Classification of Hepatic Nodules Using an Improved WOA-SVM Radiomics Model

The incidence and mortality of liver cancer in China are not optimistic. Early diagnosis and treatment have become the urgent means to solve this situation. To develop an improved radiomics model for the classification of hepatic nodules based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). The DCE-MRI images of 30 hepatitis, 30 cirrhotic nodules (CN), 30 dysplastic nodules (DN), and 30 hepatocellular carcinoma (HCC) patients were retrospectively and randomly divided into training and testing datasets in a 7:3 ratio. Firstly, the radiomics features of lesions were extracted by using feature extractor module based on Pyradiomics, from which optimal features were selected by least absolute shrinkage and selection operator (LASSO). Then, the improved whale optimization algorithm (WOA) with Tent mapping, Adaptive weight, and Levy flight (TALWOA) was used for parameter optimization of support vector machines (SVM). Finally, TALWOA-SVM was employed for the four-class classification of hepatic nodules. Receiver operating characteristic (ROC) curves, area under curve (AUC), and F1-score were used to evaluate the performance of the TALWOA-SVM model. Forty-four most informative features were selected from 851 features to train the SVM classifier. Compared with the standard whale algorithm and other optimization algorithms, the optimized model proposed in this paper has highest classification accuracy (81.315%), the ROC of each category being closer to the top left corner with AUC were 0.9378 (95% CI: 0.893–0.981), 0.9223 (95% CI: 0.873–0.971), 0.9794 (0.958–1.000), 0.9872 (0.971–1.000). The model proposed in this study can better classify hepatic nodules in different periods, and is expected to provide help for the early diagnosis of liver cancer.