Oral surgery, oral medicine, oral pathology and oral radiology · 2024
The efficacy of CBCT-based radiomics techniques in differentiating between conventional and unicystic ameloblastoma.
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Sha Xiaoyan, Wang Chao, Qi Senrong, Yuan Xiaohong, Zhang Hui, Yang Jigang
Аннотация
OBJECTIVE: The aim of this study was to develop a cone beam computed tomography (CBCT) radiomics-based model that differentiates between conventional and unicystic ameloblastoma (AB).
METHODS: In this retrospective study, CBCT images were collected from 100 patients who had ABs that were diagnosed histopathologically as conventional or unicystic AB after surgical treatment. The patients were randomly divided into training (70) and validation (30) cohorts. Radiomics features were extracted from the images, and the optimal features were incorporated into 5 models: Logistic Regression, Support Vector Machine, Linear Discriminant Analysis, Random Forest, and XGBoost for prediction of tumor type. Model performance was evaluated using the area under the curve (AUC) from receiver operating characteristic analysis, sensitivity, specificity, accuracy, calibration curves, and decision curve analysis (DCA).
RESULTS: The 20 optimal radiomics features were incorporated into the Logistic Regression (LR) model, which exhibited the best overall performance with AUC = 0.936 (95% confidence interval [CI] = 0.877-0.996) for the training cohort and AUC = 0.929 (95% CI = 0.832-1.000) for the validation cohort. The nomogram combined the clinical features and the radiomics signature and resulted in the best predictive performance.
CONCLUSIONS: The LR model demonstrated the ability of radiomics and the nomogram to distinguish between the 2 types of AB and may have the potential to replace biopsies under noninvasive conditions.
METHODS: In this retrospective study, CBCT images were collected from 100 patients who had ABs that were diagnosed histopathologically as conventional or unicystic AB after surgical treatment. The patients were randomly divided into training (70) and validation (30) cohorts. Radiomics features were extracted from the images, and the optimal features were incorporated into 5 models: Logistic Regression, Support Vector Machine, Linear Discriminant Analysis, Random Forest, and XGBoost for prediction of tumor type. Model performance was evaluated using the area under the curve (AUC) from receiver operating characteristic analysis, sensitivity, specificity, accuracy, calibration curves, and decision curve analysis (DCA).
RESULTS: The 20 optimal radiomics features were incorporated into the Logistic Regression (LR) model, which exhibited the best overall performance with AUC = 0.936 (95% confidence interval [CI] = 0.877-0.996) for the training cohort and AUC = 0.929 (95% CI = 0.832-1.000) for the validation cohort. The nomogram combined the clinical features and the radiomics signature and resulted in the best predictive performance.
CONCLUSIONS: The LR model demonstrated the ability of radiomics and the nomogram to distinguish between the 2 types of AB and may have the potential to replace biopsies under noninvasive conditions.