Yang Liu
ABSTRACT
Objective: To investigate the value of multimodal ultrasound imaging indicators in evaluating the efficacy of neoadjuvant chemotherapy for breast cancer, and to establish a multivariate Logistic regression model for evaluating neoadjuvant chemotherapy for breast cancer. Methods: A retrospective analysis of 38 breast cancer patients who underwent surgery after neoadjuvant chemotherapy was divided into pathologically ineffective (NMHR) and pathologically effective (MHR) according to Miller & Payne grading method. The image characteristics of conventional ultrasound, elastography, and contrast-enhanced ultrasound parameters were compared between the two groups. Univariate and multivariate Logistic analyses were used to analyze factors affecting the efficacy of neoadjuvant chemotherapy. The receiver operating curve (ROC) of each factor was drawn, and the area under the ROC curve and evaluation indicators such as sensitivity and specificity were calculated. Results: After neoadjuvant chemotherapy in the effective group (MHR), the tumor's longest diameter reduction rate (ΔD), morphology, margin, posterior echo attenuation, calcification, resistance index (RI), peak systole velocity (peak systole velocity, PSV), strain ratio (SR), elasticity score, peak intensity (PI) and time to peak (TTP) were the factors affecting the efficacy of neoadjuvant chemotherapy (P<0.05). The factor Logistic regression model showed that the longest tumor diameter reduction rate, PSV, SR, PI, TTP were reliable factors for the efficacy of neoadjuvant chemotherapy in breast cancer. The area under the ROC curve of the multivariate logistic regression model was 0.911, and the sensitivity and specificity were 94.7% and 68.4%, respectively, which had higher accuracy than the single-factor model. Conclusion: The multivariate regression model of multimodal ultrasound imaging technology can evaluate the efficacy of neoadjuvant chemotherapy and improve the diagnostic coincidence rate and efficacy.
- Sung H, Ferlay J, Siegel RL, Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries [J]. CAACancer J Clin, 2021, 71(3): 209-49.Google Scholar
Cross Ref
- "Expert Consensus on Pathological Diagnosis of Neoadjuvant Treatment of Breast Cancer (Edition)" Writing Group. Expert Consensus on Pathological Diagnosis of Neoadjuvant Therapy for Breast Cancer (2020 Edition) [J]. Chinese Journal of Pathology, 2020, 49 (4 ): 296- 304.Google Scholar
- Leng Xiaoling, Ma Fucheng. Ultrasound research progress of breast cancer tumor microenvironment[J]. China Journal of Clinical Medical Imaging, 2015, 26(11): 823-6.Google Scholar
- Zhao Jing, Jing Hui. Application of multimodal ultrasound technology in evaluating the efficacy of neoadjuvant chemotherapy for breast cancer [J]. Modern Oncology, 2020, 28(23): 4181-4184.Google Scholar
- Peng Juan, Deng Qing, Cao Sheng, Jia Yan, Zhou Qing. The value of ultrasound quantitative parameters in early prediction of the effect of neoadjuvant chemotherapy for breast cancer [J]. Chinese Journal of Ultrasound Imaging, 2021,30(06):513-518.Google Scholar
- Le Ting. Diagnostic value of breast contrast-enhanced ultrasound and contrast-enhanced prediction model for precancerous lesions of breast cancer [D]. University of Electronic Science and Technology of China, 2018.Google Scholar
- Shen Yang. The value of conventional ultrasound combined with panoramic technique in evaluating the efficacy of neoadjuvant chemotherapy for breast cancer [D]. Guangxi Medical University, 2017.Google Scholar
- Yang Yuejie. Application analysis of contrast-enhanced ultrasound in the evaluation of the efficacy of neoadjuvant chemotherapy for breast cancer [J]. Shanxi Medicine Journal, 2019, 48(02): 166-167.Google Scholar
- Han Yanfeng, Zou Shuwei, Wang Baohua. The application of ultrasound elastography and contrast-enhanced ultrasound in evaluating the effect of neoadjuvant chemotherapy for breast cancer [J]. China Medical Herald, 2018,15(16):114-117.Google Scholar
- Xiang Bin. Predictive value of contrast-enhanced ultrasound combined with elastography quantitative analysis for responsiveness to neoadjuvant chemotherapy in breast cancer [J]. Contemporary Medicine, 2021, 27(15): 95-98.Google Scholar
- Evaluation model and efficacy of multimodal ultrasound imaging technology to predict the efficacy of neoadjuvant chemotherapy in breast cancer
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