ArticleFrontiers in human neuroscience2017
Predicting Long-Term Cognitive Outcome Following Breast Cancer with Pre-Treatment Resting State fMRI and Random Forest Machine Learning.
Article in Frontiers in human neuroscience, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers, 3 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.
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Who cites it
55 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Prevalence of cognitive impairment following chemotherapy treatment for breast cancer: a systematic review and meta-analysis.Scientific reports · 2022Pooled it
- Prevalence of cognitive impairment and change in patients with breast cancer: A systematic review of longitudinal studies.Psycho-oncology · 2021Pooled it
- The Developmental Trajectory of Cancer-Related Cognitive Impairment in Breast Cancer Patients: A Systematic Review of Longitudinal Neuroimaging Studies.Neuropsychology review · 2020Pooled it
- Sustained effects of mantra meditation compared to music listening on neurocognitive outcomes of breast cancer survivors: A brief report of a randomized control trial.Journal of psychosomatic research · 2021Trial
- Effects of meditation compared to music listening on biomarkers in breast cancer survivors with cognitive complaints: secondary outcomes of a pilot randomized control trial.Explore (New York, N.Y.)Trial
- Unique local gradient-based functional fingerprints enable precisely identifying individual infants over ages.Developmental cognitive neuroscience · 2026Article
- Identifying pre-treatment risk factors for cancer-related cognitive decline in patients with breast cancer.Breast (Edinburgh, Scotland) · 2026Article
- Comparison of Feature Selection Methods in Machine Learning Models of Cancer Information Seeking Among United States Adults: Cross-Sectional Study.JMIR medical informatics · 2026Article
- Applications of machine learning and natural language processing to neurocognitive outcomes in posttreatment cancer survivors: a scoping review.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2026Article
- Limited generalizability of dynamic fMRI correlates of adolescent rumination.Nature. Mental health · 2025Article
- Limited generalizability of dynamic fMRI correlates of adolescent rumination.bioRxiv : the preprint server for biology · 2025Article
- Deciphering transcriptomic changes in chemobrain: a comprehensive review.Acta neuropathologica communications · 2025Review
- Barriers and facilitators for participation in brain magnetic resonance imaging (MRI) scans in cancer research: a feasibility and acceptability analysis.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2025Article
- Comparison of Deep Learning and Traditional Machine Learning Models for Predicting Mild Cognitive Impairment Using Plasma Proteomic Biomarkers.International journal of molecular sciences · 2025Article
- Challenging Cognitive Load Theory: The Role of Educational Neuroscience and Artificial Intelligence in Redefining Learning Efficacy.Brain sciences · 2025Review
- Characterizing cancer-related cognitive impairments and impact on quality of life in women with metastatic breast cancer.Breast cancer research and treatment · 2025Article
- Prediction of future aging-related slow gait and its determinants with deep learning and logistic regression.PloS one · 2025Article
- Seeing through "brain fog": neuroimaging assessment and imaging biomarkers for cancer-related cognitive impairments.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Review
- Feature Selection and Machine Learning Approaches in Prediction of Current E-Cigarette Use Among U.S. Adults in 2022.International journal of environmental research and public health · 2024Article
- Nurturing Longitudinal Samples 2.0.Western journal of nursing research · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
We aimed to determine if resting state functional magnetic resonance imaging (fMRI) acquired at pre-treatment baseline could accurately predict breast cancer-related cognitive impairment at long-term follow-up. We evaluated 31 patients with breast cancer (age 34-65) prior to any treatment, post-chemotherapy and 1 year later. Cognitive testing scores were normalized based on data obtained from 43 healthy female controls and then used to categorize patients as impaired or not based on longitudinal changes. We measured clustering coefficient, a measure of local connectivity, by applying graph theory to baseline resting state fMRI and entered these metrics along with relevant patient-related and medical variables into random forest classification. Incidence of cognitive impairment at 1 year follow-up was 55% and was predicted by classification algorithms with up to 100% accuracy (
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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.