Evidence map›Paper›PMID 40856959›Full record

ArticleInternational journal of clinical pharmacy2025

Development of a machine learning-based risk prediction model and analysis of risk factors for docetaxel-induced bone marrow suppression in breast cancer patients.

Hongya Ou, Zijan Tan, Anle Shen, Liting Yu, Yibo He, Ri Hong, Xiaoru Lin, Xiaofeng Shi, Konglang Xing, Xiaoli Song and 3 more

Abstract read
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In one paragraph

Article in International journal of clinical pharmacy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

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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.

Hongya OuHainan Branch, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Sanya, China.
Zijan TanHainan Branch, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Sanya, China.
Anle ShenHainan Branch, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Sanya, China.
Liting YuDepartment of Pharmacy, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Yibo HeDepartment of Clinical Lab, Zhejiang Provincial Hospital of Chinese Medicine, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, Zhejiang Province, China.
Ri HongHainan Branch, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Sanya, China.
Xiaoru LinHainan Branch, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Sanya, China.
Xiaofeng ShiHainan Branch, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Sanya, China.
Konglang XingDepartment of Pharmacy, Sanya People's Hospital, Sanya, Hainan, China.
Xiaoli SongDepartment of Pharmacy, Sanya Central Hospital (The Third People's Hospital of Hainan Province), Sanya, China.
Yonglin LiuReproductive Center, The Second Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, Zhejiang Province, China.
Lingli ZouThird Rehabilitation Zone, Hangzhou Rehabilitation Center for Special Duty Servicemen of the PLA Air Force, Hangzhou, China.
Junyu LiHainan Branch, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Sanya, China. GENE_LEE@hainmc.edu.cn.

Funding

Hainan Provincial Natural Science Foundation of China 823QN371Pharmacovigilance Innovation Research Project of the Hainan Pharmacovigilance Center HNPV202404the JINYE ZHONGZI Project from SCMC-HN JYZZ-YJ-05the Joint Program on Health Science & Technology Innovation of Hainan Province WSJK2024QN001the Joint Program on Health Science & Technology Innovation of Hainan Province WSJK2024QN119the Joint Program on Health Science & Technology Innovation of Hainan Province WSJK2025QN005the Special Science and Technology Plan Project of Universities and Medical Institutions in Sanya City 2021GXYL32
6 · The paper itself

Abstract

introductionBreast cancer is the most prevalent malignancy in women worldwide. Docetaxel-based chemotherapy is commonly used for treatment, but its clinical application is often constrained by hematologic toxicity, particularly severe bone marrow suppression. The early identification of high-risk patients is essential to prevent complications and optimize therapeutic outcomes. Machine learning offers advanced capabilities for risk prediction by capturing complex patterns beyond those of traditional statistical models.

aimThis study aimed to identify the risk factors associated with bone marrow suppression in breast cancer patients receiving docetaxel-based chemotherapy, and to develop and validate predictive models using machine learning algorithms.

methodThis retrospective case-control study included 119 patients with breast cancer treated with docetaxel-based chemotherapy at the Hainan Hospital of Shanghai Children's Medical Center, Shanghai Jiao Tong University School of Medicine, from January 2020 to December 2024. Patients were divided into bone marrow suppression (n = 57; WHO grades II-IV) and non-suppression (n = 62; grades 0-I) groups based on WHO toxicity criteria. Multivariate logistic regression was used to identify independent risk factors. Three prediction models, logistic regression, random forest, and AdaBoost, were constructed and evaluated. A five-fold cross-validation with 50 repetitions was performed to ensure model stability and reliability.

resultsMultivariate analysis revealed that a low baseline lymphocyte count (OR = 4.95, 95% CI 1.62-17.0), decreased white blood cell (WBC) count (OR = 0.62, 95% CI 0.40-0.9), and reduced prealbumin level (OR = 0.98, 95% CI 0.97-0.99) were significantly associated with severe bone marrow suppression (all p < 0.05). Among the models, AdaBoost achieved the best overall performance (AUC = 0.81; specificity = 94%; accuracy = 77%). The random forest model showed the highest sensitivity (83%), while logistic regression was more interpretable but demonstrated a lower predictive ability (AUC = 0.69).

conclusionPretreatment lymphocyte count, WBC count, and prealbumin level are reliable predictors of docetaxel-induced bone marrow suppression. The AdaBoost model demonstrates high specificity (94%) in identifying low-risk patients, supporting accurate risk stratification and personalized care in breast cancer treatment.

Indexed as

Antineoplastic AgentsBreast NeoplasmsDocetaxelMachine LearningAdultAgedCase-Control StudiesFemaleHumansMiddle AgedRetrospective StudiesRisk AssessmentRisk FactorsAntineoplastic AgentsDocetaxelBone marrow suppressionBreast neoplasmsChemotherapy-induced adverse effectsDocetaxelMachine learningPredictive modelsRisk factors

Identifiers

PMID40856959

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Registered trials

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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.