Radiomics-based automated machine learning for differentiating focal liver lesions on unenhanced computed tomography.
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:...
| Publicado en: | Abdominal Radiology Vol. 50; no. 5; pp. 2126 - 2140 |
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| Autores principales: | , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
May2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=184452425&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 184452425 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 2366004X JT14 jtl: Abdominal Radiology issn: 2366004X maglogo: N pubinfo: dt: May2025 vid: 50 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 184452425 181005765 10.1007/s00261-024-04685-y 184452425 ppf: 2126 ppct: 14 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Radiomics-based automated machine learning for differentiating focal liver lesions on unenhanced computed tomography. aug: au: Yang, Nan Ma, Zhuangxuan Zhang, Ling Ji, Wenbin Xi, Qian Li, Ming Jin, Liang affil: https://ror.org/012wm7481 Department of Radiology, Huadong Hospital, Fudan University, Shanghai, China sug: ab: 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. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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