The 5th edition of the World Health Organization (WHO) Classification of Head and Neck Tumors opened to online access in March 2022. This edition is conceptually similar to the previous classification of odontogenic lesions. The only newly defined entity in odontogenic lesions is adenoid ameloblastoma, which is classified under benign epithelial odontogenic tumors. While not odontogenic, the surgical ciliated cyst is a new entry to the cyst classification of the jaws. In other respects, a very important change was made in the new blue books that added ‘essential and desirable diagnostic criteria’ for each entity to highlight the features considered indispensable for diagnosis. In this article, we review the odontogenic tumors and cysts of the jaw sections of the Odontogenic and Maxillofacial Bone Tumors Chapter, outlining changes from the 2017 WHO classification and summarizing the essential diagnostic criteria and new developments.

Oral cancer is the most common type of head and neck cancer worldwide, leading to approximately 177,757 deaths every year. When identified at early stages, oral cancers can achieve survival rates of up to 75–90%. However, the majority of the cases are diagnosed at an advanced stage mainly due to the lack of public awareness about oral cancer signs and the delays in referrals to oral cancer specialists. As early detection and treatment remain to be the most effective measures in improving oral cancer outcomes, the development of vision-based adjunctive technologies that can detect oral potentially malignant disorders (OPMDs), which carry a risk of cancer development, present significant opportunities for the oral cancer screening process. In this study, we explored the potential applications of computer vision techniques in the oral cancer domain within the scope of photographic images and investigated the prospects of an automated system for detecting OPMD. Exploiting the advancements in deep learning, a two-stage model was proposed to detect oral lesions with a detector network and classify the detected region into three categories (benign, OPMD, carcinoma) with a second-stage classifier network. Our preliminary results demonstrate the feasibility of deep learning-based approaches for the automated detection and classification of oral lesions in real time. The proposed model offers great potential as a low-cost and non-invasive tool that can support screening processes and improve detection of OPMD.

The 4 < sup > th < /sup > edition of the World Health Organization (WHO) Classification of Head and Neck Tumors was published in January 2017. The edition serves to provide an updated classification scheme, and extended genetic and molecular data that are useful as diagnostic tools for the lesions of the head and neck region. This review focuses on the most current update of odontogenic cysts and tumors based on the 2017 WHO edition. The updated classification has some important differences from the 3 < sup > rd < /sup > edition (2005), including a new classification of odontogenic cysts, 'reclassified' odontogenic tumors, and some new entities.

ODONTOGENIC TUMORS. WHERE ARE WE IN 2017?