Evidence map›Paper›PMID 40634485›Full record

ArticleNPJ digital medicine2025

Transformer optimization with meta learning on pathology images for breast cancer lymph node micrometastasis.

Jing Huang, Jingtao Wang, Junhai Shi, Hengli Ni, Shan Xu, Ping Wu, Yuexiang Ren, Lijuan Bian, Chenhan Su, Yuxuan Xu and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–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

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Article
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

13 authors.

Jing HuangDepartment of Pathology, Soochow Medical College, Soochow University, Suzhou, China.
Jingtao WangSchool of Electronics and Information Engineering, Soochow University, Suzhou, China.
Junhai ShiDepartment of Pathology, Soochow Medical College, Soochow University, Suzhou, China.
Hengli NiDepartment of Pathology, Children's Hospital, Soochow University, Suzhou, China.
Shan XuDepartment of Pathology and Institute of Molecular Pathology, Jiangxi Provincial Key Laboratory for Precision Pathology and Intelligent Diagnosis, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China.
Ping WuDepartment of Pathology and Institute of Molecular Pathology, Jiangxi Provincial Key Laboratory for Precision Pathology and Intelligent Diagnosis, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China.
Yuexiang RenDepartment of Pathology and Institute of Molecular Pathology, Jiangxi Provincial Key Laboratory for Precision Pathology and Intelligent Diagnosis, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China.
Lijuan BianDepartment of Pathology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.
Chenhan SuDepartment of Pathology, Soochow Medical College, Soochow University, Suzhou, China.
Yuxuan XuDepartment of Pathology, Soochow Medical College, Soochow University, Suzhou, China.
Xinyu HeDepartment of Pathology, Soochow Medical College, Soochow University, Suzhou, China.
Xinjian ChenSchool of Electronics and Information Engineering, Soochow University, Suzhou, China. xjchen@suda.edu.cn.
Jianming LiDepartment of Pathology, Soochow Medical College, Soochow University, Suzhou, China. lijming3@sysu.edu.cn.

Funding

Key Program of the National Natural Science Foundation of China 82130080National Key Research and Development Program of China 2018YFA0701700National Natural Science Foundation of China 82203273National Natural Science Foundation of China 82303584National Natural Science Foundation of China U20A20170Natural Science Foundation of Jiangsu Province BK20220495Natural Science Foundation of the Jiangsu Higher Education Institutions of China 21KJB320018the Key Support Project of the National Natural Science Foundation of China Regional Innovation and Development Joint Fund U24A201290the Major Project of the Jiangxi Provincial Natural Science Foundation 20242BAB27004
6 · The paper itself

Abstract

Lymph node micro-metastasis represents the initial stage of breast cancer spread or metastasis. However, the limited size of these hidden lesions restricts dataset expansion, presenting a significant challenge for manual examination and conventional deep learning techniques. By harnessing the power of meta-learning on limited datasets, we developed a novel network named MetaTrans, equipped with a 34-category dataset (MT-MCD) to effectively pinpoint micro-metastases in lymph nodes from pathological images. MetaTrans demonstrated superior performance on two different multi-center datasets and excelled in the 0-shot task for intraoperative frozen section diagnosis. Beyond breast cancer, MetaTrans efficiently identifies micro-metastases in thyroid and colorectal cancers and can be directly applied to recognize images captured by digital cameras under a microscope. Across all clinical validation scenarios, our method surpasses state-of-the-art baselines, exhibiting robust cross-domain adaptation and task-specific reliability, which highlight its translational potential in diverse pathological settings.

Identifiers

PMID40634485
PMCPMC12241324

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

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.