ArticleAnnals of translational medicine2021
Combined everolimus and endocrine therapy in advanced HR-positive, HER2-negative Chinese breast cancer patients: a retrospective study.
Article in Annals of translational medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it, 7 citations in OpenAlex.
- Systematic review of the management of brain metastases from hormone receptor positive breast cancer.Journal of neuro-oncology · 2023Pooled it
- Capivasertib plus fulvestrant in patients with HR-positive/HER2-negative advanced breast cancer: phase 3 CAPItello-291 study extended Chinese cohort.Nature communications · 2025Trial
- Potential role of immunotherapy and targeted therapy in the treatment of cancer: A contemporary nursing practice.Heliyon · 2024Review
- Everolimus treatment in patients with hormone receptor-positive and human epidermal growth factor receptor 2-negative advanced breast cancer and a predictive model for its efficacy: a multicenter real-world study.Therapeutic advances in medical oncology · 2024Article
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundEverolimus (EVE) is an inhibitor of the mammalian target of rapamycin (mTOR) pathway, and it is approved for the treatment of advanced breast cancer (ABC). However, there is still little real-world data on using EVE in Chinese breast cancer patients. We retrospectively analyzed real-world data to determine the factors affecting EVE treatment efficacy and patient outcomes.
methodsWe retrospectively collected the treatment information of ABC patients treated with EVE from 2013 to 2020 in Zhejiang Cancer Hospital. Kaplan-Meier analysis and Cox regression methods were used to calculate and compare the progression-free survival (PFS), and identify the factors associated with EVE treatment efficacy.
resultsThe study finally enrolled 84 patients meeting the requirement; the median PFS in all 84 patients was 6.87 months. Multivariate analysis showed that liver metastasis [hazard ratio, 1.69; 95% confidence interval (CI), 1.00-2.84; P=0.049], and brain metastasis (hazard ratio, 2.65; 95% CI, 1.07-6.58; P=0.036) were independent risk factors. Subgroup analyses demonstrated EVE + fulvestrant (FUL) was not superior to EVE + aromatase inhibitors (AIs) for PFS (5.77
conclusionsEVE combined with endocrine therapy is an effective treatment option for Chinese patients with hormone-receptor-positive (HR+), human epidermal growth factor receptor-2-negative (HER2-) breast cancer, although EVE + FUL was not superior to EVE + AI. Liver metastasis and brain metastasis were independent risk factors for successful EVE + endocrine therapy.
Indexed as
Identifiers
What OpenQuestion holds
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.