In Academic radiology
RATIONALE AND OBJECTIVES : To develop an ultrasomics model for preoperative pathological grading of hepatocellular carcinoma (HCC) using contrast-enhanced ultrasound (CEUS).
MATERIAL AND METHODS : A total of 235 HCCs were retrospectively enrolled, including 65 high-grade and 170 low-grade HCCs. Representative images of four-phase CEUS were selected from the baseline sonography, arterial, portal venous, and delayed phase images. Tumor ultrasomics features were automatically extracted using Ultrasomics-Platform software. Models were built via the classifier support vector machine, including an ultrasomics model using the ultrasomics features, a clinical model using the clinical factors, and a combined model using them both. Model performances were tested in the independent validation cohort considering efficiency and clinical usefulness.
RESULTS : A total of 1502 features were extracted from each image. After the reproducibility test and dimensionality reduction, 25 ultrasomics features and 3 clinical factors were selected to build the models. In the validation cohort, the combined model showed the best predictive power, with an area under the curve value of 0.785 (95% confidence interval [CI] 0.662-0.909), compared to the ultrasomics model of 0.720 (95% CI 0.576-0.864) and the clinical model of 0.665 (95% CI 0.537-0.793). Decision curve analysis suggested that the combined model was clinically useful, with a corresponding net benefit of 0.760 compared to the other two models.
CONCLUSION : We presented an ultrasomics-clinical model based on multiphase CEUS imaging and clinical factors, which showed potential value for the preoperative discrimination of HCC pathological grades.
Wang Wei, Wu Shan-Shan, Zhang Jian-Chao, Xian Meng-Fei, Huang Hui, Li Wei, Zhou Zhuo-Ming, Zhang Chu-Qing, Wu Ting-Fan, Li Xin, Xu Ming, Xie Xiao-Yan, Kuang Ming, Lu Ming-De, Hu Hang-Tong
Contrast-enhanced ultrasound, Hepatocellular carcinoma, Pathological grade, Ultrasomics