Improving B-mode ultrasound diagnostic performance for focal liver lesions using deep learning: A multicentre study.

Abstract:

BACKGROUND:The diagnosis performance of B-mode ultrasound (US) for focal liver lesions (FLLs) is relatively limited. We aimed to develop a deep convolutional neural network of US (DCNN-US) for aiding radiologists in classification of malignant from benign FLLs. MATERIALS AND METHODS:This study was conducted in 13 hospitals and finally 2143 patients with 24,343 US images were enrolled. Patients who had non-cystic FLLs with pathological results were enrolled. The FLLs from 11 hospitals were randomly divided into training and internal validations (IV) cohorts with a 4:1 ratio for developing and evaluating DCNN-US. Diagnostic performance of the model was verified using external validation (EV) cohort from another two hospitals. The diagnosis value of DCNN-US was compared with that of contrast enhanced computed tomography (CT)/magnetic resonance image (MRI) and 236 radiologists, respectively. FINDINGS:The AUC of ModelLBC for FLLs was 0.924 (95% CI: 0.889-0.959) in the EV cohort. The diagnostic sensitivity and specificity of ModelLBC were superior to 15-year skilled radiologists (86.5% vs 76.1%, p = 0.0084 and 85.5% vs 76.9%, p = 0.0051, respectively). Accuracy of ModelLBC was comparable to that of contrast enhanced CT (both 84.7%) but inferior to contrast enhanced MRI (87.9%) for lesions detected by US. INTERPRETATION:DCNN-US with high sensitivity and specificity in diagnosing FLLs shows its potential to assist less-experienced radiologists in improving their performance and lowering their dependence on sectional imaging in liver cancer diagnosis.

journal_name

EBioMedicine

journal_title

EBioMedicine

authors

Yang Q,Wei J,Hao X,Kong D,Yu X,Jiang T,Xi J,Cai W,Luo Y,Jing X,Yang Y,Cheng Z,Wu J,Zhang H,Liao J,Zhou P,Song Y,Zhang Y,Han Z,Cheng W,Tang L,Liu F,Dou J,Zheng R,Yu J,Tian J,Liang P

doi

10.1016/j.ebiom.2020.102777

subject

Has Abstract

pub_date

2020-06-01 00:00:00

pages

102777

issn

2352-3964

pii

S2352-3964(20)30152-3

journal_volume

56

pub_type

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