ArticleJCO clinical cancer informatics2021
Development of Machine Learning Algorithms for the Prediction of Financial Toxicity in Localized Breast Cancer Following Surgical Treatment.
Article in JCO clinical cancer informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 3 of them syntheses that pooled it.
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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.
The trial behind it
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
30 citing papers in PubMed, 3 syntheses or guidelines pooled it, 57 citations in OpenAlex.
- Interventions to mitigate cancer-related medical financial hardship: A systematic review and meta-analysis.Cancer · 2024Pooled it
- The usefulness of artificial intelligence in breast reconstruction: a systematic review.Breast cancer (Tokyo, Japan) · 2024Pooled it
- Present and future of machine learning in breast surgery: systematic review.The British journal of surgery · 2022Pooled it
- Financial Distress Screening and Financial Navigation Among Adolescents and Young Adults Within the National Cancer Institute Community Oncology Research Program: Results of the 2022 Landscape Survey.JCO oncology practice · 2026Article
- Artificial intelligence in breast surgery: Current applications, challenges, and future perspectives.The Journal of international medical research · 2026Review
- Nurse-Led Identification of Financial Toxicity in Stroke Patients Using Machine Learning: Development and Validation of an Associational Prediction Model.Journal of nursing management · 2026Article
- Artificial Intelligence in Breast Reconstruction: Enhancing Surgical Planning, Aesthetic Outcomes, and Patient-Centered Care.Journal of clinical medicine · 2025Review
- Financial Toxicity in ESKD.Kidney360 · 2025Article
- Artificial Intelligence Advancements in Oncology: A Review of Current Trends and Future Directions.Biomedicines · 2025Review
- The financial toxicity of breast cancer: a systematic mapping of the literature and identification of research challenges.Radiology and oncology · 2025Article
- Cancer cost-related subjective financial distress among breast cancer: a scoping review.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2024Article
- Using Machine Learning to Predict Unplanned Hospital Utilization and Chemotherapy Management From Patient-Reported Outcome Measures.JCO clinical cancer informatics · 2024Article
- Use of artificial intelligence in breast surgery: a narrative review.Gland surgery · 2024Review
- Catastrophic health expenditures, insurance churn, and non-employment among women with breast cancer.JNCI cancer spectrum · 2024Article
- Digital symptom assessment tools: the next frontier in financial toxicity screening.Nature reviews. Clinical oncology · 2024Article
- Financial toxicity among people with metastatic cancer: findings from the Cancer Experience Registry.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2024Article
- Predicting patient reported outcome measures: a scoping review for the artificial intelligence-guided patient preference predictor.Frontiers in artificial intelligence · 2024Article
- The application of artificial intelligence in health financing: a scoping review.Cost effectiveness and resource allocation : C/E · 2023Article
- Enhanced Surgical Decision-Making Tools in Breast Cancer: Predicting 2-Year Postoperative Physical, Sexual, and Psychosocial Well-Being following Mastectomy and Breast Reconstruction (INSPiRED 004).Annals of surgical oncology · 2023Article
- Machine Learning Models Using Routinely Collected Clinical Data Offer Robust and Interpretable Predictions of 90-Day Unplanned Acute Care Use for Cancer Immunotherapy Patients.JCO clinical cancer informatics · 2023Article
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeFinancial burden caused by cancer treatment is associated with material loss, distress, and poorer outcomes. Financial resources exist to support patients but identification of need is difficult. We sought to develop and test a tool to accurately predict an individual's risk of financial toxicity based on clinical, demographic, and patient-reported data prior to initiation of breast cancer treatment. PATIENTS AND
methodsWe surveyed 611 patients undergoing breast cancer therapy at MD Anderson Cancer Center. We collected data using the validated COmprehensive Score for financial Toxicity (COST) patient-reported outcome measure alongside other financial indicators (credit score, income, and insurance status). We also collected clinical and perioperative data. We trained and tested an ensemble of machine learning (ML) algorithms (neural network, regularized linear model, support vector machines, and a classification tree) to predict financial toxicity. Data were randomly partitioned into training and test samples (2:1 ratio). Predictive performance was assessed using area-under-the-receiver-operating-characteristics-curve (AUROC), accuracy, sensitivity, and specificity.
resultsIn our test sample (N = 203), 48 of 203 women (23.6%) reported significant financial burden. The algorithm ensemble performed well to predict financial burden with an AUROC of 0.85, accuracy of 0.82, sensitivity of 0.85, and specificity of 0.81. Key clinical predictors of financial burden from the linear model were neoadjuvant therapy (β
conclusionML models accurately predicted financial toxicity related to breast cancer treatment. These predictions may inform decision making and care planning to avoid financial distress during cancer treatment or enable targeted financial support. Further research is warranted to validate this tool and assess applicability for other types of 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.