ArticleCancer medicine2020
Machine Learning techniques in breast cancer prognosis prediction: A primary evaluation.
Article in Cancer medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- Pretreatment Prediction of Tumor Recurrence in Breast Cancer After Neoadjuvant Systemic Therapy Using Machine Learning With Clinical and CT Radiomics Features.The Kaohsiung journal of medical sciences · 2026Article
- Applications of Artificial Intelligence (AI) in Breast Cancer Care Delivery and Education: A Scoping Review.International journal of environmental research and public health · 2026Article
- Assessment of transformer-based AI in clinical oncology.BMC medical informatics and decision making · 2025Article
- Machine learning prediction of breast cancer local recurrence localization, and distant metastasis after local recurrences.Scientific reports · 2025Article
- Harnessing artificial intelligence for predicting breast cancer recurrence: a systematic review of clinical and imaging data.Discover oncology · 2025Review
- Multimodal data integration with machine learning for predicting PARP inhibitor efficacy and prognosis in ovarian cancer.Frontiers in oncology · 2025Article
- Enhancing breast cancer prediction through stacking ensemble and deep learning integration.PeerJ. Computer science · 2025Article
- Article
- Prediction model for survival of younger patients with breast cancer using the breast cancer public staging database.Scientific reports · 2024Article
- Prognostic prediction of breast cancer patients using machine learning models: a retrospective analysis.Gland surgery · 2024Article
- Breast cancer prediction using different machine learning methods applying multi factors.Journal of cancer research and clinical oncology · 2023Article
- Article
- Applying a GAN-based classifier to improve transcriptome-based prognostication in breast cancer.PLoS computational biology · 2023Article
- The innovative model based on artificial intelligence algorithms to predict recurrence risk of patients with postoperative breast cancer.Frontiers in oncology · 2023Article
- Artificial intelligence empowered digital health technologies in cancer survivorship care: A scoping review.Asia-Pacific journal of oncology nursing · 2022Article
- Combining Molecular, Imaging, and Clinical Data Analysis for Predicting Cancer Prognosis.Cancers · 2022Review
- Classification of Breast Cancer Nottingham Prognostic Index Using High-Dimensional Embedding and Residual Neural Network.Cancers · 2022Article
- Construction and Validation of a Newly Prognostic Signature for CRISPR-Cas9-Based Cancer Dependency Map Genes in Breast Cancer.Journal of oncology · 2022Article
- Random survival forest model identifies novel biomarkers of event-free survival in high-risk pediatric acute lymphoblastic leukemia.Computational and structural biotechnology journal · 2022Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
More than 750 000 women in Italy are surviving a diagnosis of breast cancer. A large body of literature tells us which characteristics impact the most on their prognosis. However, the prediction of each disease course and then the establishment of a therapeutic plan and follow-up tailored to the patient is still very complicated. In order to address this issue, a multidisciplinary approach has become widely accepted, while the Multigene Signature Panels and the Nottingham Prognostic Index are still discussed options. The current technological resources permit to gather many data for each patient. Machine Learning (ML) allows us to draw on these data, to discover their mutual relations and to esteem the prognosis for the new instances. This study provides a primary evaluation of the application of ML to predict breast cancer prognosis. We analyzed 1021 patients who underwent surgery for breast cancer in our Institute and we included 610 of them. Three outcomes were chosen: cancer recurrence (both loco-regional and systemic) and death from the disease within 32 months. We developed two types of ML models for every outcome (Artificial Neural Network and Support Vector Machine). Each ML algorithm was tested in accuracy (=95.29%-96.86%), sensitivity (=0.35-0.64), specificity (=0.97-0.99), and AUC (=0.804-0.916). These models might become an additional resource to evaluate the prognosis of breast cancer patients in our daily clinical practice. Before that, we should increase their sensitivity, according to literature, by considering a wider population sample with a longer period of follow-up. However, specificity, accuracy, minimal additional costs, and reproducibility are already encouraging.
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