The Prediction Model of High-Frequency Ultrasound Combined with Artificial Intelligence-Assisted Scoring System Improved the Diagnosis of Sclerosing Adenosis and Early Breast Cancer
Bingxin Ma,1 Gang Wu,1 Haohui Zhu,1 Yifei Liu,1 Wenjia Hu,1 Jing Zhao,1 Yinlong Liu,1 Qiuyu Liu2 1Department of Ultrasound, Henan Provincial People’s Hospital, Zhengzhou, 450000, People’s Republic of China; 2Department of Pathology, Henan Provincial People’s Hospital, Zhengzhou, 450000, People’s Rep...
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| Format: | Article |
| Language: | English |
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Dove Medical Press
2025-02-01
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| Series: | Breast Cancer: Targets and Therapy |
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| Online Access: | https://www.dovepress.com/the-prediction-model-of-high-frequency-ultrasound-combined-with-artifi-peer-reviewed-fulltext-article-BCTT |
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| _version_ | 1823861412447911936 |
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| author | Ma B Wu G Zhu H Liu Y Hu W Zhao J Liu Y Liu Q |
| author_facet | Ma B Wu G Zhu H Liu Y Hu W Zhao J Liu Y Liu Q |
| author_sort | Ma B |
| collection | DOAJ |
| description | Bingxin Ma,1 Gang Wu,1 Haohui Zhu,1 Yifei Liu,1 Wenjia Hu,1 Jing Zhao,1 Yinlong Liu,1 Qiuyu Liu2 1Department of Ultrasound, Henan Provincial People’s Hospital, Zhengzhou, 450000, People’s Republic of China; 2Department of Pathology, Henan Provincial People’s Hospital, Zhengzhou, 450000, People’s Republic of ChinaCorrespondence: Gang Wu, Email drwugang325@126.com; Haohui Zhu, Email zhh761126@163.comObjective: The study aimed to apply an artificial intelligence (AI)-assisted scoring system, and improve the diagnostic efficiency of Sclerosing adenosis and early breast cancer.Methods: This study retrospectively collected adenopathy patients (156 cases) and early breast cancer patients (150 cases) in Henan Provincial People’s Hospital from August 2020 to April 2023.Results: The area under the curve of the model constructed by clinical ultrasound features and combined AI features to predict and identify the two in the training group was 0.89 and 0.94, respectively. The combined AI model with the best performance (training AUC, 0.94, 95% CI, 0.91– 0.97 and validation AUC, 0.95, 95% CI, 0.90– 0.99) was superior to the clinical ultrasound feature model, and the decision curve also showed that the clinical ultrasound combined with AI Nomogram had good clinical practicability. In the training group, the AUC of the sonographer and AI in differential diagnosis was 0.67(95% CI, 0.62– 0.71) and 0.89(95% CI, 0.84– 0.93), respectively, and the sonographer’s assessment showed better sensitivity (1.00 VS 0.73), but AI showed a higher accuracy rate (0.66 VS 0.80).Conclusion: Age, lesion size, burr, blood flow, and AI risk score are independent predictors of sclerosing adenosis and early breast cancer. The combined clinical ultrasound feature and AI model are correlated with AI risk score, US routine features, and clinical data, superior to the clinical ultrasound model and BI-RADS grading, and have good diagnostic performance, which can provide clinicians with a more effective diagnostic tool.Keywords: sclerosing adenosis, breast tumor, ultrasound, AI, computer-aided diagnosis |
| format | Article |
| id | doaj-art-7975ed16ac2b4fb8b1abfbc42abb9563 |
| institution | Kabale University |
| issn | 1179-1314 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Dove Medical Press |
| record_format | Article |
| series | Breast Cancer: Targets and Therapy |
