Analysis on predictive value of ultrasound features in breast cancer screening

Objective To describe ultrasonic features of breast masses and explore the predictive value of ultrasonic features in breast cancer screening.

Methods From January 2023 to December 2023, 3811 women who participated in the free breast cancer screening project in Guangdong Province, underwent breast ultrasound examination, and being diagnosed with single breast lump were recruited as research subjects. Data were collected through Guangdong Maternal and Child Information System including their general information, the results of breast ultrasound examination and pathological diagnosis. Logistic regression was used to evaluate the association between ultrasound image characteristics and breast cancer, and then nomogram prediction model was constructed by R software.

Results There were significant differences in age, menarche age, education level, reaching menopause and breastfeeding or not between women with or without breast cancer (P < 0.001). The contrast of ultrasound mass features showed that there were significant differences between women with and without breast cancer in terms of mass size, shape, direction, echo behind the edge, calcifying or not and blood flow signals (P < 0.001). There were significant differences in the size and shape of ultrasound mass features, between ductal carcinoma in situ and invasive carcinoma of breast (P < 0.05). Logistic regression analysis showed that tumor size > 2 cm (OR = 2.423, 95% CI: 1.838 ~ 3.181), irregular shape (OR = 2.361, 95% CI: 1.827 ~ 3.046), unclear edge (OR = 2.142, 95% CI: 1.609 ~ 2.840), calcification (OR = 1.676, 95% CI: 1.260 ~ 2.216) and blood flow signal (OR = 2.305, 95% CI: 1.717 ~ 3.077) were independent predictors of breast cancer. The sensitivity and specificity of the nomogram prediction model was 69.15% and 77.85%, respectively, and the Area Under the Curve (AUC) was 0.796 (95% CI: 0.770 ~ 0.823).

Conclusion The ultrasonic characteristics of breast mass size, shape, edge, calcification and blood flow signal have great potential to predict breast cancer, which provide evidence for breast cancer screening, diagnosis and treatment.