Is that mouth mass cancer?

VRU 2021

Suhyun Lee, Youjung Jang, Gahyun Lee, Sunghoon Jeon, Dongeun Kim, Jihye Choi

Background
The study aimed to characterize the computed tomography (CT) features distinguishing malignant from benign oral tumors in dogs. Malignant oral tumors are more prevalent and aggressive, often metastasizing to lymph nodes and lungs, whereas benign tumors can also display locally invasive behavior. The authors hypothesized that CT characteristics could reliably differentiate between malignant and benign oral tumors, guiding prognosis and treatment.

Methods
This retrospective, multi-center study analyzed 28 dogs with 31 histopathologically confirmed oral tumors (20 malignant and 8 benign). Pre- and post-contrast CT scans were reviewed independently by two observers. Tumors were assessed for size, location, contrast enhancement patterns, bone lysis, tooth loss, lymph node involvement, and secondary changes. Statistical analyses determined associations between CT findings and tumor malignancy.

Results
Malignant Tumors: Included malignant melanoma (14 cases), squamous cell carcinoma (4), adenocarcinoma (1), and fibrosarcoma (1). Common features:
-Larger tumor size (mean 30.1 mm).
-Heterogeneous contrast enhancement patterns.
-High prevalence of bone lysis (85%), tooth loss/displacement, and ipsilateral mandibular -lymphadenopathy.
-Lytic lesions extended to nasal cavities or orbits in 40% of cases.


Benign Tumors: Included giant cell granuloma (2 cases), peripheral odontogenic fibroma (2), acanthomatous ameloblastoma (2), plasmacytoma (1), and oncocytoma (1). Common features:
-Smaller size (mean 22.9 mm) with fewer heterogeneous enhancements.
-Bone lysis seen in specific types (e.g., ameloblastoma and granuloma) but confined to the oral cavity.
-Teeth displacement without loss.
-Periosteal reactions and mineralization occurred in both malignant and benign tumors.


Observer agreement for CT characteristics was excellent.

Limitations
Small sample size and fewer benign tumor cases limit generalizability.
Variable CT acquisition settings due to the multi-center retrospective design.
Cytological or histological lymph node evaluation was incomplete, reducing certainty regarding metastasis.
Selection bias: Benign tumors may have been underrepresented due to less frequent CT evaluation.

Conclusions
Malignant oral tumors in dogs tend to exhibit larger size, heterogeneous enhancement, extensive bone invasion, and lymphadenopathy. Although CT provides critical diagnostic insights, overlapping features between tumor types necessitate histopathological confirmation for definitive diagnosis. This study supports CT as a valuable tool for treatment planning and prognosis in canine oral oncology.

F I G U R E 1 Post-contrast CT images comparing multiple masses in an oral cavity of one dog with malignant tumors in a dorsal plane (A) withseveral transverse plane images of another dog affected by benign tumor (B-D). (A) In a 10-year-old, neutered male, poodle with malignantmelanoma, two masses with well-defined margins are found in the right mandible (white arrow) with mandibular bone lysis, as well as at thetonsillar regions (white arrowhead). The right mandibular lymph node (white dot arrow) and right medial retropharyngeal lymph node (whiteasterisk) were markedly enlarged. (B-D) In a 14-year-old male, Cocker Spaniel with giant cell granuloma, multiple masses were found in the leftmandible (B) and in the right (C) and left maxilla (D). The mass in the left mandible (white arrow) was showed heterogeneous pattern withill-defined margin. The masses in the right (white arrowhead) and left (white dot arrow) maxilla showed homogeneous pattern with well-definedmargin. Medium frequency algorithm for soft tissue window with window level = 40 HU, window width = 400 HU. Images were acquired in sternalrecumbency and the left side of the animal is indicated by L. (A) slice thickness = 1.0 mm. (B-D) slice thickness = 2.0 mm

How did we do?

Login or Subscribe to participate in polls.

Disclaimer: The summary generated in this email was created by an AI large language model. Therefore errors may occur. Reading the article is the best way to understand the scholarly work. The figure presented here remains the property of the publisher or author and subject to the applicable copyright agreement. It is reproduced here as an educational work. If you have any questions or concerns about the work presented here, reply to this email.