ArticleBMC cancer2018
Using Resistin, glucose, age and BMI to predict the presence of breast cancer.
Article in BMC cancer, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
What it found
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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
38 citing papers in PubMed, 279 citations in OpenAlex.
- Multidimensional risk impact of obesity on breast cancer incidence, treatment, and prognosis.International journal of obesity (2005) · 2026Review
- Three-way multi-criteria decision-making with linguistic information using prospect-regret theory and generalised TODIM for heart failure data analysis.Scientific reports · 2026Article
- Insulin resistance as a modifiable contributor to breast cancer: mechanisms, biomarkers, and therapeutic implications.Discover oncology · 2026Review
- Machine learning in AIRR diagnostics: Advances and applications.Immunoinformatics (Amsterdam, Netherlands) · 2025Article
- The role of AI for improved management of breast cancer: Enhanced diagnosis and health disparity mitigation.Computer methods and programs in biomedicine · 2025Review
- Breast Cancer Prediction Using Rotation Forest Algorithm Along with Finding the Influential Causes.Bioengineering (Basel, Switzerland) · 2025Article
- Efficiency of ranked set sampling designs in power Lindley system reliability estimation with uncensored and right-censored data.Scientific reports · 2025Article
- A hybrid explainable federated-based vision transformer framework for breast cancer prediction via risk factors.Scientific reports · 2025Article
- XAI-driven CatBoost multi-layer perceptron neural network for analyzing breast cancer.Scientific reports · 2024Article
- Predicting Respiratory Diseases Attributed to PM2.5 Air Pollution in Nairobi County Using Random Forest Model.International journal of innovative science and research technology · 2024Article
- On the Applicability of Quantum Machine Learning.Entropy (Basel, Switzerland) · 2023Article
- Metaverse and Healthcare: Machine Learning-Enabled Digital Twins of Cancer.Bioengineering (Basel, Switzerland) · 2023Article
- Implementation and empirical evaluation of a quantum machine learning pipeline for local classification.PloS one · 2023Article
- Utilizing logistic regression to compare risk factors in disease modeling with imbalanced data: a case study in vitamin D and cancer incidence.Frontiers in oncology · 2023Article
- Efficient cross-validation traversals in feature subset selection.Scientific reports · 2022Article
- Nanostructured Layer of Silver for Detection of Small Biomolecules in Surface-Assisted Laser Desorption Ionization Mass Spectrometry.Materials (Basel, Switzerland) · 2022Article
- LANDMark: an ensemble approach to the supervised selection of biomarkers in high-throughput sequencing data.BMC bioinformatics · 2022Article
- A Deep Neural Network for Early Detection and Prediction of Chronic Kidney Disease.Diagnostics (Basel, Switzerland) · 2022Article
- Obesity, inflammation, and cancer in dogs: Review and perspectives.Frontiers in veterinary science · 2022Review
- Prediction and Diagnosis of Respiratory Disease by Combining Convolutional Neural Network and Bi-directional Long Short-Term Memory Methods.Frontiers in public health · 2022Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundThe goal of this exploratory study was to develop and assess a prediction model which can potentially be used as a biomarker of breast cancer, based on anthropometric data and parameters which can be gathered in routine blood analysis.
methodsFor each of the 166 participants several clinical features were observed or measured, including age, BMI, Glucose, Insulin, HOMA, Leptin, Adiponectin, Resistin and MCP-1. Machine learning algorithms (logistic regression, random forests, support vector machines) were implemented taking in as predictors different numbers of variables. The resulting models were assessed with a Monte Carlo Cross-Validation approach to determine 95% confidence intervals for the sensitivity, specificity and AUC of the models.
resultsSupport vector machines models using Glucose, Resistin, Age and BMI as predictors allowed predicting the presence of breast cancer in women with sensitivity ranging between 82 and 88% and specificity ranging between 85 and 90%. The 95% confidence interval for the AUC was [0.87, 0.91].
conclusionsThese findings provide promising evidence that models combining age, BMI and metabolic parameters may be a powerful tool for a cheap and effective biomarker of breast cancer.
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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.