Identifying pre-treatment risk factors for cancer-related cognitive decline in patients with breast cancer.

PURPOSE: To explore pre-treatment risk factors for cognitive decline in patients with breast cancer using a machine learning approach applied to a comprehensive multimodal clinical, biological, and neuroimaging dataset.

METHODS

Sixty-seven women with early breast cancer were assessed at diagnosis (T0), 8 months (T1), and 17 months after diagnosis (T2). Cognitive decline was defined using the Reliable Change Index. Patients were classified as showing no decline or decline at either follow-up. Five feature sets were evaluated: (1) patient characteristics, treatment, and psychosocial measures; (2) inflammatory and neural health markers, (3) structural brain volumes, (4) resting-state functional MRI connectivity, and (5) diffusion MRI measures. Random forest models were first trained on feature set 1, then sequentially combined with sets 2-5 to explore their additive predictive value. Each model underwent standardized preprocessing, recursive feature selection of the top 6 predictors, and tuning before random forest classification. A final composite model was constructed by pooling the six top predictors from each feature set to assess potential complementary multimodal information. Feature contributions were examined using SHAP values.

RESULTS

Of 67 patients, 33 (49%) experienced cognitive decline following treatment. Models achieved prediction accuracies of 76%, improving up to 81% when MRI measures and/or serum markers were included. Key baseline predictors of cognitive decline included more aggressive subtypes, planned systemic therapy, perceived stress, and limited cognitive and brain reserve.

CONCLUSION

Machine learning explored potential pre-treatment risk factors for cancer-related cognitive decline in patients with breast cancer. These findings highlight potential risk factors that could support risk-stratification.