LAUSR

The focal liver lesions (FLLs) can be divided into two categories such as benign and malignant. The most common primary malignant FLL is hepatocellular carcinoma (HCC) that is 6 most common cancer in humans. HCC is fourth leading cause of cancer-related death worldwide [1]. Approximately 3/4 of all new cases occur in lowand middle-income countries [2]. Therefore, cost-effective cancer screening method of the liver is mandatory. B-mode ultrasound (US) without use of contrast agent can be one of the ideal screening methods for FLLs because it is widely available (inexpensive and small enough to install in outpatient clinics), less invasive (free from radiation exposure and adverse effects induced by contrast agent), and prompt (real time diagnosis). However, B-mode US has been recognized as less accurate at diagnosing FLLs compared to the other advanced tomographic modalities such as contrast-enhanced CT/MRI because of high dependence on the examiner’s experience and skills [3,4]. In this article of EBioMedicine, Yang and colleagues report the excellent diagnostic performance of the developed deep convolutional neural network of US (DCNN-US) in classification of malignant from benign FLLs using 11 standard still US images and clinical-ultrasonic factors [5]. It is noteworthy that DCNN-US showed higher diagnostic performance compared to experienced radiologists and comparable diagnostic performance to contrast-enhanced CT for lesions detected by US in a large external validation cohort obtained from a prospective multicentre study. Therefore, deep learning-based method such as DCNN-US may improve current clinical practice for patients with liver cancer especially in the situation experienced radiologist or advanced imaging modalities are not available. In the basic technical aspects, significance of this paper can be that the diagnostic performance of proposed model based on deep convolutional neural network was improved when an independently trained network and clinical-ultrasonic factors