Evidence map›Paper›PMID 40993750›Full record

ArticleJournal of translational medicine2025

A dual-branch deep learning network for circulating tumor cells classification.

Chao Han, Jiaquan Lin, Yanfang Liang, Cong Li, Danni Wang, Gonghua Huang, Ruoxi Hong, Jincheng Zeng

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of translational medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. Review
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

8 authors.

Chao HanDongguan Key Laboratory of Medical Bioactive Molecular Developmental and Translational Research, Guangdong Provincial Key Laboratory of Medical Immunology and Molecular Diagnostics, Guangdong Medical University, Dongguan, 523808, China.
Jiaquan LinAnfang Biotechnology Co., Ltd., Guangzhou, 510000, China.
Yanfang LiangDongguan Key Laboratory of Molecular Immunopathology, Department of Pathology, Binhaiwan Central Hospital of Dongguan, Dongguan, 523000, China.
Cong LiDongguan Key Laboratory of Metabolic Immunology and Oral Diseases, Dongguan Maternal and Child Health Care Hospital, Dongguan, 523800, China.
Danni WangSun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China.
Gonghua HuangGuangdong Provincial Key Laboratory of Medical Immunology and Molecular Diagnostics, The Affiliated Dongguan Songshan Lake Central Hospital, Guangdong Medical University, Dongguan, 523808, China. gonghua.huang@gdmu.edu.cn.
Ruoxi HongState Key Laboratory of Oncology in South China, Department of Medical Oncology, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China. hongrx@sysucc.org.cn.
Jincheng ZengDongguan Key Laboratory of Medical Bioactive Molecular Developmental and Translational Research, Guangdong Provincial Key Laboratory of Medical Immunology and Molecular Diagnostics, Guangdong Medical University, Dongguan, 523808, China. zengjc@gdmu.edu.cn.ORCID 0000-0001-6196-3917

Funding

Basic and Applied Basic Research Foundation of Guangdong Province 2021B1515140066Dongguan Social Science and Technology Development Project 20211800904532
6 · The paper itself

Abstract

backgroundCirculating tumor cells (CTCs) in peripheral blood are crucial for prognosis, treatment response, disease monitoring, and personalized therapy. However, identifying CTCs remains challenging due to their scarcity and heterogeneity, even with advanced deep learning models.

methodsThis study introduces an innovative hybrid framework combining a dual-branch network with traditional image processing techniques and automated CTC identification. By incorporating image and fluorescence attributes, the framework enhances feature representation robustness. Performance was evaluated using accuracy, precision, and recall metrics and comparisons with pathologists' manual counting.

resultsThe framework achieved 97.05% accuracy in distinguishing CTCs from non-CTCs, with performance closely matching pathologists' manual counting in survival prediction. The dual-branch network improved efficiency by leveraging segmentation algorithms, surpassing conventional methods. Clinical trials confirmed its practicality for direct clinical use.

conclusionsThe proposed framework enhances CTC identification accuracy and efficiency, demonstrating strong clinical applicability. Its output results can be directly utilized for prognosis without manual intervention, offering significant potential for personalized therapy.

Indexed as

Deep LearningNeoplastic Cells, CirculatingAlgorithmsHumansImage Processing, Computer-AssistedCirculating tumor cellsDeep learningFeature fusionFluorescent image

Identifiers

PMID40993750
PMCPMC12462185

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.