BMC medical imaging · 2025
CT-based radiomics models using intralesional and different perilesional signatures in predicting the microvascular density of hepatic alveolar echinococcosis.
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Hou Juan, Zhang Simiao, Li Shouxian, Zhao Zicheng, Zhao Longfei, Zhang Tieliang, Liu Wenya
Аннотация
OBJECTIVES: To evaluate the performance of CT-based intralesional combined with different perilesional radiomics models in predicting the microvascular density (MVD) of hepatic alveolar echinococcosis (HAE).
METHODS: This study retrospectively analyzed preoperative CT data from 303 patients with HAE confirmed by surgical pathology (MVD positive, n = 182; MVD negative, n = 121). The patients were randomly divided into 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 10 mm perilesional volume (GPLV10mm), gross combined 15 mm perilesional volume (GPLV15mm) and gross combined 20 mm 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 test cohort. The DCA showed that the nomogram had greater benefits among the three models.
CONCLUSION: CT-based GPLV20mm radiomics model can better predict MVD of HAE. The nomogram model showed the best predictive performance.
METHODS: This study retrospectively analyzed preoperative CT data from 303 patients with HAE confirmed by surgical pathology (MVD positive, n = 182; MVD negative, n = 121). The patients were randomly divided into 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 10 mm perilesional volume (GPLV10mm), gross combined 15 mm perilesional volume (GPLV15mm) and gross combined 20 mm 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 test cohort. The DCA showed that the nomogram had greater benefits among the three models.
CONCLUSION: CT-based GPLV20mm radiomics model can better predict MVD of HAE. The nomogram model showed the best predictive performance.