Peng Hu1, Xi Hu1, Yudong Lin2, Xiaojing Yu1, Xinwei Tao3, Jihong Sun1, Xia Wu4. 1. Department of Radiology, Sir Run Run Shaw hospital, Zhejiang University School of Medicine, 3 East Qingchun Road, Hangzhou City, Zhejiang 310016, China. 2. Zhejiang University School of Medicine, Hangzhou City, China. 3. Siemens Healthineers China, Shanghai, China. 4. Department of Radiology, Sir Run Run Shaw hospital, Zhejiang University School of Medicine, 3 East Qingchun Road, Hangzhou City, Zhejiang 310016, China. Electronic address: 3309044@zju.edu.cn.
Abstract
RATIONALE AND OBJECTIVES: To develop and validate a combination model of radiomics features and clinical biomarkers to differentiate nonadvanced from advanced liver fibrosis. MATERIALS AND METHODS: One hundred and eight consecutive patients with pathologically diagnosed liver fibrosis were randomly placed in a training or a test cohort at a ratio of 2:1. For each patient, 1674 radiomics features extracted from portal venous phase CT images were reduced by using minimum redundancy and maximum relevant. The optimal features identified were incorporated into the radiomics model. Eight clinical markers were evaluated. Integrated with clinical independent risk factors, a combination model was built. A nomogram was also established from the model. The performance of the models was assessed. Finally, a decision curve analysis was performed to estimate the clinical usefulness of the nomogram. RESULTS: The radiomics model established using five features achieved a promising level of discrimination between nonadvanced and advanced liver fibrosis. The combination model incorporated the radiomics signature with two clinical biomarkers and showed good calibration and discrimination. The training and testing cohort results of the radiomics model were area under curve values 0.864 and 0.772, accuracy 77.8% and 77.8%, sensitivity 86.7% and 73.1%, and specificity 71.4% and 90.0%, respectively. For the combination model, the training and testing cohort results were area under curve values 0.915 and 0.897, accuracy 83.3% and 86.1%, sensitivity 86% and 80.6%, and specificity 82.6% and 92.3%, respectively. The decision curve indicated the nomogram has potential in clinical application. CONCLUSION: This combination model provides a promising approach for differentiating non-advanced from advanced liver fibrosis.
RATIONALE AND OBJECTIVES: To develop and validate a combination model of radiomics features and clinical biomarkers to differentiate nonadvanced from advanced liver fibrosis. MATERIALS AND METHODS: One hundred and eight consecutive patients with pathologically diagnosed liver fibrosis were randomly placed in a training or a test cohort at a ratio of 2:1. For each patient, 1674 radiomics features extracted from portal venous phase CT images were reduced by using minimum redundancy and maximum relevant. The optimal features identified were incorporated into the radiomics model. Eight clinical markers were evaluated. Integrated with clinical independent risk factors, a combination model was built. A nomogram was also established from the model. The performance of the models was assessed. Finally, a decision curve analysis was performed to estimate the clinical usefulness of the nomogram. RESULTS: The radiomics model established using five features achieved a promising level of discrimination between nonadvanced and advanced liver fibrosis. The combination model incorporated the radiomics signature with two clinical biomarkers and showed good calibration and discrimination. The training and testing cohort results of the radiomics model were area under curve values 0.864 and 0.772, accuracy 77.8% and 77.8%, sensitivity 86.7% and 73.1%, and specificity 71.4% and 90.0%, respectively. For the combination model, the training and testing cohort results were area under curve values 0.915 and 0.897, accuracy 83.3% and 86.1%, sensitivity 86% and 80.6%, and specificity 82.6% and 92.3%, respectively. The decision curve indicated the nomogram has potential in clinical application. CONCLUSION: This combination model provides a promising approach for differentiating non-advanced from advanced liver fibrosis.