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

Autor: Yang N; Department of Radiology, Huadong Hospital, Fudan University, Shanghai, China., Ma Z; Department of Radiology, Huadong Hospital, Fudan University, Shanghai, China., Zhang L; Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China., Ji W; Radiology Department, Shanghai Electric Power Hospital, Shanghai, China., Xi Q; Department of Radiology, Eye & ENT Hospital of Fudan University, Shanghai, China. xiqian1129@163.com., Li M; Department of Radiology, Huadong Hospital, Fudan University, Shanghai, China. ming_li@fudan.edu.cn., Jin L; Department of Radiology, Huadong Hospital, Fudan University, Shanghai, China. jin_liang@fudan.edu.cn.
Jazyk: angličtina
Zdroj: Abdominal radiology (New York) [Abdom Radiol (NY)] 2024 Nov 22. Date of Electronic Publication: 2024 Nov 22.
DOI: 10.1007/s00261-024-04685-y
Abstrakt: 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.
Competing Interests: Declarations. Competing interests: The authors declare no competing interests.
(© 2024. The Author(s).)
Databáze: MEDLINE
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