International Journal of Radiation Research

en Machine learning-based radiomics for preoperative prediction of microvascular invasion in hepatocellular carcinoma Radiation Biology Radiation Biology تحقيق بديع Original Research <div style="text-align: justify;">Background: Background: To evaluate the diagnostic performance of different radiomics models for preoperatively predicting microvascular invasion (MVI) of hepatocellular carcinoma (HCC). Materials and Methods: A total of 124 patients who had histologically confirmed HCC (training dataset: n=86; validation dataset: n=38) were included. Clinical factors (CFs) were extracted from medical data. Radiomics features were derived from the unenhanced phase, artery phase (AP), portal venous phase and delay phase CT images. The least absolute shrinkage and selection operator (LASSO) method was chosen to select the radiomics feature. Twelve models were established using three modeling methods (logistic regression [LR], support vector machine [SVM], and Bayes) with the radiomics signature. Receiver operating characteristic (ROC) analysis was used to evaluate the diagnostic performance of the models. A radiomics signature that performs well was integrated with the clinical factors into a combined model. Results: AP radiomics signatures achieved the best efficiency than other radiomics models. The LR model with the AP radiomics signature as the input factor showed better performance than the SVM and Bayes models, with an area under the curve (AUC) of 0.848 in the validation datasets. When integrating the AP radiomics signatures and clinical factors, the combined model performed better and reached an AUC of 0.875 in the validation datasets. Conclusions: The radiomics model demonstrated excellent performance for preoperatively predicting MVI in patients with HCC, especially the combined model. Different modeling methods could influence the effects of the diagnostic performance.</div> Microvascular invasion, radiomics, hepatocellular carcinoma, machine learning. 383 390 http://ijrr.com/browse.php?a_code=A-10-1-1056&slc_lang=en&sid=1 L. Xiong 7900319475328460024069 7900319475328460024069 No Department of Medical Imaging Center, The Second Affiliated Hospital of Nanchang University, No. 1 Minde Road, Nanchang, Jiangxi, 330006, China Y. Peng 7900319475328460024070 7900319475328460024070 No Department of Medical Imaging Center, The Second Affiliated Hospital of Nanchang University, No. 1 Minde Road, Nanchang, Jiangxi, 330006, China Sh. Li 7900319475328460024071 7900319475328460024071 No Department of Medical Imaging Center, The Second Affiliated Hospital of Nanchang University, No. 1 Minde Road, Nanchang, Jiangxi, 330006, China X. Tang 7900319475328460024072 7900319475328460024072 No Department of Medical Imaging Center, The Second Affiliated Hospital of Nanchang University, No. 1 Minde Road, Nanchang, Jiangxi, 330006, China J. Zhou 7900319475328460024073 7900319475328460024073 No Department of Medical Imaging Center, The Second Affiliated Hospital of Nanchang University, No. 1 Minde Road, Nanchang, Jiangxi, 330006, China L. Gong gong111999@126.com 7900319475328460024074 7900319475328460024074 Yes Department of Medical Imaging Center, The Second Affiliated Hospital of Nanchang University, No. 1 Minde Road, Nanchang, Jiangxi, 330006, China