Department of Oral Pathology, Peking University School and Hospital of Stomatology & National Center of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices, Beijing, China.
Central Laboratory, Peking University School and Hospital of Stomatology, Beijing, China.
Int J Oral Sci. 2024 Feb 26;16(1):16. doi: 10.1038/s41368-024-00287-y.
Odontogenic keratocyst (OKC) is a common jaw cyst with a high recurrence rate. OKC combined with basal cell carcinoma as well as skeletal and other developmental abnormalities is thought to be associated with Gorlin syndrome. Moreover, OKC needs to be differentiated from orthokeratinized odontogenic cyst and other jaw cysts. Because of the different prognosis, differential diagnosis of several cysts can contribute to clinical management. We collected 519 cases, comprising a total of 2 157 hematoxylin and eosin-stained images, to develop digital pathology-based artificial intelligence (AI) models for the diagnosis and prognosis of OKC. The Inception_v3 neural network was utilized to train and test models developed from patch-level images. Finally, whole slide image-level AI models were developed by integrating deep learning-generated pathology features with several machine learning algorithms. The AI models showed great performance in the diagnosis (AUC = 0.935, 95% CI: 0.898-0.973) and prognosis (AUC = 0.840, 95%CI: 0.751-0.930) of OKC. The advantages of multiple slides model for integrating of histopathological information are demonstrated through a comparison with the single slide model. Furthermore, the study investigates the correlation between AI features generated by deep learning and pathological findings, highlighting the interpretative potential of AI models in the pathology. Here, we have developed the robust diagnostic and prognostic models for OKC. The AI model that is based on digital pathology shows promise potential for applications in odontogenic diseases of the jaw.
牙源性角化囊肿（OKC）是一种常见的颌骨囊肿，具有较高的复发率。OKC 合并基底细胞癌以及骨骼和其他发育异常，被认为与 Gorlin 综合征有关。此外，OKC 需要与正角化牙源性囊肿和其他颌骨囊肿相鉴别。由于预后不同，对几种囊肿进行鉴别诊断有助于临床管理。我们收集了 519 例病例，共 2157 张苏木精-伊红染色图像，用于开发基于数字病理学的 OKC 诊断和预后人工智能（AI）模型。使用 Inception_v3 神经网络对基于图像块的模型进行训练和测试。最后，通过将深度学习生成的病理特征与几种机器学习算法相结合，开发了全切片图像级别的 AI 模型。AI 模型在 OKC 的诊断（AUC=0.935，95%CI：0.898-0.973）和预后（AUC=0.840，95%CI：0.751-0.930）方面表现出了优异的性能。通过与单张切片模型的比较，展示了多切片模型在整合组织病理学信息方面的优势。此外，本研究还探讨了深度学习生成的 AI 特征与病理发现之间的相关性，强调了 AI 模型在病理学中的解释潜力。在这里，我们已经为 OKC 开发了强大的诊断和预后模型。基于数字病理学的 AI 模型在颌骨牙源性疾病的应用中具有广阔的应用前景。
