Development of a Pathomics-Based Model for the Prediction of Malignant Transformation in Oral Leukoplakia

Cai, Xinjia; Li, Long; Yu, Feiyan; Guo, Rongrong; Zhou, Xuan; Zhang, Fang; Zhang, Heyu; Zhang, Jianyun; Li, Tiejun

doi:10.1016/j.labinv.2023.100173

Cited by 6 publications

(1 citation statement)

References 29 publications

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“… 17 Recently, an increasing number of studies have employed AI-based image analysis techniques to evaluate routine histological slides. 18 – 30 This offers significant potential for disease research, as AI algorithms can support the decision-making of clinicians and pathologists by analyzing numerous digitized histological slides to derive relevant scientific and clinical insights. 17 , 31 In addition, AI systems broaden the scope of information that can be derived from histopathology slides.…”

Section: Introductionmentioning

confidence: 99%

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

Cai,

Zhang,

Wang

et al. 2024

Int J Oral Sci

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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.

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