ArticleBritish journal of cancer2021
Cancer Grade Model: a multi-gene machine learning-based risk classification for improving prognosis in breast cancer.
Article in British journal of cancer, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.
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Who cites it
13 citing papers in PubMed, 2 syntheses or guidelines pooled it, 35 citations in OpenAlex.
- Investigating the effects of artificial intelligence on the personalization of breast cancer management: a systematic study.BMC cancer · 2024Pooled it
- Machine Learning Models for the Identification of Prognostic and Predictive Cancer Biomarkers: A Systematic Review.International journal of molecular sciences · 2023Pooled it
- Independent Risk Factors and a New Nomogram for Predicting Breast Cancer Risk for Bone Metastasis in Chinese Women: A Retrospective Study with External Validation.Journal of clinical medicine · 2026Article
- Application of explainable artificial intelligence integrating with electronic health record in oncology.Exploration of targeted anti-tumor therapy · 2026Review
- Endocrine Disruptors and Breast Cancer: A Comprehensive Review.Biomedicines · 2025Review
- Rigorous validation of machine learning in laboratory medicine: guidance toward quality improvement.Critical reviews in clinical laboratory sciences · 2025Review
- Machine learning-based single-sample molecular classifier for cancer grading.Frontiers in oncology · 2025Article
- Exploring Prognostic Gene Factors in Breast Cancer via Machine Learning.Biochemical genetics · 2024Article
- Investigation of recurrence prediction ability of EndoPredictBreast cancer (Tokyo, Japan) · 2024Article
- Endoplasmic reticulum stress in breast cancer: a predictive model for prognosis and therapy selection.Frontiers in immunology · 2024Article
- Drug repurposing and prediction of multiple interaction types via graph embedding.BMC bioinformatics · 2023Article
- Determination of a DNA repair-related gene signature with potential implications for prognosis and therapeutic response in pancreatic adenocarcinoma.Frontiers in oncology · 2022Article
- Presentation of Novel Architecture for Diagnosis and Identifying Breast Cancer Location Based on Ultrasound Images Using Machine Learning.Diagnostics (Basel, Switzerland) · 2021Article
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
Abstract
backgroundPrognostic stratification of breast cancers remains a challenge to improve clinical decision making. We employ machine learning on breast cancer transcriptomics from multiple studies to link the expression of specific genes to histological grade and classify tumours into a more or less aggressive prognostic type. MATERIALS AND
methodsMicroarray data of 5031 untreated breast tumours spanning 33 published datasets and corresponding clinical data were integrated. A machine learning model based on gradient boosted trees was trained on histological grade-1 and grade-3 samples. The resulting predictive model (Cancer Grade Model, CGM) was applied on samples of grade-2 and unknown-grade (3029) for prognostic risk classification.
resultsA 70-gene signature for assessing clinical risk was identified and was shown to be 90% accurate when tested on known histological-grade samples. The predictive framework was validated through survival analysis and showed robust prognostic performance. CGM was cross-referenced with existing genomic tests and demonstrated the competitive predictive power of tumour risk.
conclusionsCGM is able to classify tumours into better-defined prognostic categories without employing information on tumour size, stage, or subgroups. The model offers means to improve prognosis and support the clinical decision and precision treatments, thereby potentially contributing to preventing underdiagnosis of high-risk tumours and minimising over-treatment of low-risk disease.
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