| spelling | doaj-art-7975ed16ac2b4fb8b1abfbc42abb95632025-02-09T16:10:20ZengDove Medical PressBreast Cancer: Targets and Therapy1179-13142025-02-01Volume 1714515599989The Prediction Model of High-Frequency Ultrasound Combined with Artificial Intelligence-Assisted Scoring System Improved the Diagnosis of Sclerosing Adenosis and Early Breast CancerMa BWu GZhu HLiu YHu WZhao JLiu YLiu QBingxin Ma,1 Gang Wu,1 Haohui Zhu,1 Yifei Liu,1 Wenjia Hu,1 Jing Zhao,1 Yinlong Liu,1 Qiuyu Liu2 1Department of Ultrasound, Henan Provincial People’s Hospital, Zhengzhou, 450000, People’s Republic of China; 2Department of Pathology, Henan Provincial People’s Hospital, Zhengzhou, 450000, People’s Republic of ChinaCorrespondence: Gang Wu, Email drwugang325@126.com; Haohui Zhu, Email zhh761126@163.comObjective: The study aimed to apply an artificial intelligence (AI)-assisted scoring system, and improve the diagnostic efficiency of Sclerosing adenosis and early breast cancer.Methods: This study retrospectively collected adenopathy patients (156 cases) and early breast cancer patients (150 cases) in Henan Provincial People’s Hospital from August 2020 to April 2023.Results: The area under the curve of the model constructed by clinical ultrasound features and combined AI features to predict and identify the two in the training group was 0.89 and 0.94, respectively. The combined AI model with the best performance (training AUC, 0.94, 95% CI, 0.91– 0.97 and validation AUC, 0.95, 95% CI, 0.90– 0.99) was superior to the clinical ultrasound feature model, and the decision curve also showed that the clinical ultrasound combined with AI Nomogram had good clinical practicability. In the training group, the AUC of the sonographer and AI in differential diagnosis was 0.67(95% CI, 0.62– 0.71) and 0.89(95% CI, 0.84– 0.93), respectively, and the sonographer’s assessment showed better sensitivity (1.00 VS 0.73), but AI showed a higher accuracy rate (0.66 VS 0.80).Conclusion: Age, lesion size, burr, blood flow, and AI risk score are independent predictors of sclerosing adenosis and early breast cancer. The combined clinical ultrasound feature and AI model are correlated with AI risk score, US routine features, and clinical data, superior to the clinical ultrasound model and BI-RADS grading, and have good diagnostic performance, which can provide clinicians with a more effective diagnostic tool.Keywords: sclerosing adenosis, breast tumor, ultrasound, AI, computer-aided diagnosishttps://www.dovepress.com/the-prediction-model-of-high-frequency-ultrasound-combined-with-artifi-peer-reviewed-fulltext-article-BCTTsclerosing adenosisbreast tumorultrasoundaicomputer-aided diagnosis |
| spellingShingle | Ma B Wu G Zhu H Liu Y Hu W Zhao J Liu Y Liu Q The Prediction Model of High-Frequency Ultrasound Combined with Artificial Intelligence-Assisted Scoring System Improved the Diagnosis of Sclerosing Adenosis and Early Breast Cancer Breast Cancer: Targets and Therapy sclerosing adenosis breast tumor ultrasound ai computer-aided diagnosis |
| title | The Prediction Model of High-Frequency Ultrasound Combined with Artificial Intelligence-Assisted Scoring System Improved the Diagnosis of Sclerosing Adenosis and Early Breast Cancer |
| title_full | The Prediction Model of High-Frequency Ultrasound Combined with Artificial Intelligence-Assisted Scoring System Improved the Diagnosis of Sclerosing Adenosis and Early Breast Cancer |
| title_fullStr | The Prediction Model of High-Frequency Ultrasound Combined with Artificial Intelligence-Assisted Scoring System Improved the Diagnosis of Sclerosing Adenosis and Early Breast Cancer |
| title_full_unstemmed | The Prediction Model of High-Frequency Ultrasound Combined with Artificial Intelligence-Assisted Scoring System Improved the Diagnosis of Sclerosing Adenosis and Early Breast Cancer |
| title_short | The Prediction Model of High-Frequency Ultrasound Combined with Artificial Intelligence-Assisted Scoring System Improved the Diagnosis of Sclerosing Adenosis and Early Breast Cancer |
| title_sort | prediction model of high frequency ultrasound combined with artificial intelligence assisted scoring system improved the diagnosis of sclerosing adenosis and early breast cancer |
| topic | sclerosing adenosis breast tumor ultrasound ai computer-aided diagnosis |
| url | https://www.dovepress.com/the-prediction-model-of-high-frequency-ultrasound-combined-with-artifi-peer-reviewed-fulltext-article-BCTT |
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