Non-invasive liver fibrosis screening on CT images using radiomics
Abstract Purpose To develop a radiomics machine learning model for detecting liver fibrosis on CT images of the liver. Methods With Ethics Board approval, 169 patients (68 women, 101 men; mean age, 51.2 years ± 14.7 [SD]) underwent an ultrasound-guided liver biopsy with simultaneous CT acquisitions...
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BMC
2025-07-01
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| Series: | BMC Medical Imaging |
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| Online Access: | https://doi.org/10.1186/s12880-025-01823-w |
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| author | Jay J. Yoo Khashayar Namdar Sean Carey Sandra E. Fischer Chris McIntosh Farzad Khalvati Patrik Rogalla |
| author_facet | Jay J. Yoo Khashayar Namdar Sean Carey Sandra E. Fischer Chris McIntosh Farzad Khalvati Patrik Rogalla |
| author_sort | Jay J. Yoo |
| collection | DOAJ |
| description | Abstract Purpose To develop a radiomics machine learning model for detecting liver fibrosis on CT images of the liver. Methods With Ethics Board approval, 169 patients (68 women, 101 men; mean age, 51.2 years ± 14.7 [SD]) underwent an ultrasound-guided liver biopsy with simultaneous CT acquisitions without and following intravenous contrast material administration. Radiomic features were extracted from two regions of interest (ROIs) on the CT images, one placed at the biopsy site and another distant from the biopsy site. A development cohort, which was split further into training and validation cohorts across 100 trials, was used to determine the optimal combinations of contrast, normalization, machine learning model, and radiomic features for liver fibrosis detection based on their Area Under the Receiver Operating Characteristic curve (AUC) on the validation cohort. The optimal combinations were then used to develop one final liver fibrosis model which was evaluated on a test cohort. Results When averaging the AUC across all combinations, non-contrast enhanced (NC) CT (AUC, 0.6100; 95% CI: 0.5897, 0.6303) outperformed contrast-enhanced CT (AUC, 0.5680; 95% CI: 0.5471, 0.5890). The most effective model was found to be a logistic regression model with input features of maximum, energy, kurtosis, skewness, and small area high gray level emphasis extracted from non-contrast enhanced NC CT normalized using Gamma correction with γ = 1.5 (AUC, 0.7833; 95% CI: 0.7821, 0.7845). Conclusions The presented radiomics-based logistic regression model holds promise as a non-invasive detection tool for subclinical, asymptomatic liver fibrosis. The model may serve as an opportunistic liver fibrosis screening tool when operated in the background during routine CT examinations covering liver parenchyma. The final liver fibrosis detection model is made publicly available at: https://github.com/IMICSLab/RadiomicsLiverFibrosisDetection . |
| format | Article |
| id | doaj-art-efd8af34a5a443eda6b71d2f2a9a2d50 |
| institution | DOAJ |
| issn | 1471-2342 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Imaging |
| spelling | doaj-art-efd8af34a5a443eda6b71d2f2a9a2d502025-08-20T03:06:31ZengBMCBMC Medical Imaging1471-23422025-07-0125111410.1186/s12880-025-01823-wNon-invasive liver fibrosis screening on CT images using radiomicsJay J. Yoo0Khashayar Namdar1Sean Carey2Sandra E. Fischer3Chris McIntosh4Farzad Khalvati5Patrik Rogalla6Institute of Medical Science, University of TorontoInstitute of Medical Science, University of TorontoJoint Department of Medical Imaging, University of Toronto, Toronto General HospitalLaboratory Medicine & Pathobiology - Anatomic Pathology, University of Toronto, Toronto General HospitalVector InstituteInstitute of Medical Science, University of TorontoJoint Department of Medical Imaging, University of Toronto, Toronto General HospitalAbstract Purpose To develop a radiomics machine learning model for detecting liver fibrosis on CT images of the liver. Methods With Ethics Board approval, 169 patients (68 women, 101 men; mean age, 51.2 years ± 14.7 [SD]) underwent an ultrasound-guided liver biopsy with simultaneous CT acquisitions without and following intravenous contrast material administration. Radiomic features were extracted from two regions of interest (ROIs) on the CT images, one placed at the biopsy site and another distant from the biopsy site. A development cohort, which was split further into training and validation cohorts across 100 trials, was used to determine the optimal combinations of contrast, normalization, machine learning model, and radiomic features for liver fibrosis detection based on their Area Under the Receiver Operating Characteristic curve (AUC) on the validation cohort. The optimal combinations were then used to develop one final liver fibrosis model which was evaluated on a test cohort. Results When averaging the AUC across all combinations, non-contrast enhanced (NC) CT (AUC, 0.6100; 95% CI: 0.5897, 0.6303) outperformed contrast-enhanced CT (AUC, 0.5680; 95% CI: 0.5471, 0.5890). The most effective model was found to be a logistic regression model with input features of maximum, energy, kurtosis, skewness, and small area high gray level emphasis extracted from non-contrast enhanced NC CT normalized using Gamma correction with γ = 1.5 (AUC, 0.7833; 95% CI: 0.7821, 0.7845). Conclusions The presented radiomics-based logistic regression model holds promise as a non-invasive detection tool for subclinical, asymptomatic liver fibrosis. The model may serve as an opportunistic liver fibrosis screening tool when operated in the background during routine CT examinations covering liver parenchyma. The final liver fibrosis detection model is made publicly available at: https://github.com/IMICSLab/RadiomicsLiverFibrosisDetection .https://doi.org/10.1186/s12880-025-01823-wMachine learningLiverFibrosisComputed tomographyRadiomics |
| spellingShingle | Jay J. Yoo Khashayar Namdar Sean Carey Sandra E. Fischer Chris McIntosh Farzad Khalvati Patrik Rogalla Non-invasive liver fibrosis screening on CT images using radiomics BMC Medical Imaging Machine learning Liver Fibrosis Computed tomography Radiomics |
| title | Non-invasive liver fibrosis screening on CT images using radiomics |
| title_full | Non-invasive liver fibrosis screening on CT images using radiomics |
| title_fullStr | Non-invasive liver fibrosis screening on CT images using radiomics |
| title_full_unstemmed | Non-invasive liver fibrosis screening on CT images using radiomics |
| title_short | Non-invasive liver fibrosis screening on CT images using radiomics |
| title_sort | non invasive liver fibrosis screening on ct images using radiomics |
| topic | Machine learning Liver Fibrosis Computed tomography Radiomics |
| url | https://doi.org/10.1186/s12880-025-01823-w |
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