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1.
Med Mol Morphol ; 47(3): 156-61, 2014 Sep.
Article in English | MEDLINE | ID: mdl-24092058

ABSTRACT

The epithelial lining of odontogenic keratocysts exhibits either parakeratosis or orthokeratosis. In 2005, the WHO classified odontogenic keratocysts with parakeratosis as keratocystic odontogenic tumors (KCOT). Odontogenic keratocysts with orthokeratosis were not classified as odontogenic tumors, but instead referred to as orthokeratinized odontogenic cysts (OOC). To clarify the difference between these two lesions, we investigated their biological characteristics using immunohistochemical studies for cytokeratins (CK) in KCOT and OOC as well as in dentigerous cysts (DC), radicular cysts (RC) and dermoid cysts (DMC). We examined twenty-five cases of KCOT, fifteen cases each of OOC, DC and RC, and ten cases of DMC. We studied the immunohistochemical expression of CK10, 13, 17 and 19. To evaluate the immunohistochemical staining pattern, we divided the epithelial lining of the lesions into three layers (surface layer: su, spinous layer: sp, basal layer: ba). For CK10, most OOC and DMC specimens of su and sp were positive. For CK13 and 19, most KCOT, DC and RC specimens of su and sp were positive. For CK17, most KCOT specimens of su and sp were positive. The percentages of total CK expression of su and sp, and ba of CK19 differed significantly between the lesions (P < 0.001). These results support the hypothesis that OOC originate from not the odontogenic apparatus, but the oral epithelial component.


Subject(s)
Dentigerous Cyst/metabolism , Dermoid Cyst/metabolism , Keratins/metabolism , Odontogenic Cysts/metabolism , Odontogenic Tumors/metabolism , Radicular Cyst/metabolism , Antibodies, Monoclonal , Epithelium/metabolism , Humans , Immunohistochemistry , Odontogenic Tumors/etiology
2.
J Digit Imaging ; 15(4): 226-31, 2002 Dec.
Article in English | MEDLINE | ID: mdl-12532255

ABSTRACT

To prefetch images in a hospital-wide picture archiving and communication system (PACS), a rule must be devised to permit accurate selection of examinations in which a patient's images are stored. We developed an inductive method to compose prefetch rules from practical data which were obtained in a hospital using a decision tree algorithm. Our methods were evaluated on data acquired in Osaka University Hospital for one month. The data collected consisted of 58,617 cases of consultation reservations, 643,797 examination histories of patients, and 323,993 records of image requests in PACS. Four parameters indicating whether the images of the patient were requested or not for each consultation reservation were derived from the database. As a result, the successful selection sensitivity for consultations in which images were requested was approximately 0.8, and the specificity for excluding consultations accurately where images were not requested was approximately 0.7.


Subject(s)
Algorithms , Information Storage and Retrieval , Radiology Information Systems , Data Display , Database Management Systems , Decision Trees , Humans , Referral and Consultation , Sensitivity and Specificity
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