A classification tree approach for pituitary adenomas

Alberto Righi, Patrizia Agati, Andrea Sisto, Giorgio Frank, Marco Faustini-Fustini, Raffaele Agati, Diego Mazzatenta, Anna Farnedi, Federico Menetti, Gianluca Marucci, Maria P. Foschini

Research output: Contribution to journalArticle

32 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)1627-1637
Number of pages11
JournalHuman Pathology
Volume43
Issue number10
DOIs
Publication statusPublished - Oct 2012

Fingerprint

Pituitary Neoplasms
ROC Curve
Regression Analysis
Recurrence
Mitotic Index
Observer Variation
Prolactin
Adenoma
Adrenocorticotropic Hormone
Neoplasms
Proteins

Keywords

  • Ki-67
  • p53
  • Pituitary adenoma
  • Prognosis

ASJC Scopus subject areas

  • Pathology and Forensic Medicine

Cite this

A classification tree approach for pituitary adenomas. / Righi, Alberto; Agati, Patrizia; Sisto, Andrea; Frank, Giorgio; Faustini-Fustini, Marco; Agati, Raffaele; Mazzatenta, Diego; Farnedi, Anna; Menetti, Federico; Marucci, Gianluca; Foschini, Maria P.

In: Human Pathology, Vol. 43, No. 10, 10.2012, p. 1627-1637.

Research output: Contribution to journalArticle

Righi, A, Agati, P, Sisto, A, Frank, G, Faustini-Fustini, M, Agati, R, Mazzatenta, D, Farnedi, A, Menetti, F, Marucci, G & Foschini, MP 2012, 'A classification tree approach for pituitary adenomas', Human Pathology, vol. 43, no. 10, pp. 1627-1637. https://doi.org/10.1016/j.humpath.2011.12.003
Righi, Alberto ; Agati, Patrizia ; Sisto, Andrea ; Frank, Giorgio ; Faustini-Fustini, Marco ; Agati, Raffaele ; Mazzatenta, Diego ; Farnedi, Anna ; Menetti, Federico ; Marucci, Gianluca ; Foschini, Maria P. / A classification tree approach for pituitary adenomas. In: Human Pathology. 2012 ; Vol. 43, No. 10. pp. 1627-1637.
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