ArticleScientific reports2017
Prognostic value of routine laboratory variables in prediction of breast cancer recurrence.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Elevated Cardiac Troponin to Detect Acute Cellular Rejection After Cardiac Transplantation: A Systematic Review and Meta-Analysis.Transplant international : official journal of the European Society for Organ Transplantation · 2022Pooled it
- Baseline serum albumin level as a predictive factor for the efficacy of trifluridine/tipiracil plus bevacizumab in metastatic colorectal cancer: a retrospective cohort study.Journal of pharmaceutical health care and sciences · 2025Article
- Multimodal BEHRT: transformers for multimodal electronic health records to predict breast cancer prognosis.Frontiers in oncology · 2025Article
- The Importance of Hypoxia-Related to Hemoglobin Concentration in Breast Cancer.Cell biochemistry and biophysics · 2024Review
- Comparing Clinician Estimates versus a Statistical Tool for Predicting Risk of Death within 45 Days of Admission for Cancer Patients.Applied clinical informatics · 2024Article
- Development and validation of a deep learning-based model to predict response and survival of T790M mutant non-small cell lung cancer patients in early clinical phase trials using electronic medical record and pharmacokinetic data.Translational lung cancer research · 2024Article
- Machine learning approaches for predicting 5-year breast cancer survival: A multicenter study.Cancer science · 2023Article
- Proposing a novel molecular subtyping scheme for predicting distant recurrence-free survival in breast cancer post-neoadjuvant chemotherapy with close correlation to metabolism and senescence.Frontiers in endocrinology · 2023Review
- Donor Cardiac Troponin for Prognosis of Adverse Outcomes in Cardiac Transplantation Recipients: a Systematic Review and Meta-analysis.Transplantation direct · 2022Review
- Computer-assisted quantification of tumor-associated collagen signatures to improve the prognosis prediction of breast cancer.BMC medicine · 2021Article
- Deep Learning-Based Prediction Model for Breast Cancer Recurrence Using Adjuvant Breast Cancer Cohort in Tertiary Cancer Center Registry.Frontiers in oncology · 2021Article
- Large-scale tumor-associated collagen signatures identify high-risk breast cancer patients.Theranostics · 2021Article
- A Preliminary Cohort Study Assessing Routine Blood Analyte Levels and Neurological Outcome after Spinal Cord Injury.Journal of neurotrauma · 2020Article
- Albumin/globulin ratio is negatively correlated with PD-1 and CD25 mRNA levels in breast cancer patients.OncoTargets and therapy · 2018Article
- De Ritis ratio in elderly glioblastoma patients treated with chemoradiation: A comprehensive analysis of serum biomarkers.Neuro-oncology advancesArticle
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The prognostic value of routine laboratory variables in breast cancer has been largely overlooked. Based on laboratory tests commonly performed in clinical practice, we aimed to develop a new model to predict disease free survival (DFS) after surgical removal of primary breast cancer. In a cohort of 1,596 breast cancer patients, we analyzed the associations of 33 laboratory variables with patient DFS. Based on 3 significant laboratory variables (hemoglobin, alkaline phosphatase, and international normalized ratio), together with important demographic and clinical variables, we developed a prognostic model, achieving the area under the curve of 0.79. We categorized patients into 3 risk groups according to the prognostic index developed from the final model. Compared with the patients in the low-risk group, those in the medium- and high-risk group had a significantly increased risk of recurrence with a hazard ratio (HR) of 1.75 (95% confidence interval [CI] 1.30-2.38) and 4.66 (95% CI 3.54-6.14), respectively. The results from the training set were validated in the testing set. Overall, our prognostic model incorporating readily available routine laboratory tests is powerful in identifying breast cancer patients who are at high risk of recurrence. Further study is warranted to validate its clinical application.
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