Digital pathology-based artificial intelligence models for differential diagnosis and prognosis of sporadic odontogenic keratocysts  

作　　者：Xinjia Cai Heyu Zhang Yanjin Wang Jianyun Zhang Tiejun Li 

机构地区：[1]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 [2]Central Laboratory,Peking University School and Hospital of Stomatology,Beijing,China [3]Department of Oral Pathology,Shanghai Stomatological Hospital&School of Stomatology,Fudan University,Shanghai,China [4]Research Unit of Precision Pathologic Diagnosis in Tumors of the Oral and Maxillofacial Regions,Chinese Academy of Medical Sciences(2019RU034),Beijing,China

出　　处：《International Journal of Oral Science》2024年第2期349-358,共10页国际口腔科学杂志（英文版）

基　　金：supported by the National Nature Science Foundation of China(81671006,81300894);CAMS Innovation Fund for Medical Sciences(2019-I2M-5-038);National Clinical Key Discipline Construction Project(PKUSSNKP202102).

摘　　要：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 2157 hematoxylin and eosinstained 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 imagelevel 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.

关 键 词：DIAGNOSIS PATHOLOGY PROGNOSIS 

分 类 号：R782[医药卫生—口腔医学]