Does Automated Versus Manual Ki67 Labeling Index Assessment Influence Risk Stratification in Patients with Localized Adrenocortical Carcinoma? Lessons from the ADIUVO Trial
Marijn A. Vermeulen, Linde M. Amelung, Otilia Kimpel, Ulrich Dischinger, Vittoria Basile, Darko Kastelan, Hélène Lasolle, Isabelle Bourdeau, Svenja Nölting, Paola Loli, Magalie Haissaguerre, Alfredo Berruti, Martin Fassnacht, Massimo Terzolo, Ronald R. de KrijgerBackground/Objectives: Adrenocortical carcinoma (ACC) is a rare tumor. Diagnosis relies on combined histopathological criteria that, in a multifactorial scoring system, may suggest malignancy. The recent ADIUVO trial, a multicenter trial randomizing ACC patients between mitotane treatment or surveillance only, demonstrated that patients with localized, low-grade ACC (Ki67 labeling index (LI) ≤ 10%) have a better prognosis than historically anticipated, questioning the routine use of adjuvant mitotane. This post hoc analysis aimed to investigate whether automated, centralized Ki67 LI assessment improves prognostic stratification beyond expert manual assessment in low-risk ACC patients enrolled in the original ADIUVO trial. Methods: Ki67 LI was centrally reassessed in 70 ADIUVO patients by digitizing slides and applying an automated algorithm to manually selected hotspots. Primary endpoints were correlation between methods and the impact of automated scoring on recurrence-free survival (RFS) and overall survival (OS). Results: Automated Ki67 LI assessment was feasible in 47 patients. Manual and automated Ki67 LI values showed good agreement (mean 5.6 ± 3.1% vs. 5.4 ± 5.3%). Automated scoring identified eight patients with KI67 LI > 10%. In this subgroup, RFS and OS did not differ significantly between patients treated with mitotane and those under surveillance. Patients with an automated Ki67 LI > 10% had larger tumors (median 16.0 cm vs. 7.0 cm, p = 0.003). Conclusions: Our findings support the original ADIUVO results and support continued use of manual expert Ki67 LI scoring. This traditional approach remains robust and pragmatic for most clinical settings, especially where automated systems are not readily accessible.