title: Imaging features contributing to the diagnosis of ameloblastomas and keratocystic odontogenic tumours: logistic regression analysis.
abstract: OBJECTIVE
  The aim of this study was to clarify the characteristic imaging features that can be used to differentiate ameloblastomas from keratocystic odontogenic tumours and to examine the significant imaging features contributing to a correct diagnosis.
  
  
  METHODS
  60 observers (39 specialists in oral and maxillofacial radiology and 21 non-specialists) examined CT and/or panoramic images of 10 ameloblastomas and 10 keratocystic odontogenic tumours shown on a webpage and made diagnoses. Their correct answer ratios were then calculated. The imaging features of the tumours were evaluated and expressed as binary numbers or quantitative values. The imaging features that contributed to a correct diagnosis were elucidated using logistic regression analysis.
  
  
  RESULTS
  The mean correct answer ratio was 61.3% ± 17.2% for the diagnosis of ameloblastomas and keratocystic odontogenic tumours. CT images produced higher correct answer ratios for diagnosis of keratocystic odontogenic tumours by specialists. The significantly different imaging features between ameloblastomas and keratocystic odontogenic tumours were the degree of bone expansion and the presence of high-density areas. The significant imaging features contributing to a correct imaging diagnosis were the number of locules, the presence of high-density areas and the inclusion of impacted teeth.
  
  
  CONCLUSIONS
  The presence of high-density areas is the most useful feature in the differential diagnosis of ameloblastomas and keratocystic odontogenic tumours based on comparison of the imaging features of both tumours and examination of the diagnostic contributions of these features.
authors.affiliation: Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry, 2-11 Suemori-dori, Chikusa-ku, Nagoya 464-8651, Japan. yoshiko@dpc.agu.ac.jp
authors.forename: Y
authors.initials: Y
authors.lastname: Ariji
authors.forename: M
authors.initials: M
authors.lastname: Morita
authors.forename: A
authors.initials: A
authors.lastname: Katsumata
authors.forename: Y
authors.initials: Y
authors.lastname: Sugita
authors.forename: M
authors.initials: M
authors.lastname: Naitoh
authors.forename: M
authors.initials: M
authors.lastname: Goto
authors.forename: M
authors.initials: M
authors.lastname: Izumi
authors.forename: Y
authors.initials: Y
authors.lastname: Kise
authors.forename: K
authors.initials: K
authors.lastname: Shimozato
authors.forename: K
authors.initials: K
authors.lastname: Kurita
authors.forename: H
authors.initials: H
authors.lastname: Maeda
authors.forename: E
authors.initials: E
authors.lastname: Ariji
mesh_terms: D000293:Adolescent; D000328:Adult; D000564:Ameloblastoma / Q000000981:diagnostic imaging* / Q000473:pathology; D002648:Child; D003720:Densitometry; D003937:Diagnosis, Differential; D005260:Female; D006801:Humans; D020407:Internet; D016015:Logistic Models; D008297:Male; D008339:Mandibular Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology; D008875:Middle Aged; D016017:Odds Ratio; D009808:Odontogenic Tumors / Q000000981:diagnostic imaging* / Q000473:pathology; D010363:Pattern Recognition, Automated; D011862:Radiography, Panoramic; D018709:Statistics, Nonparametric; D014057:Tomography, X-Ray Computed; D055815:Young Adult