Application of Ultrasound Radiomics in Differentiating Benign from Malignant Breast Nodules in Women with Post-Silicone Breast Augmentation
Purpose: To evaluate the diagnostic value of ultrasound radiomics in distinguishing between benign and malignant breast nodules in women who have undergone silicone breast augmentation. Methods: A retrospective study was conducted of 99 breast nodules detected by ultrasound in 93 women who had under...
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MDPI AG
2025-01-01
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| Series: | Current Oncology |
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| author | Ling Hao Yang Chen Xuejiao Su Buyun Ma |
| author_facet | Ling Hao Yang Chen Xuejiao Su Buyun Ma |
| author_sort | Ling Hao |
| collection | DOAJ |
| description | Purpose: To evaluate the diagnostic value of ultrasound radiomics in distinguishing between benign and malignant breast nodules in women who have undergone silicone breast augmentation. Methods: A retrospective study was conducted of 99 breast nodules detected by ultrasound in 93 women who had undergone silicone breast augmentation. The ultrasound data were collected between 1 January 2006 and 1 September 2023. The nodules were allocated into a training set (<i>n</i> = 69) and a validation set (<i>n</i> = 30). Regions of interest (ROIs) were manually delineated using 3D Slicer software, and radiomic features were extracted and selected using Python programming. Eight machine learning algorithms were applied to build predictive models, and their performance was assessed using sensitivity, specificity, area under the ROC curve (AUC), accuracy, Brier score, and log loss. Model performance was further evaluated using ROC curves and calibration curves, while clinical utility was assessed via decision curve analysis (DCA). Results: The random forest model exhibited superior performance in differentiating benign from malignant nodules in the validation set, achieving sensitivity of 0.765, specificity of 0.838, and an AUC of 0.787 (95% CI: 0.561–0.960). The model’s accuracy, Brier score, and log loss were 0.796, 0.197, and 0.599, respectively. DCA suggested potential clinical utility of the model. Conclusion: Ultrasound radiomics demonstrates promising diagnostic accuracy in differentiating benign from malignant breast nodules in women with silicone breast prostheses. This approach has the potential to serve as an additional diagnostic tool for patients following silicone breast augmentation. |
| format | Article |
| id | doaj-art-a469203924644d9f86b1e97126547f02 |
| institution | Kabale University |
| issn | 1198-0052 1718-7729 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| series | Current Oncology |
| spelling | doaj-art-a469203924644d9f86b1e97126547f022025-01-24T13:28:24ZengMDPI AGCurrent Oncology1198-00521718-77292025-01-013212910.3390/curroncol32010029Application of Ultrasound Radiomics in Differentiating Benign from Malignant Breast Nodules in Women with Post-Silicone Breast AugmentationLing Hao0Yang Chen1Xuejiao Su2Buyun Ma3Department of Medical Ultrasound, West China Hospital, Sichuan University, Chengdu 610041, ChinaDepartment of Medical Ultrasound, West China Hospital, Sichuan University, Chengdu 610041, ChinaDepartment of Medical Ultrasound, West China Hospital, Sichuan University, Chengdu 610041, ChinaDepartment of Medical Ultrasound, West China Hospital, Sichuan University, Chengdu 610041, ChinaPurpose: To evaluate the diagnostic value of ultrasound radiomics in distinguishing between benign and malignant breast nodules in women who have undergone silicone breast augmentation. Methods: A retrospective study was conducted of 99 breast nodules detected by ultrasound in 93 women who had undergone silicone breast augmentation. The ultrasound data were collected between 1 January 2006 and 1 September 2023. The nodules were allocated into a training set (<i>n</i> = 69) and a validation set (<i>n</i> = 30). Regions of interest (ROIs) were manually delineated using 3D Slicer software, and radiomic features were extracted and selected using Python programming. Eight machine learning algorithms were applied to build predictive models, and their performance was assessed using sensitivity, specificity, area under the ROC curve (AUC), accuracy, Brier score, and log loss. Model performance was further evaluated using ROC curves and calibration curves, while clinical utility was assessed via decision curve analysis (DCA). Results: The random forest model exhibited superior performance in differentiating benign from malignant nodules in the validation set, achieving sensitivity of 0.765, specificity of 0.838, and an AUC of 0.787 (95% CI: 0.561–0.960). The model’s accuracy, Brier score, and log loss were 0.796, 0.197, and 0.599, respectively. DCA suggested potential clinical utility of the model. Conclusion: Ultrasound radiomics demonstrates promising diagnostic accuracy in differentiating benign from malignant breast nodules in women with silicone breast prostheses. This approach has the potential to serve as an additional diagnostic tool for patients following silicone breast augmentation.https://www.mdpi.com/1718-7729/32/1/29ultrasound radiomicsbreast nodulebenign and malignantsilicone breast augmentationmachine learning |
| spellingShingle | Ling Hao Yang Chen Xuejiao Su Buyun Ma Application of Ultrasound Radiomics in Differentiating Benign from Malignant Breast Nodules in Women with Post-Silicone Breast Augmentation Current Oncology ultrasound radiomics breast nodule benign and malignant silicone breast augmentation machine learning |
| title | Application of Ultrasound Radiomics in Differentiating Benign from Malignant Breast Nodules in Women with Post-Silicone Breast Augmentation |
| title_full | Application of Ultrasound Radiomics in Differentiating Benign from Malignant Breast Nodules in Women with Post-Silicone Breast Augmentation |
| title_fullStr | Application of Ultrasound Radiomics in Differentiating Benign from Malignant Breast Nodules in Women with Post-Silicone Breast Augmentation |
| title_full_unstemmed | Application of Ultrasound Radiomics in Differentiating Benign from Malignant Breast Nodules in Women with Post-Silicone Breast Augmentation |
| title_short | Application of Ultrasound Radiomics in Differentiating Benign from Malignant Breast Nodules in Women with Post-Silicone Breast Augmentation |
| title_sort | application of ultrasound radiomics in differentiating benign from malignant breast nodules in women with post silicone breast augmentation |
| topic | ultrasound radiomics breast nodule benign and malignant silicone breast augmentation machine learning |
| url | https://www.mdpi.com/1718-7729/32/1/29 |
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