ArticleBMC medical imaging2021
Predicting the expression level of Ki-67 in breast cancer using multi-modal ultrasound parameters.
Article in BMC medical imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Noninvasive prediction of Ki67 proliferation index in breast cancer based on integrated photoacoustic imaging.BMC medical imaging · 2026Article
- Predicting hormone receptor status in tumors: An innovative approach using breast ultrasound-radiomics combined model.Medicine · 2026Article
- Deep learning model to predict Ki-67 expression of breast cancer using digital breast tomosynthesis.Breast cancer (Tokyo, Japan) · 2026Article
- Integrative multimodal ultrasound and radiomics for early prediction of neoadjuvant therapy response in breast cancer: a clinical study.BMC cancer · 2025Article
- Intratumoral and peritumoral ultrasound radiomics analysis for predicting HER2-low expression in HER2-negative breast cancer patients: a retrospective analysis of dual-central study.Discover oncology · 2025Article
- Multi-center study: ultrasound-based deep learning features for predicting Ki-67 expression in breast cancer.Scientific reports · 2025Article
- Ultrasound elastography: advances and challenges in early detection of breast cancer.Frontiers in oncology · 2025Review
- Multimodal ultrasound: a non-invasive method for identifying dedifferentiation of papillary thyroid carcinoma during active surveillance.Frontiers in oncology · 2025Article
- Correlation of androgen receptor with ultrasound, clinicopathological features and clinical outcomes in breast cancer.Insights into imaging · 2023Article
- Development of an interpretable machine learning model for Ki-67 prediction in breast cancer using intratumoral and peritumoral ultrasound radiomics features.Frontiers in oncology · 2023Article
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Abstract
objectiveThis study investigated the feasibility of predicting the expression levels of Ki-67 in breast cancer using ultrasonographic findings and clinical features.
methodsFifty-eight breast cancer patients, with 82 lesions confirmed by surgical pathology, were selected retrospectively for this study. Conventional ultrasound examination and elastography examination were performed before surgery. Clinical features (age, estrogen receptor (ER), progesterone receptor, and human epidermal growth factor receptor-2 expression levels), ultrasonographic findings, and elastography scores, including the maximum size, location, number, margin, borderline, blood flow, and elastography score of the mass, were collected. The expression of Ki-67 was recorded using immunohistochemical staining, and the patients were divided into a high (≥ 20%) expression group and a low (< 20%) expression group. SPSS 23.0 software was used for statistical analysis. An independent sample t-test was used for measurement data, and a χ
resultsMonofactorial analysis showed that there was a statistically significant difference (p < 0.05) between the high expression of Ki-67 and the maximum diameter of the mass, the margin of the mass, the color Doppler flow imaging of the blood flow, and the resistance index of the blood flow. There were no significant differences in age, mass location, number, morphology, borderline, microcalcification, and elastography score (p > 0.05). Multiple factor regression analysis showed that a large mass and a mass with a rich blood flow had an independent predictive value for Ki-67. When the diameter was > 21.5 mm, the diagnostic sensitivity and specificity were 91.9% and 71.3%, respectively. The expression level of Ki-67 was negatively correlated with that of ER.
conclusionThe tumor size and blood flow of breast cancer is correlated with the expression level of Ki-67 and, thus, it could be used to predict the expression level of Ki-67 in ultrasound diagnosis. The margin condition and the expression level of ER of an ultrasonic mass could also indirectly reflect the Ki-67 expression level of the mass.
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