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arXiv Machine Learning By Jonathan Legrand (IMB, MONC), Aguirre Mimoun (CHU Bordeaux), Baudouin Denis de Senneville (IMB, MONC), Audrey Bidet (CHU Bordeaux), Pierre-Yves Dumas (CHU Bordeaux, Inserm U1312 - BRIC), Christ\`ele Etchegaray (MONC, IMB)

Interpretable Multi-Instance Learning Enables Early Prediction of Key Molecular Alterations from Routine Flow Cytometry in Acute Myeloid Leukemia

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An interpretable multi‑instance learning classifier based on a decision tree was developed to predict NPM1 and FLT3‑ITD mutations in acute myeloid leukemia using routine flow cytometry data. In cross‑validation on 197 patients, the model achieved AUROCs of 0.96 for NPM1 and 0.86 for FLT3‑ITD, outperforming a clinical baseline and matching deep learning methods. On an independent cohort of 161 patients, it maintained high performance with AUROCs of 0.90 and 0.82, and positive predictive values of 0.87 and 0.68, while cell‑level interpretation recovered known immunophenotypic signatures.

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