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An Explainable AI Approach for Breast Cancer Metastasis Prediction Based on Clinicopathological Data
43/43 checkable references clean · checked 2026-07-25
Every reference with a DOI in the deposited reference list resolved to a known work in Crossref or DataCite at the dated check, and none carried a retraction, withdrawal, or removal notice.
6 without a DOI — not checked. A reference deposited without a DOI is never matched by title or guessed at; it stays outside the checked set, and this line discloses that.
The 43 checked references that resolve
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Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries
resolves10.1172/JCI22320
Distinct organ-specific metastatic potential of individual breast cancer cells and primary tumors
resolves10.1038/bjc.2015.127
Prognosis of metastatic breast cancer: are there differences between patients with de novo and recurrent metastatic breast cancer?
resolves10.1093/annonc/mdy192
4th ESO–ESMO International Consensus Guidelines for Advanced Breast Cancer (ABC 4)
resolves10.1007/978-3-030-94191-8_41
Early Prediction of ICU Admission Within COVID-19 Patients Using Machine Learning Techniques
resolves10.1016/j.cegh.2018.10.003
Prediction of survival and metastasis in breast cancer patients using machine learning classifiers
resolves10.1007/s11042-020-10448-w
Predicting the recurrence of breast cancer using machine learning algorithms
resolves10.3390/cancers12123817
Machine Learning Algorithms to Predict Recurrence within 10 Years after Breast Cancer Surgery: A Prospective Cohort Study
resolves10.1016/j.inffus.2021.10.007
Information fusion as an integrative cross-cutting enabler to achieve robust, explainable, and trustworthy medical artificial intelligence
resolves10.1016/j.ijmedinf.2019.05.003
Predicting breast cancer metastasis by using serum biomarkers and clinicopathological data with machine learning technologies
resolves10.3389/fgene.2021.771092
Predicting Bone Metastasis Using Gene Expression-Based Machine Learning Models
resolves10.1016/j.csbj.2021.09.001
Machine learning and deep learning methods that use omics data for metastasis prediction
resolves10.1002/ddr.21036
Challenges for the Application and Development of Omics Health Technologies in Developing Countries
resolves10.1109/TBME.2018.2882867
Predicting Invasive Disease-Free Survival for Early Stage Breast Cancer Patients Using Follow-Up Clinical Data
resolves10.1371/journal.pone.0274691
A machine learning ensemble approach for 5- and 10-year breast cancer invasive disease event classification
resolves10.3390/jpm12091496
Application of Artificial Intelligence Techniques to Predict Risk of Recurrence of Breast Cancer: A Systematic Review
resolves10.1023/A:1007607513941
An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization
resolves10.1371/journal.pone.0130140
On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation
resolves10.1109/ICCV.2017.74
Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization
resolves10.1038/s41598-020-62724-2
Resolving challenges in deep learning-based analyses of histopathological images using explanation methods
resolves10.1038/s42256-021-00303-4
Morphological and molecular breast cancer profiling through explainable machine learning
resolves10.1007/s11042-021-10929-6
MGBN: Convolutional neural networks for automated benign and malignant breast masses classification
resolves10.1007/s00521-020-05394-5
Convolutional neural network-based models for diagnosis of breast cancer
resolves10.1109/JPROC.2021.3060483
Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications
resolves10.1016/j.artmed.2020.101858
A case-based ensemble learning system for explainable breast cancer recurrence prediction
resolves10.1038/s41598-021-86327-7
Explainable machine learning can outperform Cox regression predictions and provide insights in breast cancer survival
resolves10.3390/s19132969
Local Interpretable Model-Agnostic Explanations for Classification of Lymph Node Metastases
resolves10.1093/bioinformatics/btr597
MissForest—non-parametric missing value imputation for mixed-type data
resolves10.1371/journal.pone.0164841
Outcome of Breast Cancer in Moroccan Young Women Correlated to Clinic-Pathological Features, Risk Factors and Treatment: A Comparative Study of 716 Cases in a Single Institution
resolves10.1038/s41591-018-0307-0
The practical implementation of artificial intelligence technologies in medicine
resolves10.4048/jbc.2011.14.4.308
Mucinous Carcinoma of the Breast in Comparison with Invasive Ductal Carcinoma: Clinicopathologic Characteristics and Prognosis
resolves10.1007/s13218-020-00636-z
Measuring the Quality of Explanations: The System Causability Scale (SCS)
resolves10.1109/MC.2021.3092610
Toward Human–AI Interfaces to Support Explainability and Causability in Medical AI
The 6 references without a DOI — listed, not checked
no DOI — not checkedBreast cancer fact sheet
no DOI — not checkedRecurrent breast cancer treatment strategies for maintaining and prolonging good quality of life
no DOI — not checkedCatBoost: Unbiased boosting with categorical features
no DOI — not checkedInterpretable Mach. Learn.
no DOI — not checkedA unified approach to interpreting model predictions
no DOI — not checkedMachine learning explainability in breast cancer survival
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