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深度学习利用常规染色的全切片图像预测管腔A型乳腺癌的亚型异质性和预后。

Deep Learning Predicts Subtype Heterogeneity and Outcomes in Luminal A Breast Cancer Using Routinely Stained Whole-Slide Images.

作者信息

Kurian Nikhil Cherian, Gann Peter H, Kumar Neeraj, McGregor Stephanie M, Verma Ruchika, Sethi Amit

机构信息

Department of Electrical Engineering, Indian Institute of Technology-Bombay, Mumbai, India.

Australian Institute for Machine Learning, University of Adelaide, Adelaide, Australia.

出版信息

Cancer Res Commun. 2025 Jan 1;5(1):157-166. doi: 10.1158/2767-9764.CRC-24-0397.

DOI:10.1158/2767-9764.CRC-24-0397
PMID:39740059
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11770635/
Abstract

A deep learning model, trained using transcriptomic data, inexpensively quantifies and fine-maps ITH due to subtype admixture in routine images of LumA breast cancer, the most favorable subtype. This new approach could facilitate exploration of the mechanisms behind such heterogeneity and its impact on selection of therapy for individual patients.

摘要

一种利用转录组数据训练的深度学习模型，能够在最有利的亚型——LumA乳腺癌的常规图像中，因亚型混合而对肿瘤内异质性进行低成本量化和精细定位。这种新方法有助于探索这种异质性背后的机制及其对个体患者治疗选择的影响。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6c92/11770635/6bd511dc284d/crc-24-0397_f6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6c92/11770635/395b63a4276d/crc-24-0397_f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6c92/11770635/f8b5d790048a/crc-24-0397_f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6c92/11770635/d30d20eff577/crc-24-0397_f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6c92/11770635/5b7a309bd6ec/crc-24-0397_f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6c92/11770635/204edf19eaf3/crc-24-0397_f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6c92/11770635/6bd511dc284d/crc-24-0397_f6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6c92/11770635/395b63a4276d/crc-24-0397_f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6c92/11770635/f8b5d790048a/crc-24-0397_f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6c92/11770635/d30d20eff577/crc-24-0397_f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6c92/11770635/5b7a309bd6ec/crc-24-0397_f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6c92/11770635/204edf19eaf3/crc-24-0397_f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6c92/11770635/6bd511dc284d/crc-24-0397_f6.jpg

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本文引用的文献

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Accelerating histopathology workflows with generative AI-based virtually multiplexed tumour profiling.借助基于生成式人工智能的虚拟多重肿瘤分析加速组织病理学工作流程。
Nat Mach Intell. 2024;6(9):1077-1093. doi: 10.1038/s42256-024-00889-5. Epub 2024 Sep 9.
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TCF12-regulated GRB7 facilitates the HER2+ breast cancer progression by activating Notch1 signaling pathway.由TCF12调控的GRB7通过激活Notch1信号通路促进HER2阳性乳腺癌进展。
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Cancer Evolution: A Multifaceted Affair.
癌症进化：一个多方面的问题。
Cancer Discov. 2024 Jan 12;14(1):36-48. doi: 10.1158/2159-8290.CD-23-0530.
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Spatial transcriptomics and the anatomical pathologist: Molecular meets morphology.空间转录组学与解剖病理学家：分子遇见形态学。
Histopathology. 2024 Mar;84(4):577-586. doi: 10.1111/his.15093. Epub 2023 Nov 22.
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Single-cell morphological and topological atlas reveals the ecosystem diversity of human breast cancer.单细胞形态和拓扑图谱揭示了人类乳腺癌的生态系统多样性。
Nat Commun. 2023 Oct 25;14(1):6796. doi: 10.1038/s41467-023-42504-y.
6
Quantification of subtype purity in Luminal A breast cancer predicts clinical characteristics and survival.Luminal A 型乳腺癌亚分型纯度的定量分析可预测临床特征和生存情况。
Breast Cancer Res Treat. 2023 Jul;200(2):225-235. doi: 10.1007/s10549-023-06961-9. Epub 2023 May 20.
7
Biological insights and novel biomarker discovery through deep learning approaches in breast cancer histopathology.通过深度学习方法在乳腺癌组织病理学中获得生物学见解和发现新型生物标志物
NPJ Breast Cancer. 2023 Apr 6;9(1):21. doi: 10.1038/s41523-023-00518-1.
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Spatial Transcriptomics for Tumor Heterogeneity Analysis.用于肿瘤异质性分析的空间转录组学
Front Genet. 2022 Jul 5;13:906158. doi: 10.3389/fgene.2022.906158. eCollection 2022.
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Trastuzumab Deruxtecan in Previously Treated HER2-Low Advanced Breast Cancer.曲妥珠单抗-德曲妥珠单抗用于既往治疗的 HER2 低表达晚期乳腺癌。
N Engl J Med. 2022 Jul 7;387(1):9-20. doi: 10.1056/NEJMoa2203690. Epub 2022 Jun 5.
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Molecular analysis of TCGA breast cancer histologic types.癌症基因组图谱（TCGA）乳腺癌组织学类型的分子分析。
Cell Genom. 2021 Dec 8;1(3). doi: 10.1016/j.xgen.2021.100067.