颌骨囊状上皮性肿瘤是一种局部侵袭性和攻击性上皮性牙源性肿瘤。BRAF-V600E基因突变是该肿瘤中常见的遗传改变，被认为在其发病机制中起关键作用。本研究的目标是开发和验证一种基于放射组学的机器学习方法，用于鉴定颌骨囊状上皮性肿瘤患者中的BRAF-V600E基因突变。 在这项回顾性研究中，收集了103例诊断为颌骨囊状上皮性肿瘤并接受BRAF-V600E突变检测的患者的数据。其中，72例被纳入训练组，31例被纳入验证组。为了解决类别不平衡问题，我们在研究中应用了合成少数过采样技术（SMOTE）。从预处理的CT图像中提取了放射组学特征，并选择了最相关的特征，包括放射组学和临床数据，进行分析。利用机器学习方法构建了模型。使用受试者工作特征曲线（ROC曲线）评估了这些模型在区分BRAF-V600E基因突变患者和非突变患者方面的性能。 基于放射组学特征进行的分析中，随机森林的表现优于其他方法，ROC曲线下的面积（AUC）为0.87（95％CI，0.68-1.00）。XGBoost模型的性能略低于随机森林，其AUC为0.83（95％CI，0.60-1.00）。规范图表明，在年轻女性中，受影响区域主要位于下颌骨，肿瘤直径较大的患者具有更高的风险。此外，放射组学特征得分较高的患者更容易受到BRAF-V600E基因突变的影响。 我们的研究提出了一种基于放射组学的机器学习模型，使用了五种不同的方法来准确检测诊断为颌骨囊状上皮性肿瘤患者中的BRAF-V600E基因突变。随机森林模型具有较高的预测性能，AUC为0.87，证明其有潜力为不需要侵入性肿瘤取样进行分子检测的患者识别提供方便和经济有效的方法。这种非侵入性方法有潜力指导受影响个体的术前或术后药物治疗，从而改善预后。 版权所有©2023年Li, Li, Liu, Wang, Chen, Qian, Zheng, Chen, Liu和林。

Ameloblastoma is a locally invasive and aggressive epithelial odontogenic neoplasm. The BRAF-V600E gene mutation is a prevalent genetic alteration found in this tumor and is considered to have a crucial role in its pathogenesis. The objective of this study is to develop and validate a radiomics-based machine learning method for the identification of BRAF-V600E gene mutations in ameloblastoma patients.In this retrospective study, data from 103 patients diagnosed with ameloblastoma who underwent BRAF-V600E mutation testing were collected. Of these patients, 72 were included in the training cohort, while 31 were included in the validation cohort. To address class imbalance, synthetic minority over-sampling technique (SMOTE) is applied in our study. Radiomics features were extracted from preprocessed CT images, and the most relevant features, including both radiomics and clinical data, were selected for analysis. Machine learning methods were utilized to construct models. The performance of these models in distinguishing between patients with and without BRAF-V600E gene mutations was evaluated using the receiver operating characteristic (ROC) curve.When the analysis was based on radiomics signature, Random Forest performed better than the others, with the area under the ROC curve (AUC) of 0.87 (95%CI, 0.68-1.00). The performance of XGBoost model is slightly lower than that of Random Forest, and its AUC is 0.83 (95% CI, 0.60-1.00). The nomogram evident that among younger women, the affected region primarily lies within the mandible, and patients with larger tumor diameters exhibit a heightened risk. Additionally, patients with higher radiomics signature scores are more susceptible to the BRAF-V600E gene mutations.Our study presents a comprehensive radiomics-based machine learning model using five different methods to accurately detect BRAF-V600E gene mutations in patients diagnosed with ameloblastoma. The Random Forest model's high predictive performance, with AUC of 0.87, demonstrates its potential for facilitating a convenient and cost-effective way of identifying patients with the mutation without the need for invasive tumor sampling for molecular testing. This non-invasive approach has the potential to guide preoperative or postoperative drug treatment for affected individuals, thereby improving outcomes.Copyright © 2023 Li, Li, Liu, Wang, Chen, Qian, Zheng, Chen, Liu and Lin.