目的:口腔成釉细胞瘤(AME)和牙源性角化囊肿(OKC)的术前诊断一直是牙科的挑战。本研究使用影像组学方法和机器学习(ML)算法来表征锥形束计算机断层扫描(CBCT)图像特征,用于AME和OKC的术前鉴别诊断,并将ML算法与放射科医生进行比较以验证性能。
方法:我们回顾性收集了326例AME和OKC患者的数据,所有诊断均通过组织病理学检查证实。总共选择了348个特征来训练六个ML模型,以通过五次交叉验证进行鉴别诊断。然后,我们将基于ML的诊断的性能与放射科医生的性能进行了比较。
结果:在六个ML模型中,XGBoost在CBCT图像中有效区分AME和OKC,其分类性能优于其他模型。平均精度,召回,准确度,F1分数,曲线下面积(AUC)分别为0.900、0.807、0.843、0.841和0.872。与放射科医生的诊断相比,基于ML的放射学诊断表现更好。
结论:基于Radiomic的ML算法可以准确区分AME和OKC的CBCT图像,促进AME和OKC的术前鉴别诊断。
结论:ML和具有高分辨率CBCT图像的放射学方法为AME和OKC的鉴别诊断提供了新的见解。
OBJECTIVE: Preoperative diagnosis of oral ameloblastoma (AME) and odontogenic keratocyst (OKC) has been a challenge in dentistry. This study uses radiomics approaches and machine learning (ML) algorithms to characterize cone-beam CT (CBCT) image features for the preoperative differential diagnosis of AME and OKC and compares ML algorithms to expert radiologists to validate performance.
METHODS: We retrospectively collected the data of 326 patients with AME and OKC, where all diagnoses were confirmed by histopathologic tests. A total of 348 features were selected to train six ML models for differential diagnosis by a 5-fold cross-validation. We then compared the performance of ML-based diagnoses to those of radiologists.
RESULTS: Among the six ML models, XGBoost was effective in distinguishing AME and OKC in CBCT images, with its classification performance outperforming the other models. The mean precision, recall, accuracy, F1-score, and area under the curve (AUC) were 0.900, 0.807, 0.843, 0.841, and 0.872, respectively. Compared to the diagnostics by radiologists, ML-based radiomic diagnostics performed better.
CONCLUSIONS: Radiomic-based ML algorithms allow CBCT images of AME and OKC to be distinguished accurately, facilitating the preoperative differential diagnosis of AME and OKC.
CONCLUSIONS: ML and radiomic approaches with high-resolution CBCT images provide new insights into the differential diagnosis of AME and OKC.