AI-based selection of tumor regions for genomic profiling in neuropathology
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ID: 317260
2026
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Abstract
Summary Automating pathology workflows with deep learning is increasingly feasible and clinically relevant. We present an AI-based method that identifies diagnostically relevant areas directly from H&E-stained slides, trained on 250 glioma cases using sparse, incomplete annotations. First, we show that attention-based multiple instance learning achieves accurate predictions despite noisy labels, easing annotation burden. Second, the model highlights tumor regions with high cellularity or grade, offering reproducible guidance for tissue selection. In a prospective evaluation, AI-selected regions achieved a mean Dice score of 0.743 [±0.077], supporting integration into neuropathology workflows as reliable guidance for molecular diagnostics
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| Reference Key |
openalex_W7164676791
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|---|---|
| Authors | Narmin Ghaffari Laleh, Lukas Friedrich, Fuat Kaan Aras, Katherine Hewitt, Leonille Schweizer, Zunamys I. Carrero, Dilan Savran, Daniel Haag, Silvia Barbosa, Felix Sahm, Jakob Nikolas Kather |
| Journal | Neuro-Oncology Advances |
| Year | 2026 |
| DOI |
10.1093/noajnl/vdag157
|
| URL | |
| Keywords | Keywords not found |
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