Abstract：
OBJECTIVE: In this study, our aim was to develop and validate the effectiveness of diverse radiomic models for distinguishing between gnathic fibrous dysplasia (FD) and ossifying fibroma (OF) before surgery.
METHODS: We enrolled 220 patients with confirmed FD or OF. We extracted radiomic features from nonenhanced CT images. Following dimensionality reduction and feature selection, we constructed radiomic models using logistic regression, support vector machine, random forest, light gradient boosting machine, and eXtreme gradient boosting. We then identified the best radiomic model using receiver operating characteristic (ROC) curve analysis. After combining radiomics features with clinical features, we developed a comprehensive model. ROC curve and decision curve analysis (DCA) demonstrated the models\' robustness and clinical value.
RESULTS: We extracted 1834 radiomic features from CT images, reduced them to eight valuable features, and achieved high predictive efficiency, with area under curves (AUC) exceeding 0.95 for all the models. Ultimately, our combined model, which integrates radiomic and clinical data, displayed superior discriminatory ability (AUC: training cohort 0.970; test cohort 0.967). DCA highlighted its optimal clinical efficacy.
CONCLUSIONS: Our combined model effectively differentiates between FD and OF, offering a noninvasive and efficient approach to clinical decision-making.
METHODS: We enrolled 220 patients with confirmed FD or OF. We extracted radiomic features from nonenhanced CT images. Following dimensionality reduction and feature selection, we constructed radiomic models using logistic regression, support vector machine, random forest, light gradient boosting machine, and eXtreme gradient boosting. We then identified the best radiomic model using receiver operating characteristic (ROC) curve analysis. After combining radiomics features with clinical features, we developed a comprehensive model. ROC curve and decision curve analysis (DCA) demonstrated the models\' robustness and clinical value.
RESULTS: We extracted 1834 radiomic features from CT images, reduced them to eight valuable features, and achieved high predictive efficiency, with area under curves (AUC) exceeding 0.95 for all the models. Ultimately, our combined model, which integrates radiomic and clinical data, displayed superior discriminatory ability (AUC: training cohort 0.970; test cohort 0.967). DCA highlighted its optimal clinical efficacy.
CONCLUSIONS: Our combined model effectively differentiates between FD and OF, offering a noninvasive and efficient approach to clinical decision-making.
摘要：
目的：在本研究中，我们的目的是开发和验证不同的影像学模型在术前区分颌骨纤维发育不良(FD)和骨化性纤维瘤(OF)的有效性.
方法：我们招募了220例确诊为FD或OF的患者。我们从未增强CT图像中提取影像组学特征。在降维和特征选择之后，我们使用逻辑回归构建了放射学模型，支持向量机，随机森林,轻型梯度增压机，和极限梯度提升。然后，我们使用接收器工作特征（ROC）曲线分析确定了最佳的放射学模型。在将影像组学特征与临床特征相结合后，我们开发了一个综合模型。ROC曲线和决策曲线分析（DCA）证明了模型的稳健性和临床价值。
结果：我们从CT图像中提取了1834个放射学特征，将它们减少到八个有价值的特征，并实现了较高的预测效率，所有模型的曲线下面积（AUC）均超过0.95。最终,我们的组合模型，整合了影像学和临床数据，显示出较好的辨别能力（AUC：训练队列0.970；测试队列0.967）。DCA强调了其最佳临床疗效。
结论：我们的组合模型有效地区分了FD和OF，为临床决策提供一种无创有效的方法。
方法：我们招募了220例确诊为FD或OF的患者。我们从未增强CT图像中提取影像组学特征。在降维和特征选择之后，我们使用逻辑回归构建了放射学模型，支持向量机，随机森林,轻型梯度增压机，和极限梯度提升。然后，我们使用接收器工作特征（ROC）曲线分析确定了最佳的放射学模型。在将影像组学特征与临床特征相结合后，我们开发了一个综合模型。ROC曲线和决策曲线分析（DCA）证明了模型的稳健性和临床价值。
结果：我们从CT图像中提取了1834个放射学特征，将它们减少到八个有价值的特征，并实现了较高的预测效率，所有模型的曲线下面积（AUC）均超过0.95。最终,我们的组合模型，整合了影像学和临床数据，显示出较好的辨别能力（AUC：训练队列0.970；测试队列0.967）。DCA强调了其最佳临床疗效。
结论：我们的组合模型有效地区分了FD和OF，为临床决策提供一种无创有效的方法。
