Integrating Serum Lipid Biomarkers Into Machine Learning for the Differential Diagnosis of Breast Nodules.
Objective: To develop and validate an interpretable machine learning (ML) model for predicting malignant risk in patients with breast nodules using serum lipid biomarkers. Methods: This retrospective study included 899 patients with breast nodules (236 malignant) admitted between March 2022 and Dece...
| Publicado en: | Breast Journal Vol. 2026; pp. 1 - 13 |
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| Autores principales: | , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
8/21/2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=196384953&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 196384953 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 1075122X ET6 jtl: Breast Journal issn: 1075122X maglogo: Y pubinfo: dt: 8/21/2026 vid: 2026 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 196384953 196384953 196384953 10.1155/tbj/2642494 196384953 ppf: 1 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: C – @attributes: type: P tig: atl: Integrating Serum Lipid Biomarkers Into Machine Learning for the Differential Diagnosis of Breast Nodules. aug: au: Chen, Longmei Du, Yuzhen Liu, Wanchao Dattachoudhury, Sreeja affil: Department of Laboratory Medicine,, Shanghai Baoshan Hospital of Integrated Traditional Chinese and Western Medicine,, Baoshan Hospital,, Shanghai University of Traditional Chinese Medicine,, Shanghai 201999,, China, shutcm.edu.cn sug: subj: Lipids Blood Tumor Markers, Biological Blood Diagnosis, Differential Breast Neoplasms Diagnosis Prediction Models Evaluation Breast Neoplasms Risk Factors Risk Assessment Machine Learning Human Retrospective Design Record Review Calibration ROC Curve Random Forest Age Factors Decision Support Systems, Clinical China Antigens, Tumor Blood Ferritin Blood Machine Learning Algorithms Female Descriptive Statistics Funding Source Female ab: Objective: To develop and validate an interpretable machine learning (ML) model for predicting malignant risk in patients with breast nodules using serum lipid biomarkers. Methods: This retrospective study included 899 patients with breast nodules (236 malignant) admitted between March 2022 and December 2024. Patients were randomly assigned to a training cohort (n = 630) and an internal validation cohort (n = 269) at a 7:3 ratio. Baseline clinical and laboratory data were collected upon admission. Following feature selection via LASSO regression, the predictive performance of 8 ML algorithms was evaluated and compared using receiver operating characteristic (ROC) curves. The optimal model's performance was further corroborated using an independent temporal validation cohort of 190 patients (admitted Jan–Aug 2025). Model interpretability was addressed using SHapley Additive exPlanations (SHAP). Results: Nine key predictors were identified from 20 candidates by Lasso regression and clinical expertise. The random forest (RF) model outperformed other algorithms, achieving areas under the curve (AUC) values of 0.789, 0.782, and 0.825 for the training, internal validation, and temporal validation cohorts, respectively. Hosmer–Lemeshow tests (p > 0.05) indicated high calibration between the predicted and observed risks. SHAP importance analysis revealed Fer, age, and CEA to be the top three predictive factors. Conclusion: The RF model based on serum lipid biomarkers serves as a robust, noninvasive tool for assessing breast cancer risk, showing significant potential for clinical decision support in screening programs. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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