Unicystic ameloblastoma: Clinico-radiological and histopathological correlation with management

Unicystic ameloblastoma is a distinct entity of ameloblastoma characterized by slow growth and locally aggressive behavior. This retrospective study aimed to assess the efficacy of different treatment modalities of unicystic ameloblastoma, focusing on clinico-radiological and histopathological features. Data from patients diagnosed with unicystic ameloblastoma were retrospectively analyzed. Patients were categorized into luminal and intraluminal (Group A) and mural (Group B) variants based on the Ackermann classification, which has a significant influence on their biological behavior, treatment approaches, and prognosis. Patients in Group A underwent enucleation with chemical cauterization, peripheral ostectomy, and iodoform packing, whereas those in Group B were treated with resection and reconstruction. Post-operatively, the patients were subjected to radiographic assessments via digital orthopantomogram at regular intervals. Because of the rarity of unicystic ameloblastoma, only 17 patients were included in the study (Group A: 9 patients; Group B: 8 patients), with a mean follow-up of 4.9 years (range: 1.4-11.8 years). The primary outcome measure was the absence of recurrence, which indicated treatment success. No patient in either group experienced recurrence within the follow-up period. . This study provides evidence supporting the successful treatment of luminal and intraluminal variants of unicystic ameloblastoma in young individuals using a conservative approach. However, the more aggressive mural variant demonstrated favorable outcomes with radical treatment. These findings emphasize the importance of the Ackermann classification in guiding treatment decisions for unicystic ameloblastoma and contribute valuable insights into optimizing therapeutic strategies based on clinico-radiological and histopathological findings. (c) 2024 British Association of Plastic, Reconstructive and Aesthetic Surgeons. Published by Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.