# Comparison of the Manual, Semiautomatic, and Automatic Selection and Leveling of Hot Spots in Whole Slide Images for Ki-67 Quantification in Meningiomas

## TL;DR

This study compares manual, semiautomatic, and automatic methods for selecting hot spots in whole slide images to assess Ki-67 levels in meningioma patients.

## Contribution

The paper introduces an automatic hot-spot selection method that shows high agreement with manual methods in Ki-67 quantification.

## Key findings

- Automatic hot-spot selection shows high agreement with manual selection by pathologists.
- Agreement between automatic and manual methods is higher than between traditional microscopy and automatic methods.
- Automation can improve reliability and support physicians in Ki-67 scoring for meningiomas.

## Abstract

Background. This paper presents the study concerning hot-spot selection in the assessment of whole slide images of tissue sections collected from meningioma patients. The samples were immunohistochemically stained to determine the Ki-67/MIB-1 proliferation index used for prognosis and treatment planning. Objective. The observer performance was examined by comparing results of the proposed method of automatic hot-spot selection in whole slide images, results of traditional scoring under a microscope, and results of a pathologist's manual hot-spot selection. Methods. The results of scoring the Ki-67 index using optical scoring under a microscope, software for Ki-67 index quantification based on hot spots selected by two pathologists (resp., once and three times), and the same software but on hot spots selected by proposed automatic methods were compared using Kendall's tau-b statistics. Results. Results show intra- and interobserver agreement. The agreement between Ki-67 scoring with manual and automatic hot-spot selection is high, while agreement between Ki-67 index scoring results in whole slide images and traditional microscopic examination is lower. Conclusions. The agreement observed for the three scoring methods shows that automation of area selection is an effective tool in supporting physicians and in increasing the reliability of Ki-67 scoring in meningioma.