Histopathological grading of adenoid cystic carcinoma of the head and neck: analysis of currently used grading systems and proposal for a simplified grading scheme

Background
Histopathological grading of adenoid cystic carcinoma (ACC) is a controversial issue. It is generally agreed that solid type ACC has a relatively poor prognosis. However, the amount of solid regions within this often mixed type tumor that predicts a poor prognosis is not firmly established. Some authors stipulate that the presence of a solid component regardless of the amount is a poor prognosticator where others argue that the amount should be taken into consideration. Two grading systems most commonly used are those described by Perzin et al./Szanto et al. and Spiro et al., respectively. They report that prognosis of ACC is poor if >30% and >50% of the tumor volume has a solid growth pattern, respectively.

Material and methods
The described grading systems are applied to a series of 81 surgically treated cases of ACC at the VU University Medical Center, Amsterdam, The Netherlands. Moreover, we introduced an alternative grading system, in which the presence of a solid component, irrespective of its amount, is considered. All three systems of grading were tested for inter-observer concordance and prediction of prognosis.

Results
Inter-observer concordance for grading ACC according to Perzin et al./Szanto et al. and Spiro et al., proved to be moderate with Kappa Scores of 0.393 and 0.433, respectively. Our alternative grading system yielded inter-observer concordance with a Cohen’s kappa result of 0.990. All systems were comparable in discriminating patients with poor clinical outcome. Histopathological grade proved to be an independent prognosticator.

Conclusion
The presence of any solid component in ACC is a negative prognosticator, and can histopathologically be diagnosed with a high reliability. These results suggest to merely register the presence or absence of a solid tumor component since its inter-observer variability is very low, its reproducibility is high and its predictive value is comparable to the traditional grading systems used.