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McDeed AP, Van Dyk K, Zhou X, Zhai W, Ahles TA, Bethea TN, Carroll JE, Cohen HJ, Nakamura ZM, Rentscher KE, Saykin AJ, Small BJ, Root JC, Jim H, Patel SK, Mcdonald BC, Mandelblatt JS, Ahn J. Prediction of cognitive decline in older breast cancer survivors: the Thinking and Living with Cancer study. JNCI Cancer Spectr 2024; 8:pkae019. [PMID: 38556480 PMCID: PMC11031271 DOI: 10.1093/jncics/pkae019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Revised: 02/27/2024] [Accepted: 03/05/2024] [Indexed: 04/02/2024] Open
Abstract
PURPOSE Cancer survivors commonly report cognitive declines after cancer therapy. Due to the complex etiology of cancer-related cognitive decline (CRCD), predicting who will be at risk of CRCD remains a clinical challenge. We developed a model to predict breast cancer survivors who would experience CRCD after systematic treatment. METHODS We used the Thinking and Living with Cancer study, a large ongoing multisite prospective study of older breast cancer survivors with complete assessments pre-systemic therapy, 12 months and 24 months after initiation of systemic therapy. Cognition was measured using neuropsychological testing of attention, processing speed, and executive function (APE). CRCD was defined as a 0.25 SD (of observed changes from baseline to 12 months in matched controls) decline or greater in APE score from baseline to 12 months (transient) or persistent as a decline 0.25 SD or greater sustained to 24 months. We used machine learning approaches to predict CRCD using baseline demographics, tumor characteristics and treatment, genotypes, comorbidity, and self-reported physical, psychosocial, and cognitive function. RESULTS Thirty-two percent of survivors had transient cognitive decline, and 41% of these women experienced persistent decline. Prediction of CRCD was good: yielding an area under the curve of 0.75 and 0.79 for transient and persistent decline, respectively. Variables most informative in predicting CRCD included apolipoprotein E4 positivity, tumor HER2 positivity, obesity, cardiovascular comorbidities, more prescription medications, and higher baseline APE score. CONCLUSIONS Our proof-of-concept tool demonstrates our prediction models are potentially useful to predict risk of CRCD. Future research is needed to validate this approach for predicting CRCD in routine practice settings.
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Affiliation(s)
- Arthur Patrick McDeed
- Department of Biostatistics, Bioinformatics, and Biomathematics, Georgetown University, Washington, DC, USA
| | - Kathleen Van Dyk
- Semel Institute for Neuroscience and Human Behavior, Department of Psychiatry & Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, USA
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, Los Angeles, CA, USA
| | - Xingtao Zhou
- Georgetown University Lombardi Comprehensive Cancer Center, Cancer Prevention and Control Program, Department of Oncology and Georgetown Lombardi Institute for Cancer and Aging Research, Georgetown University, Washington, DC, USA
| | - Wanting Zhai
- Georgetown University Lombardi Comprehensive Cancer Center, Cancer Prevention and Control Program, Department of Oncology and Georgetown Lombardi Institute for Cancer and Aging Research, Georgetown University, Washington, DC, USA
| | - Tim A Ahles
- Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Traci N Bethea
- Georgetown University Lombardi Comprehensive Cancer Center, Cancer Prevention and Control Program, Department of Oncology and Georgetown Lombardi Institute for Cancer and Aging Research, Georgetown University, Washington, DC, USA
| | - Judith E Carroll
- Semel Institute for Neuroscience and Human Behavior, Department of Psychiatry & Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, USA
- Cousins Center for Psychoneuroimmunology, University of California, Los Angeles, Los Angeles, CA, USA
| | - Harvey Jay Cohen
- Center for the Study of Aging and Human Development, Duke University Medical Center, Durham, NC, USA
| | - Zev M Nakamura
- Department of Psychiatry, University of North Carolina–Chapel Hill, Chapel Hill, NC, USA
| | - Kelly E Rentscher
- Department of Psychiatry and Behavioral Medicine, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Andrew J Saykin
- Center for Neuroimaging and Indiana Alzheimer’s Disease Research Center, Department of Radiology and Imaging Sciences and the Indiana University Melvin and Bren Simon Comprehensive Cancer Center, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Brent J Small
- School of Aging Studies, University of South Florida, and Health Outcomes and Behavior Program, Moffitt Cancer Center, Tampa, FL, USA
| | - James C Root
- Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Heather Jim
- Department of Health Outcomes and Behavior, Moffitt Cancer Center and Research Institute, University of South Florida, Tampa, FL, USA
| | - Sunita K Patel
- Outcomes Division, Population Sciences, City of Hope National Medical Center, Los Angeles, CA, USA
| | - Brenna C Mcdonald
- Center for Neuroimaging and Indiana Alzheimer’s Disease Research Center, Department of Radiology and Imaging Sciences and the Indiana University Melvin and Bren Simon Comprehensive Cancer Center, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Jeanne S Mandelblatt
- Georgetown University Lombardi Comprehensive Cancer Center, Cancer Prevention and Control Program, Department of Oncology and Georgetown Lombardi Institute for Cancer and Aging Research, Georgetown University, Washington, DC, USA
| | - Jaeil Ahn
- Department of Biostatistics, Bioinformatics, and Biomathematics, Georgetown University, Washington, DC, USA
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