AI accurately identifies targetable alterations in lung cancer histological images

IF 81.1 1区医学 Q1 ONCOLOGY

Nature Reviews Clinical Oncology Pub Date : 2025-02-10 DOI:10.1038/s41571-025-00999-y

Hortense Le, Aristotelis Tsirigos

引用次数: 0

Abstract

DeepGEM, an artificial intelligence (AI)-based model, accurately predicts the presence of key genomic alterations in histological slides prepared from samples obtained from patients with lung cancer. This approach provides a cost-effective alternative to genomic testing, generates spatial mutation maps and might support personalized treatment strategies. Validated in diverse datasets, DeepGEM highlights the potential of AI to transform precision oncology and improve global healthcare equity.

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来源期刊

Nature Reviews Clinical Oncology 医学-肿瘤学

CiteScore

99.40

自引率

0.40%

发文量

114

审稿时长

6-12 weeks

期刊介绍： Nature Reviews publishes clinical content authored by internationally renowned clinical academics and researchers, catering to readers in the medical sciences at postgraduate levels and beyond. Although targeted at practicing doctors, researchers, and academics within specific specialties, the aim is to ensure accessibility for readers across various medical disciplines. The journal features in-depth Reviews offering authoritative and current information, contextualizing topics within the history and development of a field. Perspectives, News & Views articles, and the Research Highlights section provide topical discussions, opinions, and filtered primary research from diverse medical journals.