Yijie Chen1, Lina Tang1, Zhongshi Du1, Zhaoming Zhong1, Jun Luo2, Lichun Yang3, Ruoxia Shen3, Yan Cheng4, Zizhen Zhang4, Ehui Han5, Zhihong Lv5, Lijun Yuan6, Yong Yang6, Yinrong Cheng7, Lei Yang7, Shengli Wang8, Baoyan Bai8, Qin Chen2. 1. Department of Ultrasound, Fujian Cancer Hospital & Fujian Medical University Cancer Hospital, Fuzhou 350014, China. 2. Department of Ultrasound, Sichuan Provincial People's Hospital, Chengdu 610072, China. 3. Department of Ultrasound, the Third Affiliated Hospital of Kunming Medical University & Yunnan Cancer Hospital, Kunming 650118, China. 4. Department of Ultrasound, Qujing City First People's Hospital, Qujing 655000, China. 5. Department of Ultrasound, Huangshi Central Hospital, Affiliated Hospital of Hubei Polytechnic University, Edong Healthcare Group, Huangshi 435000, China. 6. Departments of Ultrasound, Tangdu Hospital, Fourth Military Medical University, Xi'an 710032, China. 7. Department of Ultrasound, Chengdu First People's Hospital, Chengdu 610000, China. 8. Department of Ultrasound, Yanan University Affiliated Hospital, Yan'an 716000, China.
Abstract
BACKGROUND: We aimed to investigate the influence of patient and lesion characteristics on our diagnostic model for contrast-enhanced ultrasound (CEUS) of the breast, comparing its accuracy with that of histopathology. METHODS: Conducting a study with eight medical centers, we compared 1,023 breast lesions categorized as BI-RADS 4 or 5 with the score from our newly-established CEUS-based diagnostic model, comparing the results with pathological outcomes. Univariate and multivariate logistic regression analyses were conducted to determine the influence of clinicopathological characteristics on the performance of this CEUS model. RESULTS: Logistic regression analysis showed that patients' age, maximum lesion diameter, and distance from the lesion's deep edge to the pectoralis major were significant independent influencing factors. The model's diagnostic accuracy was greater for patients >35 y (P=0.005), for maximum lesion diameter >20 mm, and for distance from the lesion's deep edge to the pectoralis major ≤3.05 mm. There was no significant difference in accuracy between lesions with maximum lesion diameter 10-20 and <10 mm (P=0.393). CONCLUSIONS: The diagnostic performance of the proposed CEUS model for breast lesions is influenced by patients' age, maximum lesion diameter, and distance from the lesion's deep edge to the pectoralis major. Consideration of influencing factors is required to optimize clinical use of the CEUS model. 2019 Annals of Translational Medicine. All rights reserved.
BACKGROUND: We aimed to investigate the influence of patient and lesion characteristics on our diagnostic model for contrast-enhanced ultrasound (CEUS) of the breast, comparing its accuracy with that of histopathology. METHODS: Conducting a study with eight medical centers, we compared 1,023 breast lesions categorized as BI-RADS 4 or 5 with the score from our newly-established CEUS-based diagnostic model, comparing the results with pathological outcomes. Univariate and multivariate logistic regression analyses were conducted to determine the influence of clinicopathological characteristics on the performance of this CEUS model. RESULTS: Logistic regression analysis showed that patients' age, maximum lesion diameter, and distance from the lesion's deep edge to the pectoralis major were significant independent influencing factors. The model's diagnostic accuracy was greater for patients >35 y (P=0.005), for maximum lesion diameter >20 mm, and for distance from the lesion's deep edge to the pectoralis major ≤3.05 mm. There was no significant difference in accuracy between lesions with maximum lesion diameter 10-20 and <10 mm (P=0.393). CONCLUSIONS: The diagnostic performance of the proposed CEUS model for breast lesions is influenced by patients' age, maximum lesion diameter, and distance from the lesion's deep edge to the pectoralis major. Consideration of influencing factors is required to optimize clinical use of the CEUS model. 2019 Annals of Translational Medicine. All rights reserved.
Entities:
Keywords:
Breast cancer; contrast-enhanced ultrasound; ultrasonography
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