Abstract
Objective
Breast cancer is the most prevalent cancer and is second leading cause of death from malignancy among women worldwide. In addition to tumor factors, the host characteristics of tumors have been paid more and more attention by the medical community. This study aimed to develop a breast cancer prediction model for the Chinese population using clinical and biochemical characteristics.
Methods
This is a retrospective study. From 2012 to 2021, we selected 19,751 patients with breast diseases from the Guangdong Hospital of Traditional Chinese Medicine, which included 5660 patients with breast cancer and 14,091 patients with benign breast diseases—75% of patients were randomly assigned to the training group and 25% to the test group using a total of 34 clinical and biochemical characteristics. Significant clinical signs were investigated, and logistic regression with recursive feature elimination (RFE) model was used to develop a prediction model for distinguishing benign from malignant breast diseases. The prediction model's accuracy, precision, sensitivity, specificity, and area under the ROC curve (AUC) were calculated.
Results
Clinical statistics demonstrated that the prediction model comprised 19 clinical characteristics had statistical separability in both the training group and the test group, as well as good sensitivity and prediction.
Conclusions
This model based on biochemical parameters demonstrates a significant predictive effect for breast cancer and may be useful as a reference for invasive tissue biopsy in patients undergoing BI-RADS 3 and 4A breast imaging.
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Funding
General Project of National Natural Science Foundation of China, 82274513, based on the connection between central CRH neurons and peripheral autonomic nerves to explore the new mechanism of the pathogenesis of breast cancer "caused by depression" and the research on the role of soothing the liver and relieving depression method, 2023/01–2026/12, under research, presided over by Qianjun Chen.
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Conception and design of the research: QC, WZ. Acquisition of data: LG, QC, JH. Analysis and interpretation of the data: YX, WZ, XL. Statistical analysis: YX, XL, JH. Obtaining financing: QC. Writing of the manuscript: LG. Critical revision of the manuscript for intellectual content: WZ. All authors read and approved the final draft.
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Guo, L., Xie, Y., He, J. et al. Breast cancer prediction model based on clinical and biochemical characteristics: clinical data from patients with benign and malignant breast tumors from a single center in South China. J Cancer Res Clin Oncol 149, 13257–13269 (2023). https://doi.org/10.1007/s00432-023-05181-4
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DOI: https://doi.org/10.1007/s00432-023-05181-4