Predicting the risk of chemotherapy-induced myelosuppression in clinical practice: an interpretable machine learning approach based on routine laboratory data

Chemotherapy-induced myelosuppression (CIM) is the most common adverse reaction of tumor chemotherapy and may lead to treatment delay, dose reduction, life-threatening infection, and poor prognosis. Current predictive methods remain suboptimal, with insufficient interpretability and limited clinical application. This study aimed to develop and validate an interpretable machine learning model for early CIM risk prediction using routine laboratory data.

A total of 1,000 cancer patients from Northeast Yunnan Central Hospital between January 2024 and January 2025 were enrolled and divided into a case group (n=500, with myelosuppression) and a control group (n=500, without myelosuppression).All observed samples were randomly split into a training set (70%) and a validation set (30%). The Least Absolute Shrinkage and Selection Operator (LASSO) was used to select optimal variables for model construction. The predictive performance of 15 different ML models was evaluated using sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), F1-score, Youden's index, area under the receiver operating characteristic curve (AUC), decision curve analysis (DCA), and residual boxplots. The best-performing model was selected for SHAP-based interpretability analysis and internally validated using a confusion matrix, clinical impact curve, and calibration curve.Additionally, 488 patients treated at Northeast Yunnan Provincial Central Hospital between June 2025 and November 2025 were selected as an independent external validation cohort for external validation.

After screening, 15 clinical indicators were used for ML model development. All models demonstrated excellent discrimination. The LightGBM model showed the best overall performance (AUC: 0.999 [0.998-1.000]; sensitivity: 0.986; specificity: 0.989; accuracy: 0.987; PPV: 0.989; NPV: 0.986; F1-score: 0.987; Youden's index: 0.974). According to the LightGBM model, CIM risk increased when the following feature thresholds were met: white blood cell count <5×10⁹/L, mean hemoglobin concentration <325 g/L, monocyte percentage (MONO%) >10%, red blood cell count <4×10¹²/L, mean platelet volume <8 fL, and plateletcrit <0.1%.In the independent external validation (n=488), the LightGBM model remained stable and reliable, with an AUC of 0.940 (95% CI: 0.920-0.959), sensitivity of 0.818, specificity of 0.888, accuracy of 85.2%, and favorable calibration and clinical net benefit.

The LightGBM prediction model developed in this study can enable early identification of high-risk patients, providing a reference for healthcare providers to implement early interventions.