRI for predicting HCC histopathologic grade and to validate its predictive performance in an independent external cohort.2.MethodsClinical and imaging data of 403 consecutive HCC patients were retrospectively collected from two hospitals(265 and 138,respectively).Patients were categorized into poorly differentiated HCC and non-poorly differentiated HCC groups.A total of 851 radiomics features were extracted from the segmented tumor at the hepatobiliary phase images.Three classifiers,Logistic regression(LR),support vector machine,and Adaboost were adopted for modeling.3.ResultsThe area under the curve of the three models were 0.70,0.67,and 0.61 respectively in the external test cohort.Alpha-fetoprotein(AFP)was the only significant clinicopathological variable associated with HCC grading(odds ratio:2.75).When combining AFP,the LR+AFP model showed the best performance,with an AUC of 0.71(95%CI:0.57-0.81)in the external test cohort.4.ConclusionA radiomics model based on gadoxetic acid-enhanced MRI was constructed in this study to discriminate HCC with different histopathologic grades.Its good performance indicates a promise in the preoperative prediction of HCC differentiation levels.