Volume 80, February 2026, 152577Author links open overlay panel, Highlights•

A breast cancer–trained digital PR algorithm was evaluated in meningiomas.

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The FDA-cleared Digital Read Algorithm showed strong concordance with manual scoring.

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Spearman, Pearson, and ICC analyses confirmed robust agreement across scoring rounds.

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Automated scoring ensures objectivity and reproducibility in neuro-oncology practice.

Abstract

The primary aim of this study was to evaluate the FDA-cleared Ventana® Digital Progesterone (PR) scoring algorithm, originally designed for breast carcinoma, in meningiomas. This work was conducted retrospectively and included 129 meningioma cases diagnosed between 2018 and 2024, with only patients who underwent progesterone receptor immunohistochemical staining at initial diagnosis being eligible. Archived PR-stained slides were digitized with the VENTANA® DP200 scanner and analyzed using the uPath PR (1E2) algorithm. Three independent scoring rounds were performed, and digital results were compared with manual assessments using the H-score method. Correlations between digital and manual scores were evaluated by Spearman's rank correlation coefficient, Pearson's correlation coefficient, and the Intraclass Correlation Coefficient (ICC). Spearman's coefficients exceeded 0.75 across all scoring rounds (p < 0.001), indicating strong monotonic relationships. Pearson's coefficients showed strong linear associations in two rounds (r = 0.701 and r = 0.800) and weaker alignment in one (r = 0.188). ICC values indicated near-perfect agreement in one round (0.968), good agreement in another (0.755), and moderate agreement in the third (0.633). These findings demonstrate that the Ventana® Digital PR algorithm provides a reliable and feasible alternative to manual scoring in meningiomas, offering objectivity, reproducibility, and diagnostic applicability in neuro-oncology.

Section snippetsIntroductıon

Digital pathology powered by whole-slide imaging (WSI) and artificial intelligence (AI) is reshaping histopathology workflows by improving reproducibility and operational efficiency [1]. In immunohistochemistry (IHC), digital scoring algorithms facilitate standardized quantification of biomarkers, aligning with modern precision-medicine objectives [2]. Digital pathology adoption continues to expand globally, yet widespread implementation still faces key obstacles—including infrastructure

Study design and cases

This retrospective study included 129 patients diagnosed with meningioma between 2018 and 2024 at Gaziantep University. The study was approved by the Gaziantep University Non-Interventional Clinical Research Ethics Committee (Approval No: 2024/322). All procedures performed in this study involving human participants were conducted in accordance with the ethical standards of the institutional and national research committee and with the 1964 Helsinki declaration and its later amendments.

Results

Digital and manual progesterone receptor (PR) scores were systematically compared across three independent scoring rounds, each evaluated using the classical H-score method (%Strong × 3 + %Moderate × 2 + %Weak × 1). Overall, the analyses demonstrated strong concordance between the two assessment methods, supporting the robustness of the digital algorithm under routine diagnostic conditions.

Spearman's rank correlation coefficients consistently exceeded 0.75 across all rounds (ρ = 0.756, 0.762,

Dıscussıon

Our primary aim was to evaluate whether a digital PR scoring algorithm, originally developed for breast carcinoma, could be applied reliably to meningiomas and serve as an alternative to manual scoring. This hypothesis addresses two critical questions in modern pathology: first, the general reliability of digital pathology tools compared to traditional manual evaluation, and second, the adaptability of breast cancer–specific algorithms to other tumor types. Given that the uPath algorithm was

Conclusıon

Our study suggests that the FDA-cleared Ventana® Digital PR scoring algorithm, originally trained for breast carcinoma, can be effectively applied to meningioma tissue evaluation. The strong concordance with manual assessment indicates its promise as a clinically feasible tool. These findings support its potential broader application in neuro-oncology and underscore the value of digital pathology as an objective and efficient complement to traditional histopathological methods.

Fundıng

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Declaratıon of Generatıve AI and AI-assisted technologies in the writing process

During the preparation of this work the authors used ChatGPT (OpenAI, San Francisco, CA, USA) to improve the readability and language of the manuscript. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the published article.

CRediT authorship contribution statement

Yasemin Akca: Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Software, Resources, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization. Elif Busra Gokce: Writing – review & editing, Visualization, Data curation, Conceptualization.

Declaration of competing interest

The authors declare no conflicts of interest.

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