However, several points merit discussion. Although the digital H-score improves objectivity, the analysis is based on the manual selection of three representative regions of interest (ROIs). This process may still introduce selection bias, especially in tumors with marked heterogeneity. In contrast, a recent study has employed Whole Slide Imaging (WSI) for PR quantification, allowing assessment of the entire tumor area and potentially providing a more accurate reflection of intra-tumoral heterogeneity [3]. Furthermore, while PR was quantified digitally, the Ki-67 index and mitotic counts were assessed manually—a limitation appropriately noted by the authors. Although proliferation markers are integral to WHO grading, the comparative prognostic weight of Ki-67 and PR expression can fluctuate across different clinical cohorts and study designs. In some larger digitally analyzed cohorts, Ki-67 has shown a more consistent association with disease-free survival (DFS) than PR expression, though both markers remain complementary in risk assessment [3,4]. While PR expression serves as a reliable correlate of tumor grade, its independent utility in predicting recurrence appears less robust than digitally quantified Ki-67 hotspot analyses, which offer superior predictive accuracy as demonstrated by Güreşci et al [3]. Critically, cIMPACT-NOW update 8 mandates integrating molecular and copy-number alterations into grading [5]. TERT promoter mutations and CDKN2A/B homozygous deletions are established as independent criteria for CNS WHO grade 3, regardless of histology [4]. Furthermore, histologically low-grade cases with 1p deletion plus 22q loss and/or NF2 variants are now assigned to CNS WHO grade 2, providing objective stratification for borderline scenarios [5]. As acknowledged by the authors, the absence of these molecular data is a limitation, as they are now essential for the standardized grading of aggressive meningiomas. While digital PR and Ki-67 assessment provide useful surrogate data, they cannot fully substitute for molecular testing in borderline or high-grade cases.
Finally, the use of breast-trained algorithms for PR assessment in meningiomas represents a pragmatic and effective application of digital pathology, although biological differences between tumor types should be considered. Future studies integrating automated digital quantification of PR, Ki-67, and mitotic activity within a unified workflow may further reduce inter-observer variability and enhance analytical consistency.
Overall, this study provides a strong basis for the incorporation of digital tools into routine neuropathology practice, supporting the role of PR as a reliable marker of tumor grade and highlighting the value of progressing toward fully automated, whole-slide digital workflows for broader clinical implementation.
1) Akca Y, Gökçe EB. Digital Quantification of Progesterone Receptor Expression in Meningiomas: Associations with WHO Grade and Clinicopathological Features. Digital Quantification of Progesterone Receptor Expression in Meningiomas: Associations with WHO Grade and Clinicopathological Features. Turk Patoloji Derg. 2026;42(1):24-30. https://doi.org/10.5146/tjpath.2025.14663
2) Akca Y, Gokce EB. Evaluation of a breast cancer-trained digital progesterone receptor scoring algorithm in meningiomas: A comparative study with manual assessment. Ann Diagn Pathol. 2025;152577. https://doi.org/10.1016/j.anndiagpath.2025.152577
3) Guresci S, Aydogdu OB, Secen AE, Uzel B. Assessing the predictive value of Ki-67 and progesterone receptor algorithms for recurrence and disease-free survival in meningiomas. Ann Diagn Pathol. 2025;75:152441. https://doi.org/10.1016/j.anndiagpath.2025.152441
