Pediatric Adrenocortical Neoplasms: A Study Comparing Three Histopathological Scoring Systems.

Abstract

Adrenocortical neoplasms are rare in childhood. Their histopathological categorization into benign and malignant is often challenging, impacting further management. While the AFIP/Wieneke scoring system is widely used for the prognostic classification of these tumors, it has limitations. Few other tumor scoring systems have evolved over the past few years. These have been validated in adults but not yet in pediatric patients. We evaluated a cohort of pediatric adrenocortical neoplasms to assess the applicability of AFIP/Wieneke criteria and the recently introduced Helsinki score and reticulin algorithm in predicting clinical outcomes. A tumor was considered 'clinically aggressive' in the presence of any of the following: metastases, recurrence, progressive disease, or death due to disease. Cases without any such event were considered 'clinically good'. Event-free survival time was the duration from the date of clinical presentation to any post-operative adverse event. For overall survival analysis, the endpoint was either the last follow-up or death due to disease.Using ROC curve analysis, the obtained cut-off Helsinki score of 24 could stratify the cases into two prognostically relevant groups. Survival analysis showed significant differences in the event-free and overall survival of these two groups of patients, validating the proposed cut-off. None of the three histopathological scoring systems could predict an unfavorable outcome with 100% accuracy. All showed a sensitivity of ≥ 80%, with the reticulin algorithm achieving 100% sensitivity. The specificity and accuracy of the AFIP/Wieneke criteria were the lowest (62.5% and 73.08%, respectively). While the Helsinki score (at the cut-off score of 24) and the reticulin algorithm had similar accuracy rates (80.77%, and 80%, respectively), the specificity of the former was higher (81.25%) than the latter (68.75%). A separate analysis revealed that the Ki-67 index at a cut-off of 18% had a sensitivity of 80% and a specificity of 81.25% for predicting an unfavorable outcome.