研究者が腫瘍の分析に新たなAIアプローチを採用(Researchers take new AI approach to analyse tumours)

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2023-11-13 カロリンスカ研究所(KI)

◆スウェーデンのカロリンスカ研究所とSciLifeLabの研究者は、がん組織の大量データを解釈するために、衛星画像解析と生態学の人工知能(AI)技術を組み合わせた手法を開発。これにより、がん患者へのより個別化された治療が可能となり、がん組織の複雑なデータを新たな視点で理解できるようになりました。
◆新しい手法は臨床試験での適用が次のステップであり、がん免疫療法への応答差異や乳がん患者の化学療法必要性などの課題に取り組む予定です。

<関連情報>

NIPMAP: コミュニティ生態学によるマルチプレックス組織学データのニッチ表現型マッピング NIPMAP: niche-phenotype mapping of multiplex histology data by community ecology

Anissa El Marrahi,Fabio Lipreri,Ziqi Kang,Louise Gsell,Alper Eroglu,David Alber & Jean Hausser
Nature Communications  Published:07 November 2023
DOI:https://doi.org/10.1038/s41467-023-42878-z

研究者が腫瘍の分析に新たなAIアプローチを採用(Researchers take new AI approach to analyse tumours)

Abstract

Advances in multiplex histology allow surveying millions of cells, dozens of cell types, and up to thousands of phenotypes within the spatial context of tissue sections. This leads to a combinatorial challenge in (a) summarizing the cellular and phenotypic architecture of tissues and (b) identifying phenotypes with interesting spatial architecture. To address this, we combine ideas from community ecology and machine learning into niche-phenotype mapping (NIPMAP). NIPMAP takes advantage of geometric constraints on local cellular composition imposed by the niche structure of tissues in order to automatically segment tissue sections into niches and their interfaces. Projecting phenotypes on niches and their interfaces identifies previously-reported and previously-unreported spatially-driven phenotypes, concisely summarizes the phenotypic architecture of tissues, and reveals fundamental properties of tissue architecture. NIPMAP is applicable to both protein and RNA multiplex histology of healthy and diseased tissue. An open-source R/Python package implements NIPMAP.

医療・健康
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