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. 2024 Feb 29;8(2):pkae019.
doi: 10.1093/jncics/pkae019.

Prediction of cognitive decline in older breast cancer survivors: the Thinking and Living with Cancer study

Arthur Patrick McDeed  1 , Kathleen Van Dyk  2   3 , Xingtao Zhou  4 , Wanting Zhai  4 , Tim A Ahles  5 , Traci N Bethea  4 , Judith E Carroll  2   6 , Harvey Jay Cohen  7 , Zev M Nakamura  8 , Kelly E Rentscher  9 , Andrew J Saykin  10 , Brent J Small  11 , James C Root  5 , Heather Jim  12 , Sunita K Patel  13 , Brenna C Mcdonald  10 , Jeanne S Mandelblatt  4 , Jaeil Ahn  1
Affiliations

Prediction of cognitive decline in older breast cancer survivors: the Thinking and Living with Cancer study

Arthur Patrick McDeed et al. JNCI Cancer Spectr. .

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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Conflict of interest statement

Dr Saykin has received support from Avid Radiopharmaceuticals, a subsidiary of Eli Lilly (in kind contribution of positron emission tomography tracer precursor); Bayer Oncology (Scientific Advisory Board); Eisai (Scientific Advisory Board); Siemens Medical Solutions USA, Inc (Dementia Advisory Board); and Springer-Nature Publishing (Editorial Office Support as Editor-in-Chief, Brain Imaging and Behavior).

All other authors have declared no conflicts of interest.

Figures

Figure 1.
Figure 1.
Sample for longitudinal evaluation of cognition in older breast cancer survivors and matched controls without cancer, from the Thinking and Living with Cancer (TLC) Study. Only survivors who were enrolled and completed baseline (pre-systemic therapy), 12-month, and 24-month neurocognitive assessments before March 1, 2020 were included for analysis.
Figure 2.
Figure 2.
Receiver operating characteristic (ROC) curves from Least Absolute Shrinkage and Selection Operator (LASSO) models classifying 12-month transient cognitive decline using baseline to 12-month change scores on neuropsychological tests of attention, processing speed, and executive function (APE). A) Final LASSO machine learning model using 4-fold cross-validation including baseline APE scores as a selected predictor among 94 possible predictors. B) Final LASSO machine learning model excluding baseline APE scores as a predictor. The area under the ROC curve (AUC) and its 95% confidence interval (CI), sensitivity (%, 95% CI), and specificity (%, 95% CI) are presented at the Youden Index. Polygon 95 confidence regions for the ROC curve are displayed.
Figure 3.
Figure 3.
Receiver operating characteristic (ROC) curves from Least Absolue Shrinkage and Selection Operator (LASSO) models classifying 12-month transient cognitive decline using baseline to 12-month change scores on neuropsychological tests of attention, processing speed, and executive function (APE). A) Final LASSO machine learning model using 4-fold cross-validation including baseline APE scores as a selected predictor among 94 possible predictors. B) Final LASSO machine learning model excluding baseline APE scores as a predictor. The area under the ROC curve (AUC) and its 95% confidence interval (CI), sensitivity (%, 95% CI), and specificity (%, 95% CI) are presented at the Youden Index. Polygon 95 confidence regions for the ROC curve are displayed.
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