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.

中文翻译：

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使用机器学习将持续的自我报告的认知衰退与老年乳腺癌幸存者的神经认知衰退联系起来：与癌症一起思考和生活研究

介绍

许多癌症幸存者报告在诊断和治疗后出现认知问题。然而，患者在生存早期报告的认知症状的临床意义可能尚不清楚。我们使用机器学习方法来确定诊断后两年持续自我报告的认知症状与老年乳腺癌幸存者的前瞻性队列中的神经认知测试表现之间的关联。

材料和方法

我们招募了患有非转移性疾病的乳腺癌幸存者 ( n  = 435) 和年龄和教育匹配的非癌症对照 ( n = 441) 从 2010 年 8 月到 2017 年 12 月，一直持续到 2020 年 1 月；我们排除了患有神经系统疾病的女性，所有女性在入组时都通过了认知筛查。女性完成了 FACT-Cog 感知认知障碍 (PCI) 量表和注意力、处理速度、执行功能、学习、记忆和视觉空间能力的神经认知测试，以及入学时（系统前治疗前）和每年进行的日常生活评估的定时活动24 个月，共计 59 项个体神经认知测量。我们将持续的自我报告的认知能力下降定义为从入组到 12 个月持续到 24 个月的 PCI 量表有临床意义的下降（3.7+ 分）。

结果

幸存者和对照组的样本年龄在 60-89 岁之间。33% 的幸存者在 12 个月时自我报告认知能力下降，三分之二的人持续下降到 24 个月（n  = 60）。最小绝对收缩和选择算子 (LASSO) 模型将自我报告持续下降的幸存者与对照组（AUC = 0.736）和没有下降的幸存者（n  = 147；AUC = 0.744）区分开来。将组分开的变量主要是神经认知测试性能变化分数，包括列表学习、语言流畅性和注意力测量的下降。

讨论

机器学习可能有助于加深我们对癌症相关认知能力下降的理解。我们的结果表明，患有乳腺癌的老年女性持续存在的自我报告的认知问题与一系列值得临床关注的轻度神经认知变化有关。