Associating persistent self-reported cognitive decline with neurocognitive decline in older breast cancer survivors using machine learning: The Thinking and Living with Cancer study
Creators: name: Small, Brent J
name: Dilawari, Asma A
name: Jacobsen, Paul B
name: Zhai, Wanting
name: McDonald, Brenna C
name: Ahn, Jaeil
name: Saykin, Andrew J
name: Jim, Heather
name: Cohen, Harvey Jay
name: Patel, Sunita K
name: Rentscher, Kelly E
name: Nakamura, Zev M
name: Zhou, Xingtao
name: Bethea, Traci N
name: Mandelblatt, Jeanne S
name: Root, James C
name: Ahles, Tim A
name: Carroll, Judith E
name: Graham, Deena
name: Van Dyk, Kathleen
Publisher: name: Elsevier
Year: 2022

description: <p>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&nbsp;=&nbsp;435) and age- and education-matched non-cancer controls (n&nbsp;=&nbsp;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&nbsp;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&nbsp;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&nbsp;months (n&nbsp;=&nbsp;60). Least Absolute Shrinkage and Selection Operator (LASSO) models distinguished survivors with persistent self-reported declines from controls (AUC&nbsp;=&nbsp;0.736) and survivors without decline (n&nbsp;=&nbsp;147; AUC&nbsp;=&nbsp;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.</p>