Value of Serum Tumor Markers Combined with Color Doppler Ultrasound in Diagnosis and Treatment of Breast Cancer
Breast cancer is the most common malignant tumor among women in the world, and its prognosis is closely related to the diagnosis and staging. Early diagnosis is very important for reducing mortality and improving prognosis. However, the existing diagnostic techniques, such as mammography, MRI, ultrasound and single serum tumor markers, have limitations. The purpose of this study is to explore the value of serum tumor markers (CA15-3, CEA, CA125) combined with color Doppler ultrasound in early diagnosis, pathological characteristics evaluation and chemotherapy efficacy prediction of breast cancer. The study included 450 subjects, including breast cancer patients, patients with benign lesions and healthy controls. The results show that the diagnostic efficiency of the combined detection model is significantly better than that of the single detection method, and its AUC value is increased to 0.742, and the sensitivity and specificity are significantly improved. The combined indexes are closely related to TNM staging of breast cancer, and the mean RI, CA15-3 level and CEA positive rate increase significantly with the progress of the disease. In addition, dynamic monitoring of serum markers and ultrasound parameters can effectively predict chemotherapy response and prognosis, and the combined model shows good application value in chemotherapy efficacy evaluation and prognosis stratification. In the subgroup of dense breast and young patients, the diagnostic sensitivity and specificity of the combined detection model are further improved. By integrating morphological, hemodynamic and molecular marker information, serum tumor markers combined with color Doppler ultrasound can significantly improve the diagnostic accuracy, the ability to evaluate pathological characteristics and the predictive value of chemotherapy efficacy of breast cancer, which provides strong support for early diagnosis and accurate treatment of breast cancer.
Publication Details
- Journal
- MEDS Basic Medicine
- Published
- 2025-01-01
- DOI
- https://doi.org/10.23977/medbm.2025.030119
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00