SynthesisPloS one2014
Predicting survival of de novo metastatic breast cancer in Asian women: systematic review and validation study.
Synthesis in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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
17 citing papers in PubMed.
- Ribociclib plus letrozole versus letrozole alone in patients with de novo HR+, HER2- advanced breast cancer in the randomized MONALEESA-2 trial.Breast cancer research and treatment · 2018Trial
- Deep learning-based non-invasive prediction of axillary lymph node metastasis in breast cancer: performance of the YOLO-v11 object detection algorithm.BMC medical imaging · 2026Article
- Use of Pharmacokinetic and Pharmacodynamic Data to Develop the CDK4/6 Inhibitor Ribociclib for Patients with Advanced Breast Cancer.Clinical pharmacokinetics · 2024Review
- Expression and Significance of LINC02418 in Breast Cancer.Breast cancer (Dove Medical Press) · 2024Article
- Comparison between de novo and metachronous metastatic breast cancer: the presence of a primary tumour is not the only difference-a Dutch population-based study from 2008 to 2018.Breast cancer research and treatment · 2023Article
- Prognostic Model for De Novo and Recurrent Metastatic Breast Cancer.JCO clinical cancer informatics · 2021Article
- Development and validation of a nomogram in survival prediction among advanced breast cancer patients.Annals of translational medicine · 2020Article
- Comparison of the clinicopathological characteristics and prognosis between Chinese patients with breast cancer with bone-only and non-bone-only metastasis.Oncology letters · 2020Article
- Early Death Incidence and Prediction in Stage IV Breast Cancer.Medical science monitor : international medical journal of experimental and clinical research · 2020Article
- Complete Clinical Response in Locally Advanced Metastatic de novo Breast Cancer after Front-Line Treatment with Ribociclib/Letrozole within the RIBANNA Study.Breast care (Basel, Switzerland) · 2020Article
- Prognostic Value of Site-Specific Metastases and Surgery in De Novo Stage IV Triple-Negative Breast Cancer: A Population-Based Analysis.Medical science monitor : international medical journal of experimental and clinical research · 2020Article
- Predicting factors for survival of breast cancer patients using machine learning techniques.BMC medical informatics and decision making · 2019Article
- Score for the Survival Probability in Metastasis Breast Cancer: A Nomogram-Based Risk Assessment Model.Cancer research and treatment · 2018Article
- Overexpression of TMPRSS4 promotes tumor proliferation and aggressiveness in breast cancer.International journal of molecular medicine · 2017Article
- A Comparative Performance Analysis of Multispectral and RGB Imaging on HER2 Status Evaluation for the Prediction of Breast Cancer Prognosis.Translational oncology · 2016Article
- Integrative analysis of mutational and transcriptional profiles reveals driver mutations of metastatic breast cancers.Cell discovery · 2016Article
- Association of CYP2D6*10, OATP1B1 A388G, and OATP1B1 T521C polymorphisms and overall survival of breast cancer patients after tamoxifen therapy.Medical science monitor : international medical journal of experimental and clinical research · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIn Asia, up to 25% of breast cancer patients present with distant metastases at diagnosis. Given the heterogeneous survival probabilities of de novo metastatic breast cancer, individual outcome prediction is challenging. The aim of the study is to identify existing prognostic models for patients with de novo metastatic breast cancer and validate them in Asia. MATERIALS AND
methodsWe performed a systematic review to identify prediction models for metastatic breast cancer. Models were validated in 642 women with de novo metastatic breast cancer registered between 2000 and 2010 in the Singapore Malaysia Hospital Based Breast Cancer Registry. Survival curves for low, intermediate and high-risk groups according to each prognostic score were compared by log-rank test and discrimination of the models was assessed by concordance statistic (C-statistic).
resultsWe identified 16 prediction models, seven of which were for patients with brain metastases only. Performance status, estrogen receptor status, metastatic site(s) and disease-free interval were the most common predictors. We were able to validate nine prediction models. The capacity of the models to discriminate between poor and good survivors varied from poor to fair with C-statistics ranging from 0.50 (95% CI, 0.48-0.53) to 0.63 (95% CI, 0.60-0.66).
conclusionThe discriminatory performance of existing prediction models for de novo metastatic breast cancer in Asia is modest. Development of an Asian-specific prediction model is needed to improve prognostication and guide decision making.
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.