Odontogenic carcinosarcoma: a comprehensive review of clinical and therapeutic insights
File(s)
Published version
Author(s)
Type
Journal Article
Abstract
Malignant odontogenic tumors are rare, accounting for only 1%–6.1% of all odontogenic tumors. Among them, odontogenic carcinosarcoma (OCS) is an exceptionally rare and aggressive malignant neoplasm originating from dental tissues. First recognized by the World Health Organization (WHO) in 1992, OCS is characterized by high-grade biphasic malignant epithelial and mesenchymal components, contributing to its aggressive clinical behavior. OCS often presents with nonspecific symptoms such as pain, swelling, and loosening of teeth, which complicate early diagnosis. Its rarity adds to the diagnostic challenges, frequently leading to delays in identification. Histopathological evaluation remains the cornerstone for accurate diagnosis, distinguishing OCS from other odontogenic tumors through features like epithelial nuclear pleomorphism, mitotic activity, and mesenchymal sarcomatous differentiation. Management typically involves surgical resection with clear margins, while adjuvant therapies such as chemotherapy and radiation are considered in select cases. Recent advancements in molecular oncology and surgical techniques, including robotic-assisted procedures and 3D-printed reconstructive aids, offer promising avenues for improving patient outcomes. A multidisciplinary approach and ongoing research are essential to enhance diagnostic accuracy, refine treatment protocols, and improve the prognosis for patients affected by this rare malignancy. The primary objective of this review is to consolidate current knowledge on OCS, focusing on its diagnostic complexities, treatment strategies, and potential emerging therapies.
Date Issued
2025-04-23
Date Acceptance
2025-03-26
Citation
Frontiers in Oral Health, 2025, 6
ISSN
2673-4842
Publisher
Frontiers Media S.A.
Journal / Book Title
Frontiers in Oral Health
Volume
6
Copyright Statement
© 2025 Osama, Kocherry, Ullah, Ubaid, Ubaid, Ullah, Nawaz, Qasem, Odat, Farhan and Ahmed. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
License URL
Identifier
10.3390/geomatics5010015
Subjects
Ray, G.
Singh, M. Tropical Forest Carbon Accounting Through Deep Learning-Based Species Mapping and Tree Crown Delineation computer vision
species identification
aboveground biomass
deep learning
tropical forests
Publication Status
Published
Article Number
ARTN 1544921