ArticleJournal of neuropathology and experimental neurology2026
Quantification of Ki-67 labeling index in pediatric brain tumor immunohistochemistry images.
Article in Journal of neuropathology and experimental neurology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Quantification of the Kiel 67 (Ki-67) labeling index (LI) is critical for assessing proliferation and prognosis in tumors but manual scoring remains a common practice. We present an automated framework for Ki-67 scoring in whole slide images (WSIs) developed for research settings using an Apache Groovy code script for QuPath and complemented by a Python postprocessing script that provides cell density maps and summary tables. Tissue segmentation is performed by pixel classifiers and cell segmentation is conducted using StarDist, a deep learning model, followed by adaptive thresholding to classify Ki-67 positive and negative nuclei. The pipeline was applied to a cohort of 632 pediatric brain tumor cases with 734 Ki-67 WSIs from the Children's Brain Tumor Network. Medulloblastomas showed the highest Ki-67 LI (median: 19.84), followed by atypical teratoid rhabdoid tumors (median: 19.36), brainstem glioma-diffuse intrinsic pontine gliomas (median: 11.50), high-grade gliomas (grades 3, 4) (median: 9.50), and ependymomas (median: 5.88). Lower indices were found in meningiomas (median: 1.84) and the lowest were seen in low-grade gliomas (grades 1, 2) (median: 0.85), dysembryoplastic neuroepithelial tumors (median: 0.63), and gangliogliomas (median: 0.50). The results demonstrate a significant correlation (P < .05) in Ki-67 LI across most of the tumor families/types aligning with neuro-oncology and neuropathology consensus.
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