ReviewBreast cancer research and treatment2021
A clinical calculator to predict disease outcomes in women with triple-negative breast cancer.
Review in Breast cancer research and treatment, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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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.
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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.
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Who cites it
25 citing papers in PubMed, 37 citations in OpenAlex.
- Blood Cell-Camouflaged Liquid Metal Nanoconjugates Orchestrate Treg Depletion and STING-Amplified Photothermal Immunity for Metastatic Triple-Negative Breast Cancer Therapy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Konjac petroleum ether extract inhibits triple-negative breast cancer cell migration and invasion by attenuatingFrontiers in pharmacology · 2026Article
- Development of predictive models for the prognosis of triple-negative breast cancer using multiple transcriptomic analyses.PloS one · 2026Article
- CTPS1 is an unexplored vulnerability in breast and ovarian cancer.Theranostics · 2026Article
- Localized Hydrogel-Mediated Docetaxel-Carboplatin Combination Chemotherapy Targets Ganglioside Metabolism to Mitigate Tumor Progression.ACS pharmacology & translational science · 2025Article
- Circulating chemokines and leukocytes and their impact on breast cancer prognosis.Discover oncology · 2025Article
- Predicting prognosis of patients with triple‑negative breast cancer undergoing neoadjuvant chemotherapy based on inflammatory status at different time points: A propensity score matching analysis.Oncology letters · 2025Article
- Article
- Network Pharmacology andCurrent pharmaceutical design · 2025Article
- Treatment Outcomes after Postoperative Radiotherapy in Triple-Negative Breast Cancer: Multi-Institutional Retrospective Study (KROG 17-05).Journal of personalized medicine · 2024Article
- P4HA1: an important target for treating fibrosis related diseases and cancer.Frontiers in pharmacology · 2024Review
- Phytochemical characterization by GC-MS andBiotechnologia · 2024Article
- Radiologic imaging biomarkers in triple-negative breast cancer: a literature review about the role of artificial intelligence and the way forward.BJR artificial intelligence · 2024Review
- Neutrophils in triple-negative breast cancer: an underestimated player with increasingly recognized importance.Breast cancer research : BCR · 2023Review
- Triple Negative Breast Cancer Treatment Options and Limitations: Future Outlook.Pharmaceutics · 2023Review
- Artificial intelligence: opportunities and challenges in the clinical applications of triple-negative breast cancer.British journal of cancer · 2023Review
- Construct ceRNA Network and Risk Model of Breast Cancer Using Machine Learning Methods under the Mechanism of Cuproptosis.Diagnostics (Basel, Switzerland) · 2023Article
- A novel conditional survival nomogram for monitoring real-time prognosis of non-metastatic triple-negative breast cancer.Frontiers in endocrinology · 2023Article
- Predictive Significance of Systemic Immune-Inflammation Index in Patients with Breast Cancer: A Retrospective Cohort Study.OncoTargets and therapy · 2023Article
- Survival prediction in triple negative breast cancer using multiple instance learning of histopathological images.Scientific reports · 2022Article
Corrections and comments
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Authors and funding
20 authors at 4 institutions in 2 countries.
Funding
Abstract
purposeTriple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer, characterized by substantial risks of early disease recurrence and mortality. We constructed and validated clinical calculators for predicting recurrence-free survival (RFS) and overall survival (OS) for TNBC.
methodsData from 605 women with centrally confirmed TNBC who underwent primary breast cancer surgery at Mayo Clinic during 1985-2012 were used to train risk models. Variables included age, menopausal status, tumor size, nodal status, Nottingham grade, surgery type, adjuvant radiation therapy, adjuvant chemotherapy, Ki67, stromal tumor-infiltrating lymphocytes (sTIL) score, and neutrophil-to-lymphocyte ratio (NLR). Final models were internally validated for calibration and discrimination using ten-fold cross-validation and compared with their base-model counterparts which include only tumor size and nodal status. Independent external validation was performed using data from 478 patients diagnosed with stage II/III invasive TNBC during 1986-1992 in the British Columbia Breast Cancer Outcomes Unit database.
resultsFinal RFS and OS models were well calibrated and associated with C-indices of 0.72 and 0.73, as compared with 0.64 and 0.62 of the base models (p < 0.001). In external validation, the discriminant ability of the final models was comparable to the base models (C-index: 0.59-0.61). The RFS model demonstrated greater accuracy than the base model both overall and within patient subgroups, but the advantages of the OS model were less profound.
conclusionsThis TNBC clinical calculator can be used to predict patient outcomes and may aid physician's communication with TNBC patients regarding their long-term disease outlook and planning treatment strategies.
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Registered trials
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.