# Diagnosis and grading of adrenal cortical carcinoma

## TL;DR

This paper reviews the updated WHO classification for adrenal cortical carcinoma, combining histopathology and molecular insights to improve diagnosis and grading.

## Contribution

The paper provides a practical diagnostic framework integrating molecular and histopathological criteria for adrenal cortical carcinoma.

## Key findings

- The WHO classification uses multiparametric scoring to assess invasion, architecture, and mitotic activity for ACC diagnosis.
- A two-tiered grading system based on mitotic count improves consistency in adrenal cortical carcinoma evaluation.
- Immunohistochemistry with markers like SF1 and Ki-67 aids in confirming diagnosis and predicting outcomes.

## Abstract

The 5th edition of the WHO classification of endocrine and neuroendocrine tumors represents a significant advancement in the diagnostic approach to adrenocortical carcinoma (ACC), integrating novel molecular insights with established histopathological criteria to enhance diagnostic accuracy and to refine prognostic assessment. This review outlines key histopathological features and diagnostic strategies for ACC, offering a practical framework for evaluation and grading in daily practice. The updated WHO classification reaffirms the central role of histopathology, employing multiparametric scoring systems that assess invasion, architectural and cytological features, mitotic activity, and necrosis. However, these parameters often pose interpretive challenges, and no single algorithm ensures complete sensitivity, specificity, or reproducibility. Therefore, combining diagnostic approaches is advisable, particularly in morphologically ambiguous cases. For tumor grading, the WHO employs a two-tiered system based on a mitotic count cut of 20 per 10 mm2, aiming to improve interinstitutional consistency. Immunohistochemistry remains essential for diagnostic confirmation and prognostic evaluation. Among available markers, SF1 is the most specific for adrenocortical origin, while Ki-67, mismatch repair proteins, p53, and β-catenin are useful for predicting patient outcomes or screening for hereditary predisposition. In this complex diagnostic setting, artificial intelligence holds potential to support ACC diagnostics. However, its application is limited by the rarity of the disease, histological variability, and the scarcity of large, well-annotated datasets necessary for algorithm development.