Development and internal validation of a conventional ultrasound-based nomogram for predicting malignant nonmasslike breast lesions

Quantitative Imaging in Medicine and Surgery

article

Xian Lin¹  Shulian Zhuang¹  Shuang Yang¹  Danhui Lai¹  Miao Chen¹  Jianxing Zhang¹ 

[1] Department of Ultrasound , The Second Affiliated Hospital of Guangzhou University of Chinese Medicine

关键词: Nonmasslike (NML);    breast cancer;    nomogram;    ultrasound (US);    predictive model;   

DOI  :  10.21037/qims-22-378

学科分类：外科医学

来源: AME Publications

【 摘 要 】

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 1st, 2017, to April 17th, 2020, were retrospectively enrolled. Candidate variables included age, clinical symptoms, and the image features obtained from the conventional US. Nomograms were developed based on the results of the multiple logistic regression analysis via R language. One thousand bootstraps were used for internal validation. The area under the curve (AUC) and the bias-corrected concordance index (C-index) were calculated. Decision curve analysis (DCA) was also performed for further comparison between the nomogram and the Breast Imaging Reporting and Data System (BI-RADS). The study has not yet been registered. 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.

【 授权许可】

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