ArticleQuantitative imaging in medicine and surgery2022
Development and internal validation of a conventional ultrasound-based nomogram for predicting malignant nonmasslike breast lesions.
Article in Quantitative imaging in medicine and surgery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 8 citations in OpenAlex.
- A nomogram based on conventional ultrasound and elastography for diagnosing BI-RADS category 3-5 lesions.BMC medical imaging · 2026Article
- Random forest with preoperative core biopsy categories: a novel method for refining ultrasonic Breast Imaging Reporting and Data System evaluation.Quantitative imaging in medicine and surgery · 2025Article
- Nomogram based on multimodal ultrasound features for evaluating breast nonmass lesions: a single center study.BMC medical imaging · 2024Article
- Development and Validation of a Nomogram for Predicting Breast Malignancy in Male Patients Based on Clinical and Ultrasound Features.Current radiopharmaceuticals · 2024Article
- Development and validation of a prediction model for the diagnosis of breast cancer based on clinical and ultrasonic features.Gland surgery · 2023Article
- Ultrasound-based prediction of preoperative core biopsy categories in solid breast tumor using machine learning.Quantitative imaging in medicine and surgery · 2023Article
- A comparative study on the features of breast sclerosing adenosis and invasive ductal carcinoma via ultrasound and establishment of a predictive nomogram.Frontiers in oncology · 2023Article
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The aim of this study was to develop a conventional ultrasound (US) features-based nomogram for the prediction of malignant nonmasslike (NML) breast lesions. Methods: Consecutive cases of adult females diagnosed with NML breast lesions via US screening in our center from June 1 Results: A total of 229 patients were included in the study after exclusion and follow-up. The overall malignant rate of NML breast lesions was 31.0%. Age, clinical symptoms, echo pattern, calcification, orientation, and Adler's classification were selected to generate the nomogram according to the results of the multivariable logistic regression analysis. The bias-corrected C-index and the AUC of our nomogram were 0.790 and 0.828, respectively. The DCA showed that our model had larger net benefits in a range from 0.2 to 0.7 when compared with the BI-RADS. Conclusions: We developed a prediction model using a combination of age, clinical symptoms, echo pattern, calcification, orientation, and Adler's classification for malignant NML breast lesion prediction that yielded adequate discrimination and calibration.
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