ArticleScientific reports2023
A ultrasonic nomogram of quantitative parameters for diagnosing breast cancer.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Clinical evaluation of a nomogram model incorporating multimodal ultrasound features for breast cancer diagnosis.Gland surgery · 2026Article
- Prediction model for assessing HER2 status patient with invasive ductal carcinoma based on clinical parameters and ultrasound features: a dual-center study.BMC women's health · 2025Article
- Development and validation of a screening model for benign and malignant breast masses based on S-Detect and microvascular flow imaging.Gland surgery · 2025Article
- Development of a nomogram for predicting malignancy in BI-RADS 4 breast lesions using contrast-enhanced ultrasound and shear wave elastography parameters.Scientific reports · 2025Article
- A nomogram with Nottingham prognostic index for predicting locoregional recurrence in breast cancer patients.Frontiers in oncology · 2024Article
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study aimed to develop a nomogram through the collection of quantitative ultrasound parameters to predict breast cancer. From March 2021 to September 2022, a total of 313 breast tumors were included with pathological results. Through collecting quantitative ultrasound parameters of breast tumors and multivariate regression analysis, a nomogram was developed. The diagnostic performances, calibration and clinical usefulness of the nomogram for predicting breast cancer were assessed. A total of 182 benign and 131 malignant breast tumors were included in this study. The nomogram indicated excellent predictive properties with an AUC of 0.934, sensitivity of 0.881, specificity of 0.848, PPV of 0.795 and NPV of 0.841. The calibration curve showed the predicted values are basically consistent with the actual observed values. The optimum cut-off for the nomogram was 0.310 for predicting cancer. The decision curve analysis results corroborated good clinical usefulness. The model including BI-RADS score, SWE and VI is potentially useful for predicting breast cancer.
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