SynthesisJournal of the National Cancer Institute2020
A Systematic Review of Estimating Breast Cancer Recurrence at the Population Level With Administrative Data.
Synthesis in Journal of the National Cancer Institute, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses 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
19 citing papers in PubMed, 2 syntheses or guidelines pooled it, 40 citations in OpenAlex.
- Extracting cancer concepts from clinical notes using natural language processing: a systematic review.BMC bioinformatics · 2023Pooled it
- Predicting breast cancer 5-year survival using machine learning: A systematic review.PloS one · 2021Pooled it
- External validation of a data-driven algorithm to identify breast cancer recurrences from administrative healthcare data.Breast (Edinburgh, Scotland) · 2026Article
- Fully Automated Abstraction of Longitudinal Breast Oncology Records with Off-The-Shelf Large Language Models.medRxiv : the preprint server for health sciences · 2026Article
- Impact of Delaying Disease Recurrence on Economic Burden in Patients with HER2+ Early-Stage Breast Cancer (eBC).ClinicoEconomics and outcomes research : CEOR · 2026Article
- Integrating a Shareable Artificial Intelligence Model Into Clinical Research for Cancer Recurrence in Patients With Breast and Colorectal Cancer.JCO clinical cancer informatics · 2025Article
- Development of a Machine Learning Model for Predicting Treatment-Related Amenorrhea in Young Women with Breast Cancer.Bioengineering (Basel, Switzerland) · 2025Article
- Metastatic Breast Cancer Prevalence in New South Wales, Australia, in 2016: A Health Record Linkage Study.Asia-Pacific journal of clinical oncology · 2025Article
- Prognostic model for predicting recurrence in breast cancer patients in Saudi Arabia.Scientific reports · 2025Article
- Misclassification of second primary and recurrent breast cancer in the surveillance epidemiology and end results registry.Cancer causes & control : CCC · 2025Article
- Retrospective Case-Cohort Study on Risk Factors for Developing Distant Metastases in Women With Breast Cancer.Cancer medicine · 2025Article
- Estimated Incidence and Prevalence of Metastatic Breast Cancer in Northern Ireland, 2009 to 2020.JAMA network open · 2025Article
- Evaluation of machine learning methods for the retrospective detection of ovarian cancer recurrences from chemotherapy data.ESMO real world data and digital oncology · 2024Article
- Evaluation of Algorithms Using Automated Health Plan Data to Identify Breast Cancer Recurrences.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2024Article
- A Validated Register-Based Algorithm to Identify Patients Diagnosed with Recurrence of Surgically Treated Stage I Lung Cancer in Denmark.Clinical epidemiology · 2023Article
- Prevention of Late Recurrence: An Increasingly Important Target for Breast Cancer Research and Control.Journal of the National Cancer Institute · 2022Article
- Article
- Weakly supervised temporal model for prediction of breast cancer distant recurrence.Scientific reports · 2021Article
- A Validated Register-Based Algorithm to Identify Patients Diagnosed with Recurrence of Malignant Melanoma in Denmark.Clinical epidemiology · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundExact numbers of breast cancer recurrences are currently unknown at the population level, because they are challenging to actively collect. Previously, real-world data such as administrative claims have been used within expert- or data-driven (machine learning) algorithms for estimating cancer recurrence. We present the first systematic review and meta-analysis, to our knowledge, of publications estimating breast cancer recurrence at the population level using algorithms based on administrative data.
methodsThe systematic literature search followed Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. We evaluated and compared sensitivity, specificity, positive predictive value, negative predictive value, and overall accuracy of algorithms. A random-effects meta-analysis was performed using a generalized linear mixed model to obtain a pooled estimate of accuracy.
resultsSeventeen articles met the inclusion criteria. Most articles used information from medical files as the gold standard, defined as any recurrence. Two studies included bone metastases only in the definition of recurrence. Fewer studies used a model-based approach (decision trees or logistic regression) (41.2%) compared with studies using detection rules without specified model (58.8%). The generalized linear mixed model for all recurrence types reported an accuracy of 92.2% (95% confidence interval = 88.4% to 94.8%).
conclusionsPublications reporting algorithms for detecting breast cancer recurrence are limited in number and heterogeneous. A thorough analysis of the existing algorithms demonstrated the need for more standardization and validation. The meta-analysis reported a high accuracy overall, which indicates algorithms as promising tools to identify breast cancer recurrence at the population level. The rule-based approach combined with emerging machine learning algorithms could be interesting to explore in the future.
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.