|
|
|
| Diagnosis of significant liver fibrosis: An evaluation of the combined model of extracting liver-spleen radiomics features with MRI and clinical risk factors |
| LI Jiajia, WANG Zhaohong, NI Zhonglin, CHEN Hui,ZHOU Bin, TONG Hongfei. |
| Department of Hepatological Surgery, the Second Affiliated Hospital of Wenzhou Medical University ,Wenzhou 325027, China |
|
| Cite this article: |
|
LI Jiajia,WANG Zhaohong,NI Zhonglin, et al. Diagnosis of significant liver fibrosis: An evaluation of the combined model of extracting liver-spleen radiomics features with MRI and clinical risk factors[J]. JOURNAL OF WEZHOU MEDICAL UNIVERSITY, 2023, 53(2): 93-101.
|
|
|
|
Abstract Objective: To investigate the effect of combining liver-spleen radiomics features based on multimodality abdominal MRI with clinical risk factors in the diagnosis of significant liver fibrosis. Methods:A total of 110 patients who underwent liver biopsy or surgical pathological examination and received standard abdominal MRI within 6 months of pathological examination were collected from the Second Affiliated Hospital of Wenzhou Medical University from May 2017 to May 2022. All patients were randomly divided into training set and testing set by 7:3. According to the METAVIR scoring system, grade F2 and above was defined as significant liver fibrosis, and grade below F2 as no or non-significant liver fibrosis. The liver and spleen features were marked respectively, and the radiomics features were extracted respectively. After feature screening, SVM machine learning radiomics models of the liver and liver -spleen combined features were constructed to calculate the radiomics score of each patient (Rad-Score). Logistic regression was used to analyze the clinical influence factors for significant liver fibrosis. Finally, Logistic regression was used to construct a joint model based on clinical influence factors and Rad-score, and to draw a nomogram. The performance of the model was evaluated by using the receiver operating characteristic (ROC) curve and the decision curve analysis (DCA). Results: After screening, 22 and 36 radiomics features were involved in the construction of liver and liver-spleen radiomics model respectively. In the multifactorial regression analysis, results showed that gender (female) (OR=0.126,95%CI=0.040-0.354, P<0.001), age (OR=0.985, 95%CI=0.066-0.999, P=0.011), hepatitis B infection (OR=5.139,95%CI=1.898-15.137, P=0.002), and APRI index≥1 (OR=3.793, 95 %CI=1.231-14.5, P=0.033) were independent clinical influence factors, which were included in the construction of the clinical predictive models. In the Logistic regression model, the area under the ROC curve (AUC) of the liver features and liver-spleen combined feature radiomics model were 0.828 and 0.917, respectively, indicating that the diagnostic efficiency of the liver-spleen combined feature radiomics model was better. The radiomics model was combined with the clinical predictive model to establish a combined predictive model, of which AUC in the training sets and in the testing sets was 0.948 and 0.963 respectively. DCA showed that the clinical applicability of the combined predictive model was optimal.Conclusion: The combined feature radiomics model based on multimodality abdominal MRI has better diagnostic efficacy than the single liver features. And the combined predictive model has better diagnostic efficacy than the clinical predictive model.
|
|
Received: 23 August 2022
|
| |
|
|
|
|
|
|
