Radiomics-based automated machine learning for differentiating focal liver lesions on unenhanced computed tomography

• Published in: Abdominal imaging Vol. 50; no. 5; pp. 2126 - 2139
• Main Authors: Yang, Nan, Ma, Zhuangxuan, Zhang, Ling, Ji, Wenbin, Xi, Qian, Li, Ming, Jin, Liang
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.05.2025; Springer Nature B.V
• Subjects: Abscesses; Adult; Aged; Algorithms; Automation; Computed tomography; Cysts; Diagnosis; Diagnosis, Differential; Female; Gastroenterology; Health care facilities; Hemangioma; Hepatology; Humans; Imaging; Learning algorithms; Lesions; Liver; Liver - diagnostic imaging; Liver Diseases - diagnostic imaging; Liver Neoplasms - diagnostic imaging; Machine Learning; Male; Malignancy; Medical imaging; Medicine; Medicine & Public Health; Middle Aged; Radiographic Image Interpretation, Computer-Assisted - methods; Radiology; Radiomics; Retrospective Studies; Tomography; Tomography, X-Ray Computed - methods; Carcinoma; CT scan; Liver; Machine learning; Radiomics
• ISSN: 2366-0058; 2366-004X; 2366-0058
• DOI: 10.1007/s00261-024-04685-y

Background & Aims Enhanced computed tomography (CT) is the primary method for focal liver lesion diagnosis. We aimed to use automated machine learning (AutoML) algorithms to differentiate between benign and malignant focal liver lesions on the basis of radiomics from unenhanced CT images. Methods We enrolled 260 patients from 2 medical centers who underwent CT examinations between January 2017 and March 2023. This included 60 cases of hepatic malignancies, 93 cases of hepatic hemangiomas, 48 cases of hepatic abscesses, and 84 cases of hepatic cysts. The Pyradiomics method was used to extract radiomics features from unenhanced CT images. By using the mljar-supervised (MLJAR) AutoML framework, clinical, radiomics, and fusion models combining clinical and radiomics features were established. Results In the training and validation sets, the area under the curve (AUC) values for the clinical, radiomics, and fusion models exceeded 0.900. In the external testing set, the respective AUC values for the clinical, radiomics, and fusion models were as follows: 0.88, 1.00, and 1.00 for hepatic cysts; 0.81, 0.90, and 0.97 for hepatic hemangiomas; 0.89, 0.98, and 0.92 for hepatic abscesses; and 0.23, 0.80, and 0.93 for hepatic malignancies. The diagnostic accuracy rates for hepatic cysts, hemangiomas, malignancies, and abscesses by radiologists in the external testing cohort were 0.96, 0.60, 0.79, and 0.66, respectively. Conclusion The fusion model based on noninvasive radiomics and clinical features of unenhanced CT images has high clinical value for distinguishing focal hepatic lesions. Lay summary The preoperative diagnosis of focal liver lesions is essential for choosing appropriate treatment. Thus, we aimed to use the MLJAR AutoML framework to differentiate benign and malignant focal liver lesions on the basis of radiomics from unenhanced CT images.