Predicting the risk of chemotherapy-induced myelosuppression in clinical practice: an interpretable machine learning approach based on routine laboratory data
- 1Department of Pharmacy, Northeast Yunnan Central Hospital
- 2Department of Pharmacy, Yiliang County People’s Hospital
Abstract
Objective: 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.
Methods: 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.
Results: 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 <5×10 ⁹ /L, mean hemoglobin concentration <325 g/L, monocyte percentage (MONO%) >10%, red blood cell count <4×10 ¹² /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.
Conclusion: 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.
Related
- Efficacy and Safety of Trilaciclib for the Prevention of Chemotherapy-Induced Myelosuppression in Patients With Solid Tumors: A Systematic Review and Meta-Analysis.
- Predicting lymph node metastasis in non-small cell lung cancer using clinical and PET/CT-derived morphological features: An interpretable machine-learning study
- Interpretable machine learning for predicting intraoperative oxygenation impairment during one-lung ventilation in thoracic surgery: a retrospective cohort study
- Predicting delirium in critically Ill patients with cancer using interpretable machine learning
- Interpretable deep learning and radiomics for predicting the occurrence of brain metastases following prophylactic brain irradiation in small cell lung cancer
BibTeX
@article{fan2026predicting,
title = {{Predicting the risk of chemotherapy-induced myelosuppression in clinical practice: an interpretable machine learning approach based on routine laboratory data}},
author = {Hao Fan and Xuejiao Wu and Zhenyu Cheng and Xiubai Tang and Hui Huang and Keyan Chen},
journal = {Frontiers in Oncology},
year = {2026},
month = oct,
doi = {10.3389/fonc.2026.1780678},
url = {https://www.frontiersin.org/articles/10.3389/fonc.2026.1780678/full},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Predicting the risk of chemotherapy-induced myelosuppression in clinical practice: an interpretable machine learning approach based on routine laboratory data
AU - Hao Fan
AU - Xuejiao Wu
AU - Zhenyu Cheng
AU - Xiubai Tang
AU - Hui Huang
AU - Keyan Chen
PY - 2026
DA - 2026/10/14
JO - Frontiers in Oncology
DO - 10.3389/fonc.2026.1780678
UR - https://www.frontiersin.org/articles/10.3389/fonc.2026.1780678/full
AB - Objective: 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. Methods: 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. Results: 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 <5×10 ⁹ /L, mean hemoglobin concentration <325 g/L, monocyte percentage (MONO%) >10%, red blood cell count <4×10 ¹² /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. Conclusion: 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.
SN - 2234-943X
ER -