Integrating radiomics into predictive models for low nuclear grade DCIS using machine learning
Abstract Predicting low nuclear grade DCIS before surgery can improve treatment choices and patient care, thereby reducing unnecessary treatment. Due to the high heterogeneity of DCIS and the limitations of biopsies in fully characterizing tumors, current diagnostic methods relying on invasive biops...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-03-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-92080-y |
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| author | Yimin Wu Daojing Xu Zongyu Zha Li Gu Jieqing Chen Jiagui Fang Ziyang Dou Pingyang Zhang Chaoxue Zhang Junli Wang |
| author_facet | Yimin Wu Daojing Xu Zongyu Zha Li Gu Jieqing Chen Jiagui Fang Ziyang Dou Pingyang Zhang Chaoxue Zhang Junli Wang |
| author_sort | Yimin Wu |
| collection | DOAJ |
| description | Abstract Predicting low nuclear grade DCIS before surgery can improve treatment choices and patient care, thereby reducing unnecessary treatment. Due to the high heterogeneity of DCIS and the limitations of biopsies in fully characterizing tumors, current diagnostic methods relying on invasive biopsies face challenges. Here, we developed an ensemble machine learning model to assist in the preoperative diagnosis of low nuclear grade DCIS. We integrated preoperative clinical data, ultrasound images, mammography images, and Radiomic scores from 241 DCIS cases. The ensemble model, based on Elastic Net, Generalized Linear Models with Boosting (glmboost), and Ranger, improved the ability to predict low nuclear grade DCIS preoperatively, achieving an AUC of 0.92 on the validation set, outperforming the model using clinical data alone. The comprehensive model also demonstrated notable enhancements in integrated discrimination improvement and net reclassification improvement (p < 0.001). Furthermore, the Radiomic ensemble model effectively stratified DCIS patients by risk based on disease-free survival. Our findings emphasize the importance of integrating Radiomic into DCIS prediction models, offering fresh perspectives for personalized treatment and clinical management of DCIS. |
| format | Article |
| id | doaj-art-d65cb13fca1c48ff830508a48e3946e3 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-d65cb13fca1c48ff830508a48e3946e32025-08-20T01:57:44ZengNature PortfolioScientific Reports2045-23222025-03-0115111410.1038/s41598-025-92080-yIntegrating radiomics into predictive models for low nuclear grade DCIS using machine learningYimin Wu0Daojing Xu1Zongyu Zha2Li Gu3Jieqing Chen4Jiagui Fang5Ziyang Dou6Pingyang Zhang7Chaoxue Zhang8Junli Wang9Department of Ultrasound, WuHu Hospital, East China Normal University (The Second People’s Hospital, WuHu)Department of Ultrasound, WuHu Hospital, East China Normal University (The Second People’s Hospital, WuHu)Department of Ultrasound, WuHu Hospital, East China Normal University (The Second People’s Hospital, WuHu)Department of Ultrasound, WuHu Hospital, East China Normal University (The Second People’s Hospital, WuHu)Department of Ultrasound, WuHu Hospital, East China Normal University (The Second People’s Hospital, WuHu)Department of Ultrasound, WuHu Hospital, East China Normal University (The Second People’s Hospital, WuHu)Department of Ultrasound, WuHu Hospital, East China Normal University (The Second People’s Hospital, WuHu)Department of Echocardiography, Nanjing First Hospital, Nanjing Medical UniversityDepartment of Ultrasound, The First Affiliated Hospital of Anhui Medical UniversityDepartment of Ultrasound, WuHu Hospital, East China Normal University (The Second People’s Hospital, WuHu)Abstract Predicting low nuclear grade DCIS before surgery can improve treatment choices and patient care, thereby reducing unnecessary treatment. Due to the high heterogeneity of DCIS and the limitations of biopsies in fully characterizing tumors, current diagnostic methods relying on invasive biopsies face challenges. Here, we developed an ensemble machine learning model to assist in the preoperative diagnosis of low nuclear grade DCIS. We integrated preoperative clinical data, ultrasound images, mammography images, and Radiomic scores from 241 DCIS cases. The ensemble model, based on Elastic Net, Generalized Linear Models with Boosting (glmboost), and Ranger, improved the ability to predict low nuclear grade DCIS preoperatively, achieving an AUC of 0.92 on the validation set, outperforming the model using clinical data alone. The comprehensive model also demonstrated notable enhancements in integrated discrimination improvement and net reclassification improvement (p < 0.001). Furthermore, the Radiomic ensemble model effectively stratified DCIS patients by risk based on disease-free survival. Our findings emphasize the importance of integrating Radiomic into DCIS prediction models, offering fresh perspectives for personalized treatment and clinical management of DCIS.https://doi.org/10.1038/s41598-025-92080-yDuctal carcinoma in situMachine learningRadiomicUltrasoundMammography |
| spellingShingle | Yimin Wu Daojing Xu Zongyu Zha Li Gu Jieqing Chen Jiagui Fang Ziyang Dou Pingyang Zhang Chaoxue Zhang Junli Wang Integrating radiomics into predictive models for low nuclear grade DCIS using machine learning Scientific Reports Ductal carcinoma in situ Machine learning Radiomic Ultrasound Mammography |
| title | Integrating radiomics into predictive models for low nuclear grade DCIS using machine learning |
| title_full | Integrating radiomics into predictive models for low nuclear grade DCIS using machine learning |
| title_fullStr | Integrating radiomics into predictive models for low nuclear grade DCIS using machine learning |
| title_full_unstemmed | Integrating radiomics into predictive models for low nuclear grade DCIS using machine learning |
| title_short | Integrating radiomics into predictive models for low nuclear grade DCIS using machine learning |
| title_sort | integrating radiomics into predictive models for low nuclear grade dcis using machine learning |
| topic | Ductal carcinoma in situ Machine learning Radiomic Ultrasound Mammography |
| url | https://doi.org/10.1038/s41598-025-92080-y |
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