Development and validation of a risk prediction model for cognitive impairment in breast cancer patients

Abstract

Background: Breast cancer patients often experience cognitive impairment as a complication during treatment, which seriously affects their quality of life. This study aimed to assess the risk factors associated with cognitive impairment in breast cancer patients and to construct and validate a nomogram model to predict cognitive impairment in this population.

Methods: In this study, we used a convenience sampling method to select 423 breast cancer patients who attended the Department of Breast Surgery at the First Hospital of Jinzhou Medical University from September 2023 to March 2024. We analyzed these patients' cognitive impairment risk factors through LASSO regression and logistic regression analysis to develop a predictive model. The model was evaluated using the area under the curve (AUC) from the receiver operating characteristic (ROC) curve and the calibration curve and decision curve analysis.

Results: This study found a prevalence of cognitive impairment of 19.62% among breast cancer patients. A nomogram model was developed based on six influencing factors: age, educational level, pathological type, treatment program, emotional state, and fatigue. The area under the curve (AUC) for the model's training and validation groups was 0.944 and 0.931, respectively. The model calibration curves showed a high degree of consistency, and the decision curve analysis (DCA) indicated good clinical applicability of the model.

Conclusions: This nomogram demonstrates good discrimination, calibration, and clinical applicability, making it a more intuitive predictor of the risk of cognitive impairment in breast cancer patients.

Keywords: Breast cancer; Cognitive impairment; Nomogram; Risk factors; Risk prediction.

Fig. 1

A LASSO regression coefficient path diagram. B LASSO regression cross-validation curves. The optimal lambda is determined using the ten-fold cross-validation method. The left vertical dashed line indicates the minimum value of the cross-validation error, while the right vertical dashed line indicates the minimum value of the cross-validation error within one standard deviation

Fig. 2

Nomogram for detecting cognitive impairment in breast cancer patients. Each factor corresponds to a single point at the top of the graph for an individual patient. We sum the scores from all individual points to obtain the total score for that patient. The probability of cognitive impairment in a breast cancer patient is derived by projecting the total score onto the incidence risk axis

Fig. 3

A Training group ROC curve; B Validation group ROC curve. The X-axis indicates specificity, and the Y-axis indicates sensitivity

Fig. 4

A Training group calibration curve; B Validation group calibration curve. The horizontal axis represents the predicted probability of cognitive impairment, while the vertical axis represents the actual probability of cognitive impairment. The diagonal dashed line represents the perfect state of the ideal model. The solid line represents the nomogram’s performance; when it is closer to the dashed line, it indicates better prediction accuracy

Fig. 5

A Training group decision curve; B Validation group decision curve. The horizontal axis represents the threshold probability, while the vertical axis represents the net benefit. The black horizontal line indicates the prediction that all patients have no cognitive impairment, whereas the grey line indicates that all patients are predicted to have cognitive impairment. The red curve represents the net benefit of the nomogram