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Development of a machine learning-based risk prediction model and analysis of risk factors for docetaxel-induced bone
Hongya Ou1, Zijan Tan1, Anle Shen1,2
1Hainan Branch, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Sanya, China.
Introduction:
Breast cancer is the most prevalent malignancy in women worldwide. Docetaxel-based chemotherapy is commonly used for treatment, but its clinical application is often constrained by hematologic toxicity, particularly severe bone marrow suppression. The early identification of high-risk patients is essential to prevent complications and optimize therapeutic outcomes. Machine learning offers advanced capabilities for risk prediction by capturing complex patterns beyond those of traditional statistical models.
Aim:
This study aimed to identify the risk factors associated with bone marrow suppression in breast cancer patients receiving docetaxel-based chemotherapy, and to develop and validate predictive models using machine learning algorithms.
Method:
This retrospective case-control study included 119 patients with breast cancer treated with docetaxel-based chemotherapy at the Hainan Hospital of Shanghai Children's Medical Center, Shanghai Jiao Tong University School of Medicine, from January 2020 to December 2024. Patients were divided into bone marrow suppression (n = 57; WHO grades II-IV) and non-suppression (n = 62; grades 0-I) groups based on WHO toxicity criteria. Multivariate logistic regression was used to identify independent risk factors. Three prediction models, logistic regression, random forest, and AdaBoost, were constructed and evaluated. A five-fold cross-validation with 50 repetitions was performed to ensure model stability and reliability.
Results:
Multivariate analysis revealed that a low baseline lymphocyte count (OR = 4.95, 95% CI 1.62-17.0), decreased white blood cell (WBC) count (OR = 0.62, 95% CI 0.40-0.9), and reduced prealbumin level (OR = 0.98, 95% CI 0.97-0.99) were significantly associated with severe bone marrow suppression (all p < 0.05). Among the models, AdaBoost achieved the best overall performance (AUC = 0.81; specificity = 94%; accuracy = 77%). The random forest model showed the highest sensitivity (83%), while logistic regression was more interpretable but demonstrated a lower predictive ability (AUC = 0.69).
Conclusion:
Pretreatment lymphocyte count, WBC count, and prealbumin level are reliable predictors of docetaxel-induced bone marrow suppression. The AdaBoost model demonstrates high specificity (94%) in identifying low-risk patients, supporting accurate risk stratification and personalized care in breast cancer treatment.
