Karyotype AI for Precision Oncology

• URL: http://arxiv.org/abs/2211.14312v5
• Date: Fri, 21 Mar 2025 16:34:17 GMT
• Title: Karyotype AI for Precision Oncology
• Authors: Zahra Shamsi, Isaac Reid, Drew Bryant, Jacob Wilson, Xiaoyu Qu, Avinava Dubey, Konik Kothari, Mostafa Dehghani, Mariya Chavarha, Valerii Likhosherstov, Brian Williams, Michael Frumkin, Fred Appelbaum, Krzysztof Choromanski, Ali Bashir, Min Fang,
• Abstract summary: We present a machine learning method capable of accurately detecting chromosome abnormalities that cause blood cancers.<n>The pipeline is built on a series of fine-tuned Vision Transformers.<n>We achieve a high precision-recall score of 94% AUC for the clinically significant del(5q) and t(9;22) anomalies.
• Score: 23.346771200529332
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a machine learning method capable of accurately detecting chromosome abnormalities that cause blood cancers directly from microscope images of the metaphase stage of cell division. The pipeline is built on a series of fine-tuned Vision Transformers. Current state of the art (and standard clinical practice) requires expensive, manual expert analysis, whereas our pipeline takes only 15 seconds per metaphase image. Using a novel pretraining-finetuning strategy to mitigate the challenge of data scarcity, we achieve a high precision-recall score of 94% AUC for the clinically significant del(5q) and t(9;22) anomalies. Our method also unlocks zero-shot detection of rare aberrations based on model latent embeddings. The ability to quickly, accurately, and scalably diagnose genetic abnormalities directly from metaphase images could transform karyotyping practice and improve patient outcomes. We will make code publicly available.

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