npj Precision Oncology· 2026Q1
Deep learning predicts IDH1 mutation status in acute myeloid leukemia from bone marrow smears
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- 2026year
Short summary
An attention-based multiple-instance learning model predicts IDH1 mutation status in acute myeloid leukemia (AML) from bone marrow smear images with an ROC-AUC of 0.81.
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Key points
- An attention-based multiple-instance learning model was developed to predict IDH1 mutation status in AML.
- The model utilizes bone marrow smear whole-slide images from 436 AML patients.
- The model achieved a mean single pooled ROC-AUC of 0.81.
- Model predictions were associated with immature leukemia phenotypes in top tile analysis.
AI-generated from the title and abstract; the full text is not read.
Abstract
Abstract Acute myeloid leukemia (AML) with IDH1 mutations is amenable to targeted therapy, highlighting the need for reliable identification. We developed an attention-based multiple-instance learning model to predict IDH1 mutation status using bone marrow smear whole-slide images from 436 AML patients across four centers in Germany and the US, achieving a mean single pooled ROC-AUC of 0.81. In top tile analysis, model predictions for IDH1 were associated with immature leukemia phenotypes.
The authors' abstract, as published at the source. npj Precision Oncology, 2026 · DOI ↗
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Field: Computer Vision and Pattern Recognition
Computer Vision and Pattern RecognitionComputer Science