Comparing radiomics models with different inputs for accurate diagnosis of significant fibrosis in chronic liver disease

• Published in: European radiology Vol. 31; no. 11; pp. 8743 - 8754
• Main Authors: Lu, Xue, Zhou, Hui, Wang, Kun, Jin, Jieyang, Meng, Fankun, Mu, Xiaojie, Li, Shuoyang, Zheng, Rongqin, Tian, Jie
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2021; Springer Nature B.V
• Subjects: Accuracy; Biopsy; Cirrhosis; Deep learning; Diagnosis; Diagnostic Radiology; Diagnostic systems; Fibrosis; Gray scale; Imaging; Imaging Informatics and Artificial Intelligence; Internal Medicine; Interventional Radiology; Liver; Liver cirrhosis; Liver diseases; Medical diagnosis; Medical imaging; Medicine; Medicine & Public Health; Neuroradiology; Parenchyma; Patients; Performance evaluation; Radiology; Radiomics; Serology; Statistical analysis; Ultrasound; Deep learning; Hepatic cirrhosis; Elasticity imaging techniques; Liver disease
• ISSN: 0938-7994; 1432-1084
• DOI: 10.1007/s00330-021-07934-6

Objective The non-invasive discrimination of significant fibrosis (≥ F2) in patients with chronic liver disease (CLD) is clinically critical but technically challenging. We aimed to develop an updated deep learning radiomics model of elastography (DLRE2.0) based on our previous DLRE model to achieve significantly improved performance in ≥ F2 evaluation. Methods This was a retrospective multicenter study with 807 CLD patients and 4842 images from three hospitals. All of these patients have liver biopsy results as referenced standard. Multichannel deep learning radiomics models were developed. Elastography images, gray-scale images of the liver capsule, gray-scale images of the liver parenchyma, and serological results were gradually integrated to establish different diagnosis models, and the optimal model was selected for assessing ≥ F2. Its accuracy was thoroughly investigated by applying different F0–1 prevalence cohorts and independent external test cohorts. Analysis of receiver operating characteristic (ROC) curves was performed to calculate the area under the ROC curve (AUC) for significance of fibrosis (≥ F2) and cirrhosis (F4). Results The AUC of the DLRE2.0 model significantly increased to 0.91 compared with the DLRE model (AUC 0.83) when evaluating ≥ F2 ( p = 0.0167). However, it did not show statistically significant differences as integrating gray-scale images and serological data into the DLRE2.0 model. AUCs of DLRE and DLRE2.0 increased, when there was higher F0–1 prevalence. All radiomics models had good robustness in the independent external test cohort. Conclusions DLRE2.0 was the most suitable model for staging significant fibrosis while considering the balance of diagnostic accuracy and clinical practicability. Key Points • The non-invasive discrimination of significant fibrosis (≥ F2) in patients with chronic liver disease (CLD) is clinically critical but technically challenging. • We aimed to develop an updated deep learning radiomics model of elastography (DLRE2.0) based on our previous DLRE model to achieve significantly improved performance in ≥ F2 evaluation. • Our study based on 807 CLD patients and 4842 images with liver biopsy found that DLRE2.0 was the most suitable model for staging significant fibrosis while considering the balance of diagnostic accuracy and clinical practicability.