ArticleNeuro-oncology advances
AI-based selection of tumor regions for genomic profiling in neuropathology.
Article in Neuro-oncology advances. 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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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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11 authors.
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Abstract
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 the 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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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.