ArticleGland surgery2023
Development and validation of a prediction model for the diagnosis of breast cancer based on clinical and ultrasonic features.
Article in Gland surgery, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 2 citations in OpenAlex.
- Development and validation of a screening model for benign and malignant breast masses based on S-Detect and microvascular flow imaging.Gland surgery · 2025Article
- A diagnostic test of two-dimensional ultrasonic feature extraction based on artificial intelligence combined with blood flow Adler classification and contrast-enhanced ultrasound for predictingTranslational cancer research · 2025Article
- Construction of a nomogram prediction model for the pathological complete response after neoadjuvant chemotherapy in breast cancer: a study based on ultrasound and clinicopathological features.Frontiers in oncology · 2025Article
- Integrating the incident angle of main vessel of breast nodule with BI-RADS can improve the efficacy of breast malignancy evaluation.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The aim of this study was to develop a simple and effective prediction model for calculating the probability of breast cancer by selecting clinical and sonographic features associated with breast cancer. Methods: A total of 402 lesions from 304 adult females from the ultrasound department of of PLA General Hospital from March 1 Results: One hundred and seventy-nine malignant lesions and 223 benign lesions were included in the development group after exclusion and follow-up, whereas 62 malignant lesions and 59 benign lesions were enrolled in the validation group. Age, bloody nipple discharge, irregular shape, irregular border, heterogeneous echo, microcalcification, attenuation effects, decreased echo in surrounding tissues, lesions in ducts, abnormal lymph node morphology, nourishing vessel and nourishing vessel's resistance index (RI) greater than 0.70 were selected as independent risk factors. There was no significant difference in the area under the curve (AUC) of the development group between the prediction model and the BI-RADS category (0.959 Conclusions: A prediction model was developed with the clinical and ultrasonic features for the precise and intuitive probability of breast cancer. This could provide a reliable reference for further examination.
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Registered trials
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