2024•Research SquareOpen access

Comparison of ultrasound features and establishment of a predictive nomogram for triple-negative and non-triple-negative breast cancer

Liyang Su, Qiaojie Xie, Jiaohong Chen, Qingquan Zhang, Nian Li, Chuntian Hong

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Abstract

Abstract Objective: The objective of this study was to compare ultrasound features and establish a predictive nomogram for distinguishing between triple-negative breast cancer (TNBC) and non-triple-negative breast cancer (non-TNBC). Materials and Methods: The study included a total of 205 patients with confirmed TNBC and 574 patients with non-TNBC, randomly divided into a training set and a validation set at a ratio of 7:3. All patients underwent ultrasound examination and received a confirmatory pathological diagnosis. Nodules were classified according to the Breast Imaging-Reporting and Data System (BI-RADS) standard. Subsequently, the study conducted a comparative analysis of clinical characteristics and ultrasonic features. Results: A statistically significant difference was observed in multiple clinical and ultrasonic features between TNBC and non-TNBC. Specifically, in the logistic regression analysis conducted on the training set, indicators such as posterior echo, lesion size, presence of clinical symptoms, margin characteristics, internal blood flow signals, halo, and microcalcification were found to be statistically significant (P<0.05). These significant indicators were then effectively incorporated into a static and dynamic nomogram model, demonstrating high predictive performance in distinguishing TNBC from non-TNBC. Conclusion: The results of our study demonstrated that ultrasound features can be valuable in distinguishing between TNBC and non-TNBC. The presence of posterior echo, size, clinical symptoms, margin, internal flow, halo and microcalcification were identified as predictive factors for this differentiation. Microcalcification, hyperechoic halo, internal flow, and clinical symptoms emerged as the strongest predictive factors, indicating their potential as reliable indicators for identifying TNBC and non-TNBC.

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Abstract Objective: The objective of this study was to compare ultrasound features and establish a predictive nomogram for distinguishing between triple-negative breast cancer (TNBC) and non-triple-negative breast cancer (non-TNBC). Materials and Methods: The study included a total of 205 patients with confirmed TNBC and 574 patients with non-TNBC, randomly divided into a training set and a validation set at a ratio of 7:3. All patients underwent ultrasound examination and received a confirmatory pathological diagnosis. Nodules were classified according to the Breast Imaging-Reporting and Data System (BI-RADS) standard. Subsequently, the study conducted a comparative analysis of clinical characteristics and ultrasonic features. Results: A statistically significant difference was observed in multiple clinical and ultrasonic features between TNBC and non-TNBC. Specifically, in the logistic regression analysis conducted on the training set, indicators such as posterior echo, lesion size, presence of clinical symptoms, margin characteristics, internal blood flow signals, halo, and microcalcification were found to be statistically significant (P<0.05). These significant indicators were then effectively incorporated into a static and dynamic nomogram model, demonstrating high predictive performance in distinguishing TNBC from non-TNBC. Conclusion: The results of our study demonstrated that ultrasound features can be valuable in distinguishing between TNBC and non-TNBC. The presence of posterior echo, size, clinical symptoms, margin, internal flow, halo and microcalcification were identified as predictive factors for this differentiation. Microcalcification, hyperechoic halo, internal flow, and clinical symptoms emerged as the strongest predictive factors, indicating their potential as reliable indicators for identifying TNBC and non-TNBC.

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Available abstract

Abstract Objective: The objective of this study was to compare ultrasound features and establish a predictive nomogram for distinguishing between triple-negative breast cancer (TNBC) and non-triple-negative breast cancer (non-TNBC). Materials and Methods: The study included a total of 205 patients with confirmed TNBC and 574 patients with non-TNBC, randomly divided into a training set and a validation set at a ratio of 7:3. All patients underwent ultrasound examination and received a confirmatory pathological diagnosis. Nodules were classified according to the Breast Imaging-Reporting and Data System (BI-RADS) standard. Subsequently, the study conducted a comparative analysis of clinical characteristics and ultrasonic features. Results: A statistically significant difference was observed in multiple clinical and ultrasonic features between TNBC and non-TNBC. Specifically, in the logistic regression analysis conducted on the training set, indicators such as posterior echo, lesion size, presence of clinical symptoms, margin characteristics, internal blood flow signals, halo, and microcalcification were found to be statistically significant (P<0.05). These significant indicators were then effectively incorporated into a static and dynamic nomogram model, demonstrating high predictive performance in distinguishing TNBC from non-TNBC. Conclusion: The results of our study demonstrated that ultrasound features can be valuable in distinguishing between TNBC and non-TNBC. The presence of posterior echo, size, clinical symptoms, margin, internal flow, halo and microcalcification were identified as predictive factors for this differentiation. Microcalcification, hyperechoic halo, internal flow, and clinical symptoms emerged as the strongest predictive factors, indicating their potential as reliable indicators for identifying TNBC and non-TNBC.

Key concepts: Triple-negative breast cancer, Medicine, Triple test, Triple negative, Nomogram, Breast cancer, Oncology, Cancer

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Comparison of ultrasound features and establishment of a predictive nomogram for triple-negative and non-triple-negative breast cancer — Research Paper | ScholarLens