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Associating persistent self-reported cognitive decline with neurocognitive decline in older breast cancer survivors using machine learning: The Thinking and Living with Cancer study

  • Author Footnotes
    1 Co-first authors.
    Kathleen Van Dyk
    Correspondence
    Corresponding author at: Dept. of Psychiatry, Division of Geriatric Psychiatry, UCLA Semel Institute for Neuroscience & Human Behavior, 760 Westwood Plaza, Suite 37-372, Los Angeles, CA 90095-1759, United States of America.
    Footnotes
    1 Co-first authors.
    Affiliations
    Semel Institute for Neuroscience and Human Behavior, Department of Psychiatry & Biobehavioral Sciences, University of California, Los Angeles, CA, United States of America

    Jonsson Comprehensive Cancer Center, University of California, Los Angeles, CA, United States of America
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  • Author Footnotes
    1 Co-first authors.
    Jaeil Ahn
    Footnotes
    1 Co-first authors.
    Affiliations
    Department of Biostatistics, Bioinformatics, and Biomathematics, Georgetown University, Washington, DC, United States of America
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  • Xingtao Zhou
    Affiliations
    Georgetown Lombardi Comprehensive Cancer Center Georgetown University, Washington, DC, United States of America
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  • Wanting Zhai
    Affiliations
    Georgetown Lombardi Comprehensive Cancer Center Georgetown University, Washington, DC, United States of America
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  • Tim A. Ahles
    Affiliations
    Memorial Sloan Kettering Cancer Center, New York, NY, United States of America
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  • Traci N. Bethea
    Affiliations
    Georgetown Lombardi Comprehensive Cancer Center Georgetown University, Washington, DC, United States of America
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  • Judith E. Carroll
    Affiliations
    Semel Institute for Neuroscience and Human Behavior, Department of Psychiatry & Biobehavioral Sciences, University of California, Los Angeles, CA, United States of America

    Cousins Center for Psychoneuroimmunology, University of California, Los Angeles, CA, United States of America
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  • Harvey Jay Cohen
    Affiliations
    Center for the Study of Aging and Human Development, Duke University Medical Center, Durham, NC, United States of America
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  • Asma A. Dilawari
    Affiliations
    Georgetown Lombardi Comprehensive Cancer Center Georgetown University, Washington, DC, United States of America
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  • Deena Graham
    Affiliations
    John Theurer Cancer Center, Hackensack University Medical Center, Hackensack, NJ, United States of America
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  • Paul B. Jacobsen
    Affiliations
    Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, MD, United States of America
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  • Heather Jim
    Affiliations
    Department of Health Outcomes and Behavior, Moffitt Cancer Center and Research Institute, University of South Florida, Tampa, FL, United States of America
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  • Brenna C. McDonald
    Affiliations
    Center for Neuroimaging, Department of Radiology and Imaging Sciences and the Indiana University Melvin and Bren Simon Comprehensive Cancer Center, Indiana University School of Medicine, Indianapolis, IN, United States of America
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  • Zev M. Nakamura
    Affiliations
    Department of Psychiatry, University of North Carolina–Chapel Hill, Chapel Hill, NC, United States of America
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  • Sunita K. Patel
    Affiliations
    City of Hope National Medical Center, Los Angeles, CA, United States of America
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  • Kelly E. Rentscher
    Affiliations
    Semel Institute for Neuroscience and Human Behavior, Department of Psychiatry & Biobehavioral Sciences, University of California, Los Angeles, CA, United States of America

    Cousins Center for Psychoneuroimmunology, University of California, Los Angeles, CA, United States of America
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  • Andrew J. Saykin
    Affiliations
    Center for Neuroimaging, Department of Radiology and Imaging Sciences and the Indiana University Melvin and Bren Simon Comprehensive Cancer Center, Indiana University School of Medicine, Indianapolis, IN, United States of America
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  • Brent J. Small
    Affiliations
    University of South Florida, Health Outcome and Behavior Program and Biostatistics Resource Core, H. Lee Moffitt Cancer Center, Research Institute at the University of South Florida, Tampa, FL, United States of America
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  • Author Footnotes
    2 Co-supervising authors.
    Jeanne S. Mandelblatt
    Footnotes
    2 Co-supervising authors.
    Affiliations
    Georgetown Lombardi Comprehensive Cancer Center Georgetown University, Washington, DC, United States of America
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  • Author Footnotes
    2 Co-supervising authors.
    James C. Root
    Footnotes
    2 Co-supervising authors.
    Affiliations
    Memorial Sloan Kettering Cancer Center, New York, NY, United States of America
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  • Author Footnotes
    1 Co-first authors.
    2 Co-supervising authors.
Published:August 24, 2022DOI:https://doi.org/10.1016/j.jgo.2022.08.005

      Abstract

      Introduction

      Many cancer survivors report cognitive problems following diagnosis and treatment. However, the clinical significance of patient-reported cognitive symptoms early in survivorship can be unclear. We used a machine learning approach to determine the association of persistent self-reported cognitive symptoms two years after diagnosis and neurocognitive test performance in a prospective cohort of older breast cancer survivors.

      Materials and methods

      We enrolled breast cancer survivors with non-metastatic disease (n = 435) and age- and education-matched non-cancer controls (n = 441) between August 2010 and December 2017 and followed until January 2020; we excluded women with neurological disease and all women passed a cognitive screen at enrollment. Women completed the FACT-Cog Perceived Cognitive Impairment (PCI) scale and neurocognitive tests of attention, processing speed, executive function, learning, memory and visuospatial ability, and timed activities of daily living assessments at enrollment (pre-systemic treatment) and annually to 24 months, for a total of 59 individual neurocognitive measures. We defined persistent self-reported cognitive decline as clinically meaningful decline (3.7+ points) on the PCI scale from enrollment to twelve months with persistence to 24 months. Analysis used four machine learning models based on data for change scores (baseline to twelve months) on the 59 neurocognitive measures and measures of depression, anxiety, and fatigue to determine a set of variables that distinguished the 24-month persistent cognitive decline group from non-cancer controls or from survivors without decline.

      Results

      The sample of survivors and controls ranged in age from were ages 60–89. Thirty-three percent of survivors had self-reported cognitive decline at twelve months and two-thirds continued to have persistent decline to 24 months (n = 60). Least Absolute Shrinkage and Selection Operator (LASSO) models distinguished survivors with persistent self-reported declines from controls (AUC = 0.736) and survivors without decline (n = 147; AUC = 0.744). The variables that separated groups were predominantly neurocognitive test performance change scores, including declines in list learning, verbal fluency, and attention measures.

      Discussion

      Machine learning may be useful to further our understanding of cancer-related cognitive decline. Our results suggest that persistent self-reported cognitive problems among older women with breast cancer are associated with a constellation of mild neurocognitive changes warranting clinical attention.

      Keywords

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