Ki67 in cytological specimens of pancreatic neuroendocrine tumors: A literature review and validation of automated quantification.

Abstract

<h4>Introduction</h4>The Ki67 proliferation index is mandatory for grading, prognostication, and clinical decision-making in pancreatic neuroendocrine tumors (PanNETs). Automatic Ki67 quantification on cytology has been shown to be at least as accurate, less time-consuming, and more consistent than the current gold-standard manual determination. After a thorough literature review, we aimed to validate the Visiopharm image analysis software for automatic Ki67 quantification on diagnostic cell block material from PanNETs.<h4>Methods</h4>We conducted a retrospective study and assembled a cohort of 69 PanNETs from clinical routine with available endoscopic ultrasound fine needle aspiration cell block, Ki67, and synaptophysin immunostained slides. The manual Ki67 index, if available, was obtained from the original pathology report. Otherwise, a manual count was performed by a pathologist using a cell counter. The automatic Ki67 index was quantified through four consecutive algorithms from the Visiopharm Image Analysis software on aligned serial sections.<h4>Results</h4>Automatic Ki67 quantification showed a strong correlation with manual counting based on the non-parametric Spearman correlation coefficients of <i>r</i> = 0.786 [95% confidence interval (CI): 0.650-0.873, <i>p</i> < 0.001] and <i>r</i> = 0.721 (95% CI: 0.558-0.830, <i>p</i> < 0.001]<i>)</i>, for absolute Ki67 values and grades, respectively. Grade concordance showed excellent agreement for Grade 1 and Grade 3 tumors (91.89% and 83.3%) and rather moderate agreement for Grade 2 lesions (59.09%) due to underestimation. Bland-Altman analysis obtained excellent results, with a mean underestimation of digital versus manual quantification of 0.2265%.<h4>Conclusion</h4>Our findings show accurate assessment of the proliferation index from PanNETs using the Visiopharm software for digital Ki67 quantification and provide a prevalidation framework for the implementation of this technique in pathology practice. Discrepancies were mainly seen in Grade 2 tumors due to tumor heterogeneity of Grade 2 lesions. To this end, future research should seek refinement of the digital algorithms and examine the reliability of prognosis and clinical endpoints based on this technique.