Deep learning radiomics for focal liver lesions diagnosis on long-range contrast-enhanced ultrasound and clinical factors

Abstract

Background: Routine clinical factors play an important role in the clinical diagnosis of focal liver lesions (FLLs); however, they are rarely used in computer-assisted diagnosis. Therefore, we developed a deep learning (DL) radiomics model, and investigated its effectiveness in diagnosing FLLs using long-range contrast-enhanced ultrasound (CEUS) cines and clinical factors. Methods: Herein, 303 patients with pathologically confirmed FLLs after surgery at three hospitals were retrospectively enrolled and divided into a training cohort (n=203), internal validation (IV) cohort (n=50) from one hospital with the ratio of 4:1, and external validation (EV) cohort (n=50) from the other two hospitals. Four DL radiomics models, namely Four Stream 3D convolutional neural network (FS3DU) (trained with CEUS cines only), FS3DU+A (trained with CEUS cines and alpha fetoprotein), FS3DU+H (trained with CEUS cines and hepatitis), and FS3DU+A+H (trained with CEUS cines, alpha fetoprotein, and hepatitis), were formed based on 3D convolutional neural networks (CNNs). They used approximately 20-s preoperative CEUS cines and/or clinical factors to extract spatiotemporal features for the classification of FLLs and the location of the region of interest. The area under curve of the receiver operating characteristic and diagnosis speed were calculated to evaluate the models in the IV and EV cohorts, and they were compared with those of two radiologists. Two-sided Delong tests were used to calculate the statistical differences between the models and radiologists. Results: FS3DU+A+H, which incorporated CEUS cines, hepatitis, and alpha fetoprotein, achieved the highest area under curve of 0.969 (95% CI: 0.901-1.000) and 0.957 (95% CI: 0.894-1.000) among radiologists and other models in IV and EV cohorts, respectively. A significant difference was observed when comparing FS3DU and radiologist 2 (all P<0.05). The diagnosis speed of all the models was the same (10.76 s per patient), and it was two times faster than those of the radiologists (radiologist 1: 23.74 and 27.75 s; radiologist 2: 25.95 and 29.50 s in IV and EV cohorts, respectively). Conclusions: The proposed DL radiomics demonstrated excellent performance on the benign and malignant diagnosis of FLLs by combining CEUS cines and clinical factors. It could help the individualized characterization of FLLs, and enhance the accuracy of diagnosis in the future.