ArticleBreast cancer research and treatment2018
Machine-learning-derived classifier predicts absence of persistent pain after breast cancer surgery with high accuracy.
Article in Breast cancer research and treatment, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 2 of them syntheses that pooled it.
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Who cites it
33 citing papers in PubMed, 2 syntheses or guidelines pooled it, 70 citations in OpenAlex.
- Moving towards the use of artificial intelligence in pain management.European journal of pain (London, England) · 2025Pooled it
- Artificial Intelligence and Machine Learning in Cancer Pain: A Systematic Review.Journal of pain and symptom management · 2024Pooled it
- Prediction Models for Acute and Chronic Postoperative Pain in Patients with Cancer: A Systematic Review and Meta-Analysis.Journal of pain research · 2026Review
- Machine learning research methods to predict postoperative pain and opioid use: a narrative review.Regional anesthesia and pain medicine · 2025Review
- Optimizing pain management in breast cancer care: Utilizing 'All of Us' data and deep learning to identify patients at elevated risk for chronic pain.Journal of nursing scholarship : an official publication of Sigma Theta Tau International Honor Society of Nursing · 2025Observational
- Distress, Pain, and Nausea on Postoperative Days 1 and 14 in Women Recovering From Breast-Conserving Surgery: A Repeated-Measures Study.Oncology nursing forum · 2024Article
- Incorporation of "Artificial Intelligence" for Objective Pain Assessment: A Comprehensive Review.Pain and therapy · 2024Review
- Use of artificial intelligence in breast surgery: a narrative review.Gland surgery · 2024Review
- Machine Learning Approaches to Predict Symptoms in People With Cancer: Systematic Review.JMIR cancer · 2024Review
- Distress, Pain, and Coping Strategies in Patients Undergoing Breast-Conserving Surgery: A Scoping Literature Review.Oncology nursing forum · 2023Article
- Artificial intelligence in breast cancer: application and future perspectives.Journal of cancer research and clinical oncology · 2023Review
- Non-Surgical Site Pain in Women following Breast Cancer Surgery: A Systematic Review and Meta-Analysis.Breast care (Basel, Switzerland) · 2023Review
- A Machine Learning Approach to Assess Patients with Deep Neck Infection Progression to Descending Mediastinitis: Preliminary Results.Diagnostics (Basel, Switzerland) · 2023Article
- Mobile care app development process: using the ADDIE model to manage symptoms after breast cancer surgery (step 1).Discover oncology · 2023Review
- Recursive computed ABC (cABC) analysis as a precise method for reducing machine learning based feature sets to their minimum informative size.Scientific reports · 2023Article
- Deep Learning Artificial Intelligence to Predict the Need for Tracheostomy in Patients of Deep Neck Infection Based on Clinical and Computed Tomography Findings-Preliminary Data and a Pilot Study.Diagnostics (Basel, Switzerland) · 2022Article
- Review
- Survivors' perceptions regarding the follow-up of pain complaints after breast cancer treatment: Distinct coping patterns.Frontiers in psychology · 2022Article
- Article
- Big Data in Studying Acute Pain and Regional Anesthesia.Journal of clinical medicine · 2021Review
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 2 countries.
Funding
Abstract
backgroundPrevention of persistent pain following breast cancer surgery, via early identification of patients at high risk, is a clinical need. Supervised machine-learning was used to identify parameters that predict persistence of significant pain.
methodsOver 500 demographic, clinical and psychological parameters were acquired up to 6 months after surgery from 1,000 women (aged 28-75 years) who were treated for breast cancer. Pain was assessed using an 11-point numerical rating scale before surgery and at months 1, 6, 12, 24, and 36. The ratings at months 12, 24, and 36 were used to allocate patents to either "persisting pain" or "non-persisting pain" groups. Unsupervised machine learning was applied to map the parameters to these diagnoses.
resultsA symbolic rule-based classifier tool was created that comprised 21 single or aggregated parameters, including demographic features, psychological and pain-related parameters, forming a questionnaire with "yes/no" items (decision rules). If at least 10 of the 21 rules applied, persisting pain was predicted at a cross-validated accuracy of 86% and a negative predictive value of approximately 95%.
conclusionsThe present machine-learned analysis showed that, even with a large set of parameters acquired from a large cohort, early identification of these patients is only partly successful. This indicates that more parameters are needed for accurate prediction of persisting pain. However, with the current parameters it is possible, with a certainty of almost 95%, to exclude the possibility of persistent pain developing in a woman being treated for breast cancer.
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Registered trials
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