Evidence map›Paper›PMID 41776447›Full record

ArticleBMC cancer2026

Circulating tumor cells (CTCs) enumeration and machine-learning based diagnostic biomarkers for breast cancer detection.

Chun-Yu Liu, Yu-Hsiang Lin, Yi-Fang Tsai, Po-Yen Lu, Ji-Lin Chen, Yu-Hsuan Li, Chi-Cheng Huang, Yen-Shu Lin, Ta-Chung Chao, Chin-Jung Feng and 4 more

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Article in BMC cancer, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

14 authors.

Chun-Yu LiuSchool of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei, 11217, Taiwan.
Yu-Hsiang LinSchool of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei, 11217, Taiwan.
Yi-Fang TsaiSchool of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei, 11217, Taiwan.
Po-Yen LuInstitute of Public Health, College of Medicine, National Yang Ming Chiao Tung University, No. 155, Sec. 2, Linong St., Beitou Dist, Taipei City, 112, Taiwan.
Ji-Lin ChenSchool of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei, 11217, Taiwan.
Yu-Hsuan LiInstitute of Public Health, College of Medicine, National Yang Ming Chiao Tung University, No. 155, Sec. 2, Linong St., Beitou Dist, Taipei City, 112, Taiwan.
Chi-Cheng HuangComprehensive Breast Health Center, Department of Surgery, Taipei Veterans General Hospital, No. 201, Section 2, Shipai Road, Taipei, 11217, Taiwan.
Yen-Shu LinSchool of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei, 11217, Taiwan.
Ta-Chung ChaoSchool of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei, 11217, Taiwan.
Chin-Jung FengSchool of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei, 11217, Taiwan.
Chih-Yi HsuSchool of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei, 11217, Taiwan.
Jen-Hwey ChiuComprehensive Breast Health Center, Department of Surgery, Taipei Veterans General Hospital, No. 201, Section 2, Shipai Road, Taipei, 11217, Taiwan.
Chyong-Mei ChenInstitute of Public Health, College of Medicine, National Yang Ming Chiao Tung University, No. 155, Sec. 2, Linong St., Beitou Dist, Taipei City, 112, Taiwan. cmchen2@nycu.edu.tw.
Ling-Ming TsengSchool of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei, 11217, Taiwan. lmtseng@vghtpe.gov.tw.

Funding

Dr. Morris Chang ABMRD002National Science and Technology Council NSTC 112-2314-B-A49-083-MY3Taipei Veterans General Hospital V114C-013Taipei Veterans General Hospital-National Taiwan University Hospital Joint Research Program VN111-06Teh-Tzer Study Group for Human Medical Research Foundation of Taiwan B1131020Yong-Lin Healthcare Foundation SINO-CANCER project
6 · The paper itself

Abstract

backgroundCirculating tumor cells (CTCs) are detectable in early-stage cancer and may enable early cancer detection. We evaluated a CTC-based assay as a complementary biomarker for breast cancer detection in an Asian population with a high prevalence of dense breast tissue.

methodsIn this single-center, prospective, blinded study, peripheral blood from Taiwanese women with breast cancer and healthy controls was analyzed using a CTC-enumeration platform (CMx) based on biomarker expression (cytokeratin 18 [CK18], mammaglobin [MGB], CD45), cell morphometry, and nuclear features. A machine-learning model integrating CTC biomarkers with age, white blood cell (WBC) count, and platelet count was developed to assess classification performance, providing proof-of-concept for combining CTC-derived and routine blood parameters in breast cancer risk assessment.

resultsA total of 228 breast cancer patients and 170 healthy controls were included. Age and CK18- and MGB-positive CTC counts differed significantly between groups, whereas WBC and platelet counts did not. An ensemble linear support vector machines model incorporating age and CTC features achieved an area under the curve of 0.85 (95% CI, 0.73-0.96) in the independent test cohort, with high sensitivity (0.93), positive predictive value (0.74), and negative predictive value (0.86), but modest specificity (0.57). In the exploratory BI-RADS 3/4 subgroup, the model identified all cancer cases (sensitivity 1.00), with a specificity of 0.44 and overall accuracy of 0.79.

conclusionsThis study demonstrates the feasibility of combining CTC enumeration with machine learning for breast cancer detection and supports the need for future large-scale, multicenter, multiethnic prospective external validation.

Indexed as

Biomarkers, TumorBreast NeoplasmsMachine LearningNeoplastic Cells, CirculatingAdultAgedCase-Control StudiesFemaleHumansKeratin-18Mammaglobin AMiddle AgedProspective StudiesBiomarkers, TumorKeratin-18Mammaglobin ABreast cancerCirculating tumor cellsEnsemble classifierMachine-learning algorithmsMonte carlo cross-validation

Identifiers

PMID41776447
PMCPMC13063714

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