ArticleTranslational cancer research2025
Construction and validation of a nomogram prediction model for predicting the risk of chemotherapy-induced myelosuppression after chemotherapy in patients with triple-negative breast cancer: a single-center retrospective case-control study.
Article in Translational cancer research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Interpretable machine-learning prediction of severe myelosuppression in colorectal cancer patients receiving chemotherapy using XGBoost and SHAP: a retrospective study with a web-based calculator.Frontiers in oncology · 2026Article
- Development and validation of a machine learning-based predictive model for chemotherapy-induced myelosuppression in colorectal cancer patients.Frontiers in medicine · 2026Article
- Incorporating host genetics and inflammation in myelosuppression risk prediction for triple-negative breast cancer.Translational cancer research · 2025Article
- Response to the letter regarding 'Construction and validation of a nomogram prediction model for predicting the risk of chemotherapy-induced myelosuppression after chemotherapy in patients with triple-negative breast cancer'.Translational cancer research · 2025Article
- Development of a Predictive Model for the Risk of Myelosuppression in Patients With Nasopharyngeal Carcinoma Undergoing Chemoradiotherapy.Clinical Medicine Insights. Oncology · 2025Article
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Abstract
Background: Triple-negative breast cancer (TNBC) has a poor prognosis due to limited targeted treatments. Chemotherapy often causes chemotherapy-induced myelosuppression (CIM), complicating treatment and raising costs, yet predictive tools for this risk are scarce. This study examined the prevalence and risk factors of CIM in TNBC patients after chemotherapy and created nomograms to predict this risk. Methods: Nomograms were developed from a retrospective study of 316 TNBC patients treated at the Anning First People's Hospital Affiliated to Kunming University of Science and Technology between 1 July 2021 and 31 May 2024. The patients were split into development and validation cohorts in an 8:2 ratio. Least absolute shrinkage and selection operator (LASSO) identified risk factors for CIM, which were used to create the nomograms. The models' accuracy, calibration, and clinical utility were evaluated using the area under the curve (AUC), calibration curves, and decision curve analysis (DCA), with validation through bootstrapping. Results: In this study of 316 TNBC patients, 102 experienced CIM, an incidence rate of 32.28%. Patient characteristics were similar across cohorts. The development cohort had a mean age of 52.05 years, with a median hospital stay of 5 days. Myelosuppression of degree I was the most common CIM event. LASSO and logistic regression analyses linked CIM to factors like bone metastasis, platinum regimens, chemotherapy cycles, pre-chemotherapy neutrophil count, and drug combinations. The nomograms showed strong predictive accuracy with AUCs of 0.886 [95% confidence interval (CI): 0.836-0.937] and 0.905 (95% CI: 0.834-0.976) in the development and validation cohorts, respectively, and high agreement in calibration curves. DCA confirmed their clinical utility. Conclusions: This study developed a validated nomogram that accurately predicts the risk of CIM in TNBC patients, helping healthcare providers create personalized treatment plans.
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