Department of Biostatistics, Bioinformatics, and Biomathematics, Georgetown University, Washington, DC, USA.
Semel Institute for Neuroscience and Human Behavior, Department of Psychiatry & Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, USA.
JNCI Cancer Spectr. 2024 Feb 29;8(2). doi: 10.1093/jncics/pkae019.
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.
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.
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.
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.
癌症幸存者在癌症治疗后常报告认知能力下降。由于癌症相关认知障碍（CRCD）的复杂病因，预测谁将有患 CRCD 的风险仍然是一个临床挑战。我们开发了一种模型，以预测接受系统治疗后会发生 CRCD 的乳腺癌幸存者。
我们使用了“思考和生活中的癌症”研究，这是一项正在进行的大型多中心前瞻性研究，对象是接受系统治疗前、治疗开始后 12 个月和 24 个月有完整评估的老年乳腺癌幸存者。认知能力使用注意力、处理速度和执行功能（APE）的神经心理学测试进行测量。CRCD 的定义为 APE 评分从基线到 12 个月（一过性）或持续下降 0.25 个标准差或更多，与匹配对照组从基线到 12 个月的观察变化相比下降 0.25 个标准差或更多。我们使用机器学习方法，使用基线人口统计学、肿瘤特征和治疗、基因型、合并症以及自我报告的身体、心理社会和认知功能来预测 CRCD。
32%的幸存者有一过性认知下降，其中 41%的女性有持续性认知下降。CRCD 的预测效果良好：一过性和持续性下降的曲线下面积分别为 0.75 和 0.79。预测 CRCD 最有信息的变量包括载脂蛋白 E4 阳性、肿瘤 HER2 阳性、肥胖、心血管合并症、更多处方药和更高的基线 APE 评分。
我们的概念验证工具表明，我们的预测模型有可能用于预测 CRCD 的风险。需要进一步的研究来验证这种方法在常规实践环境中预测 CRCD 的能力。
