Clinicopathological study of a series of 92 adrenocortical carcinomas: from a proposal of simplified diagnostic algorithm to prognostic stratification
Article first published online: 4 NOV 2009
© 2009 The Authors. Journal compilation © 2009 Blackwell Publishing Limited
Volume 55, Issue 5, pages 535–543, November 2009
How to Cite
Volante, M., Bollito, E., Sperone, P., Tavaglione, V., Daffara, F., Porpiglia, F., Terzolo, M., Berruti, A. and Papotti, M. (2009), Clinicopathological study of a series of 92 adrenocortical carcinomas: from a proposal of simplified diagnostic algorithm to prognostic stratification. Histopathology, 55: 535–543. doi: 10.1111/j.1365-2559.2009.03423.x
- Issue published online: 4 NOV 2009
- Article first published online: 4 NOV 2009
- Date of submission 21 January 2009 Accepted for publication 6 May 2009
- adrenal cortex;
- reticulin stain
Aims: Pathological diagnosis of adrenocortical carcinoma relies on several microscopic features commonly used in combination in different scoring systems that are sometimes subjective and/or time consuming. The aim was to investigate the impact of individual pathological parameters in the diagnosis and prognosis of adrenocortical carcinoma.
Methods and results: The series included 92 malignant cases and a control series of 47 adenomas, all classified according to Weiss score criteria. The presence of disruption of the reticular network, as highlighted by histochemical staining, was present in all adrenocortical carcinomas and the inclusion of at least one of the three following additional parameters – mitotic index >5/50 high-power fields (HPF), presence of necrosis and presence of vascular invasion – gave an algorithm with 100% sensitivity and specificity to recognize malignant tumours according to the Weiss system, with easier and more practical applicability. Moreover, on multivariate analysis, stage III/IV and mitotic count >9/50 HPF showed a strong adverse impact on disease-free and overall survival, leading to the identification of three risk groups affected by a significantly different prognosis.
Conclusions: We have defined an easy-to-perform and highly specific and sensitive algorithm for the diagnosis and prognostic categorization of adrenocortical tumours.