Evidence map›Paper›PMID 42639147›Full record

ArticleResearch (Washington, D.C.)2026

MRI-Based Deep Learning Guides Multi-Omics Discovery of NBPF4 as a Therapeutic Target for Breast Cancer Lymph Node Metastasis.

Dianqi Cai, Haoxuan Huang, Zijun Chen, Chao Zhu, Zehui Li, Ming Chen, Zhiyi Guo, Jie Liu, Gengxi Cai, Wenjun Mao and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Research (Washington, D.C.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Dianqi CaiGuangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, China.
Haoxuan HuangDepartment of Urology, The Third Affiliated Hospital (the First Hosptial of Nanchang), Jiangxi Medical College, Nanchang University, Nanchang 330006, Jiangxi, China.
Zijun ChenGuangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, China.
Chao ZhuDepartment of Clinical Laboratory, The First Affiliated Hospital of Nanchang University, Nanchang, China.
Zehui LiGuangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, China.
Ming ChenGuangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, China.
Zhiyi GuoGuangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, China.
Jie LiuDepartment of Breast Cancer, Foshan Women and Children Hospital Affiliated to Guangdong Medical University, Foshan, China.
Gengxi CaiThe First People's Hospital of Foshan (Foshan Hospital Affiliated to Southern University of Science and Technology), School of Medicine, Southern University of Science and Technology, Guangdong, China.
Wenjun MaoDepartment of Thoracic Surgery, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi People's Hospital, Wuxi Medical Center, Nanjing Medical University, Wuxi, China.
Jianguo LaiGuangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, China.ORCID https://orcid.org/0000-0002-0120-0245

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Deep learning models are increasingly used to analyze medical images, but their "black box" nature makes it hard to understand the underlying biology and slows down the development of targeted treatments. To tackle this, we built a multi-step approach that combines deep learning analysis of breast magnetic resonance imaging (MRI) with several types of molecular data, including gene activity, protein levels, and genetic information, along with laboratory experiments. Our MRI-based deep learning model accurately predicted whether breast cancer had spread to lymph nodes, and it performed consistently across 3 separate groups of patients. Causal inference using double least absolute shrinkage and selection operator (LASSO) and causal forest double machine learning established a significant effect of

Identifiers

PMID42639147
PMCPMC13500915

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.