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.
Haoling Xie, Rong Zhang, Chunmei Wei, Jinsong Xu, Jie Chu, Xuexing Wang
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引用次数: 0
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.
期刊介绍：
Translational Cancer Research (Transl Cancer Res TCR; Print ISSN: 2218-676X; Online ISSN 2219-6803; http://tcr.amegroups.com/) is an Open Access, peer-reviewed journal, indexed in Science Citation Index Expanded (SCIE). TCR publishes laboratory studies of novel therapeutic interventions as well as clinical trials which evaluate new treatment paradigms for cancer; results of novel research investigations which bridge the laboratory and clinical settings including risk assessment, cellular and molecular characterization, prevention, detection, diagnosis and treatment of human cancers with the overall goal of improving the clinical care of cancer patients. The focus of TCR is original, peer-reviewed, science-based research that successfully advances clinical medicine toward the goal of improving patients'' quality of life. The editors and an international advisory group of scientists and clinician-scientists as well as other experts will hold TCR articles to the high-quality standards. We accept Original Articles as well as Review Articles, Editorials and Brief Articles.
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