Objectives
To evaluate the performance of CT-based intralesional combined with different perilesional radiomics models in predicting the bioactivity of hepatic alveolar echinococcosis (HAE).
Methods
This study retrospectively analyzed preoperative CT data from 303 patients with HAE confirmed by surgical pathology (bioactivity positive, n = 182; bioactivity negative, n = 121). The patients were randomly assigned to the training cohort (n = 242) and test cohort (n = 61) at a ratio of 8:2. The radiomics features were extracted from CT images on the portal vein phase. Four radiomics models were constructed based on gross lesion volume (GLV), gross combined 10mm perilesional volume (GPLV10mm), gross combined 15mm perilesional volume (GPLV15mm) and gross combined 20mm perilesional volume (GPLV20mm). The best radiomics signature model and clinical features were combined to establish a nomogram. Receiver operating characteristic curve (ROC) and decision curve analysis (DCA) were used to evaluate the predictive performance of models.
Results
Among the four radiomics models, the GPLV20mm model performed the highest prediction performance with the area under the curves (AUCs) in training cohort and test cohort was 0.876 and 0.802, respectively. The AUC of the clinical model was 0.753 in the training cohort and 0.699 in the test cohort. The AUC of the nomogram model based clinical and GPLV20mm radiomic signatures was 0.922 in the training cohort and 0.849 in the validation cohort. The DCA showed that the nomogram had greater benefits compared with the single radiomics model or clinical model.
Conclusion
CT-based GPLV20mm radiomics model can better predict the bioactivity of HAE. The nomogram model showed the best predictive performance.