人工智能直接从乳腺癌组织学预测多类分子特征和亚型：一项多中心回顾性研究。

IF 12.5 2区医学 Q1 SURGERY

International journal of surgery Pub Date : 2025-04-01 DOI:10.1097/JS9.0000000000002220

Xiangyang Zhang, Yang Chen, Changjing Cai, Yifeng Wang, Jun Tan, Zijie Fang, Le Wei, Zhuchen Shao, Liwen Wang, Tiezheng Qi, Yihan Liu, Zhaohui Jiang, Yin Li, Ying Han, Tibera Kagemulo Rugambwa, Shan Zeng, Haoqian Wang, Hong Shen, Yongbing Zhang

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引用次数: 0

摘要

检测乳腺癌的生物标志物会产生额外的费用和组织负担。我们提出了一种基于深度学习的算法（BBMIL），直接从苏木精和伊红染色的组织病理学图像中预测经典生物标志物、免疫治疗相关基因特征和预后相关亚型。在经典生物标志物、免疫治疗相关基因特征和亚型预测方面，BBMIL在比较算法中表现最好。

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Artificial intelligence predicts multiclass molecular signatures and subtypes directly from breast cancer histology: a multicenter retrospective study.

Detection of biomarkers of breast cancer incurs additional costs and tissue burden. We propose a deep learning-based algorithm (BBMIL) to predict classical biomarkers, immunotherapy-associated gene signatures, and prognosis-associated subtypes directly from hematoxylin and eosin stained histopathology images. BBMIL showed the best performance among comparative algorithms on the prediction of classical biomarkers, immunotherapy related gene signatures, and subtypes.

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来源期刊

International journal of surgery SURGERY-

CiteScore

17.70

自引率

3.30%

发文量

审稿时长

6-12 weeks

期刊介绍： The International Journal of Surgery (IJS) has a broad scope, encompassing all surgical specialties. Its primary objective is to facilitate the exchange of crucial ideas and lines of thought between and across these specialties.By doing so, the journal aims to counter the growing trend of increasing sub-specialization, which can result in "tunnel-vision" and the isolation of significant surgical advancements within specific specialties.