目的:诊断口腔潜在恶性疾病(OPMD)对于预防口腔癌至关重要。这项研究旨在自动检测和分类最常见的癌前口腔病变,如白斑和口腔扁平苔藓(OLP),并使用视觉转换器在临床照片上将它们与口腔鳞状细胞癌(OSCC)和健康的口腔粘膜区分开。
方法:4,161张健康粘膜照片,白斑,OLP,OSCC也包括在内。研究结果按像素进行注释,并由三名临床医生进行审查。照片分为3,337张进行培训和验证,824张进行测试。训练和验证图像进一步分为五层分层。带有SwinTransformer的MaskR-CNN通过交叉验证进行了五次训练,并采用保持测试分割来评估模型性能。精度,F1分数,灵敏度,特异性,并计算了准确性。给出了最有效模型的接收器工作特征曲线(AUC)和混淆矩阵下的面积。
结果:用所用模型检测OSCC产生0.852的F1和0.974的AUC。OLP的检测具有0.825的F1和0.948的AUC。对于白斑,F1为0.796,AUC为0.938。
结论:使用的模型可以有效地检测到OSCC,而OLP和白斑的检测是中等有效的。
结论:口腔癌通常在晚期发现。证明的技术可以支持OPMD的检测和观察,以降低疾病负担并更早地识别恶性口腔病变。
OBJECTIVE: Diagnosing oral potentially malignant disorders (OPMD) is critical to prevent oral cancer. This study aims to automatically detect and classify the most common pre-malignant oral lesions, such as
leukoplakia and oral lichen planus (OLP), and distinguish them from oral squamous cell carcinomas (OSCC) and healthy oral mucosa on clinical photographs using vision transformers.
METHODS: 4,161 photographs of healthy mucosa,
leukoplakia, OLP, and OSCC were included. Findings were annotated pixel-wise and reviewed by three clinicians. The photographs were divided into 3,337 for training and validation and 824 for testing. The training and validation images were further divided into five folds with stratification. A Mask R-CNN with a Swin Transformer was trained five times with cross-validation, and the held-out test split was used to evaluate the model performance. The precision, F1-score, sensitivity, specificity, and accuracy were calculated. The area under the receiver operating characteristics curve (AUC) and the confusion matrix of the most effective model were presented.
RESULTS: The detection of OSCC with the employed model yielded an F1 of 0.852 and AUC of 0.974. The detection of OLP had an F1 of 0.825 and AUC of 0.948. For
leukoplakia the F1 was 0.796 and the AUC was 0.938.
CONCLUSIONS: OSCC were effectively detected with the employed model, whereas the detection of OLP and
leukoplakia was moderately effective.
CONCLUSIONS: Oral cancer is often detected in advanced stages. The demonstrated technology may support the detection and observation of OPMD to lower the disease burden and identify malignant oral cavity lesions earlier.