ArticleJNCI cancer spectrum2020
Prediction of Breast Cancer Treatment-Induced Fatigue by Machine Learning Using Genome-Wide Association Data.
Article in JNCI cancer spectrum, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT01993498 (A Prospective Cohort to Investigate Survivorship Issues in Patients With Early Cancer), which is not on this map. Cited by 11 papers, 2 of them syntheses that pooled it.
What it found
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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.
A Prospective Cohort to Investigate Survivorship Issues in Patients With Early Cancer
Who cites it
11 citing papers in PubMed, 2 syntheses or guidelines pooled it, 19 citations in OpenAlex.
- Diagnostic predictive models for cancer-related fatigue: current evidence and future directions.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2026Pooled it
- Exploring the role of health-related quality of life measures in predictive modelling for oncology: a systematic review.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2025Pooled it
- Genetic predisposition to persistent fatigue after a diagnosis of colorectal cancer.Journal of the National Cancer Institute · 2025Article
- Development of machine learning models to predict cancer-related fatigue in Dutch breast cancer survivors up to 15 years after diagnosis.Journal of cancer survivorship : research and practice · 2025Article
- Machine Learning to Predict the Individual Risk of Treatment-Relevant Toxicity for Patients With Breast Cancer Undergoing Neoadjuvant Systemic Treatment.JCO clinical cancer informatics · 2024Article
- Development and validation of a nomogram for predicting sever cancer-related fatigue in patients with cervical cancer.BMC cancer · 2024Article
- Predicting patient reported outcome measures: a scoping review for the artificial intelligence-guided patient preference predictor.Frontiers in artificial intelligence · 2024Article
- Predicting the germline dependence of hematuria risk in prostate cancer radiotherapy patients.Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology · 2023Article
- Mitigating long-term and delayed adverse events associated with cancer treatment: implications for survivorship.Nature reviews. Clinical oncology · 2023Review
- The Future of Breast Cancer Research in the Survivorship Field.Oncology and therapy · 2023Review
- PrACTiC: A Predictive Algorithm for Chemoradiotherapy-Induced Cytopenia in Glioblastoma Patients.Journal of oncology · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
23 authors at 9 institutions in 3 countries.
Funding
Abstract
backgroundWe aimed at predicting fatigue after breast cancer treatment using machine learning on clinical covariates and germline genome-wide data.
methodsWe accessed germline genome-wide data of 2799 early-stage breast cancer patients from the Cancer Toxicity study (NCT01993498). The primary endpoint was defined as scoring zero at diagnosis and higher than quartile 3 at 1 year after primary treatment completion on European Organization for Research and Treatment of Cancer quality-of-life questionnaires for Overall Fatigue and on the multidimensional questionnaire for Physical, Emotional, and Cognitive fatigue. First, we tested univariate associations of each endpoint with clinical variables and genome-wide variants. Then, using preselected clinical (false discovery rate < 0.05) and genomic (
resultsStatistically significant clinical associations were found only with Emotional and Cognitive Fatigue, including receipt of chemotherapy, anxiety, and pain. Some single nucleotide polymorphisms had some degree of association (
conclusionsGenomic analyses in this large cohort of breast cancer survivors suggest a possible genetic role for severe Cognitive Fatigue that warrants further exploration.
Identifiers
What OpenQuestion holds
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.