title: A classification tree approach for pituitary adenomas.
abstract: It is difficult to evaluate the recurrence and progression potential of pituitary adenomas at presentation. The World Health Organization classification of endocrine tumors suggests that invasion of the surrounding structures, size at presentation, an elevated mitotic index, a Ki-67 labeling index higher than 3%, and extensive p53 expression are indicators of aggressive behavior. Nevertheless, Ki-67 and p53 labeling index evaluation is subject to interobserver variability, and their cutoff values are controversial. In the present study, the prognostic value of Ki-67 and p53 protein labeling indices and their correlation with clinical and radiologic parameters were evaluated using digital image analysis in a series of 166 pituitary adenomas in patients having undergone a follow-up of at least 6 years to evaluate the impact on the recurrence and progression potential of pituitary adenomas. The data were analyzed using the receiver operating characteristic curve and classification and regression tree analysis. The results showed that, in the unstratified data set, the commonly used threshold of the Ki-67 index of 3% has a high specificity (89.5%) but a low sensitivity (53.8%). Unsatisfactory performance results were obtained by performing receiver operating characteristic curve analysis on the p53 labeling index. On the contrary, the classification and regression tree analysis-derived tree demonstrated that each pituitary adenoma subtype has specific prognostic factors. Specifically, the Ki-67 labeling index is a useful prognostic factor in nonfunctioning, adrenocorticotropin, and prolactin adenomas, but with different thresholds. In conclusion, our study emphasizes that the term pituitary adenomas includes different types of tumors, each one having specific prognostic factors.
authors.affiliation: Section of Anatomic Pathology, Department of Haematology and Oncology L e A. Seragnoli University of Bologna, Bellaria Hospital, 40139 Bologna, Italy.
authors.forename: Alberto
authors.initials: A
authors.lastname: Righi
authors.forename: Patrizia
authors.initials: P
authors.lastname: Agati
authors.forename: Andrea
authors.initials: A
authors.lastname: Sisto
authors.forename: Giorgio
authors.initials: G
authors.lastname: Frank
authors.forename: Marco
authors.initials: M
authors.lastname: Faustini-Fustini
authors.forename: Raffaele
authors.initials: R
authors.lastname: Agati
authors.forename: Diego
authors.initials: D
authors.lastname: Mazzatenta
authors.forename: Anna
authors.initials: A
authors.lastname: Farnedi
authors.forename: Federico
authors.initials: F
authors.lastname: Menetti
authors.forename: Gianluca
authors.initials: G
authors.lastname: Marucci
authors.forename: Maria P
authors.initials: MP
authors.lastname: Foschini
mesh_terms: D000328:Adult; D019540:Area Under Curve; D014408:Biomarkers, Tumor / Q000032:analysis / Q000378:metabolism; D018450:Disease Progression; D005260:Female; D006801:Humans; D019394:Ki-67 Antigen / Q000032:analysis / Q000378:metabolism; D008297:Male; D008875:Middle Aged; D010911:Pituitary Neoplasms / Q000145:classification* / Q000378:metabolism / Q000473:pathology; D011379:Prognosis; D012372:ROC Curve; D012680:Sensitivity and Specificity; D016159:Tumor Suppressor Protein p53 / Q000032:analysis / Q000378:metabolism
chemical_list: D014408:Biomarkers, Tumor; D019394:Ki-67 Antigen; D016159:Tumor Suppressor Protein p53