Super‐Resolution Ultrasound Radiomics Can Predict the Upstaging of Ductal Carcinoma In Situ
ABSTRACT Introduction Preoperatively distinguishing pure ductal carcinoma in situ (DCIS) from upstaged DCIS is important for deciding optimal surgical strategies. However, it is hard to preoperatively predict the upstaging of biopsy‐proven DCIS. This study aims to develop an effective radiomics mode...
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Wiley
2025-08-01
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| Series: | Cancer Medicine |
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| Online Access: | https://doi.org/10.1002/cam4.71155 |
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| author | Liang Yang Xiaoxian Li Zhiyuan Wang Qian Li Juan Fu Xuebin Zou Xia Liang Xu Liu Ruirui Zhang Junjun Chen Hui Xie Yini Huang Jianhua Zhou |
| author_facet | Liang Yang Xiaoxian Li Zhiyuan Wang Qian Li Juan Fu Xuebin Zou Xia Liang Xu Liu Ruirui Zhang Junjun Chen Hui Xie Yini Huang Jianhua Zhou |
| author_sort | Liang Yang |
| collection | DOAJ |
| description | ABSTRACT Introduction Preoperatively distinguishing pure ductal carcinoma in situ (DCIS) from upstaged DCIS is important for deciding optimal surgical strategies. However, it is hard to preoperatively predict the upstaging of biopsy‐proven DCIS. This study aims to develop an effective radiomics model for predicting the upstaging of DCIS based on super‐resolution (SR) ultrasound images. Methods In this multicentre retrospective study, patients with biopsy‐proven DCIS who underwent ultrasound examination were included. A super‐resolution reconstruction algorithm was used to enhance the resolution of original high resolution (HR) ultrasound images and obtain SR images. Pyradiomics was used for feature extraction. The selected HR radiomics features and SR radiomics features were combined with clinical features to construct the HR fusion model and SR fusion model, respectively. The area under the receiver operating characteristic curve (AUC) of the models and radiologists was analyzed and compared by the Delong test. Results A total of 681 women (median age, 47 years; interquartile range, 42–54) with 681 biopsy‐proven DCIS lesions were included, with 422 lesions in the training set, 106 lesions in the validation set, and 153 lesions in the external test set. The SR Fusion model achieved an AUC of 0.819 (0.732–0.887) in the validation set and 0.800 (95% CI 0.728–0.860) in the external test set. It outperformed the radiologists (AUC = 0.603–0.627; p < 0.001) in the validation set. Additionally, it surpassed the clinical model (AUC = 0.682, 95% CI 0.602–0.755; p = 0.02) and the HR Fusion model (AUC = 0.724, 95% CI 0.646–0.793; p = 0.03) in the external test set. Conclusion The SR Fusion model integrating SR features and clinical features can effectively predict the upstaging of DCIS. |
| format | Article |
| id | doaj-art-a2dfc6a575574aef9e62ad6048cd9d14 |
| institution | Kabale University |
| issn | 2045-7634 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Wiley |
| record_format | Article |
| series | Cancer Medicine |
| spelling | doaj-art-a2dfc6a575574aef9e62ad6048cd9d142025-08-20T04:03:08ZengWileyCancer Medicine2045-76342025-08-011415n/an/a10.1002/cam4.71155Super‐Resolution Ultrasound Radiomics Can Predict the Upstaging of Ductal Carcinoma In SituLiang Yang0Xiaoxian Li1Zhiyuan Wang2Qian Li3Juan Fu4Xuebin Zou5Xia Liang6Xu Liu7Ruirui Zhang8Junjun Chen9Hui Xie10Yini Huang11Jianhua Zhou12Department of Ultrasound Sun Yat‐Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine Guangzhou P. R. ChinaDepartment of Ultrasound Sun Yat‐Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine Guangzhou P. R. ChinaDepartment of Medical Ultrasound The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University/Hunan Cancer Hospital Changsha Hunan ChinaDepartment of Ultrasound Affiliated Tumor Hospital of Zhengzhou University, Henan Cancer Hospital Zhengzhou Henan ChinaDepartment of Ultrasound Sun Yat‐Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine Guangzhou P. R. ChinaDepartment of Ultrasound Sun Yat‐Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine Guangzhou P. R. ChinaDepartment of Medical Ultrasound The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University/Hunan Cancer Hospital Changsha Hunan ChinaDepartment of Medical Ultrasound The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University/Hunan Cancer Hospital Changsha Hunan ChinaDepartment of Ultrasound Affiliated Tumor Hospital of Zhengzhou University, Henan Cancer Hospital Zhengzhou Henan ChinaDepartment of Ultrasound Dongguan People's Hospital Affiliated to Southern Medical University Dongguan Guangdong ChinaDepartment of Radiation Oncology Affiliated Hospital (Clinical College) of Xiangnan University Chenzhou Hunan People's Republic of ChinaDepartment of Ultrasound Sun Yat‐Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine Guangzhou P. R. ChinaDepartment of Ultrasound Sun Yat‐Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine Guangzhou P. R. ChinaABSTRACT Introduction Preoperatively distinguishing pure ductal carcinoma in situ (DCIS) from upstaged DCIS is important for deciding optimal surgical strategies. However, it is hard to preoperatively predict the upstaging of biopsy‐proven DCIS. This study aims to develop an effective radiomics model for predicting the upstaging of DCIS based on super‐resolution (SR) ultrasound images. Methods In this multicentre retrospective study, patients with biopsy‐proven DCIS who underwent ultrasound examination were included. A super‐resolution reconstruction algorithm was used to enhance the resolution of original high resolution (HR) ultrasound images and obtain SR images. Pyradiomics was used for feature extraction. The selected HR radiomics features and SR radiomics features were combined with clinical features to construct the HR fusion model and SR fusion model, respectively. The area under the receiver operating characteristic curve (AUC) of the models and radiologists was analyzed and compared by the Delong test. Results A total of 681 women (median age, 47 years; interquartile range, 42–54) with 681 biopsy‐proven DCIS lesions were included, with 422 lesions in the training set, 106 lesions in the validation set, and 153 lesions in the external test set. The SR Fusion model achieved an AUC of 0.819 (0.732–0.887) in the validation set and 0.800 (95% CI 0.728–0.860) in the external test set. It outperformed the radiologists (AUC = 0.603–0.627; p < 0.001) in the validation set. Additionally, it surpassed the clinical model (AUC = 0.682, 95% CI 0.602–0.755; p = 0.02) and the HR Fusion model (AUC = 0.724, 95% CI 0.646–0.793; p = 0.03) in the external test set. Conclusion The SR Fusion model integrating SR features and clinical features can effectively predict the upstaging of DCIS.https://doi.org/10.1002/cam4.71155breast cancerductal carcinoma in situmachine learningsuper‐resolution reconstructionultrasound |
| spellingShingle | Liang Yang Xiaoxian Li Zhiyuan Wang Qian Li Juan Fu Xuebin Zou Xia Liang Xu Liu Ruirui Zhang Junjun Chen Hui Xie Yini Huang Jianhua Zhou Super‐Resolution Ultrasound Radiomics Can Predict the Upstaging of Ductal Carcinoma In Situ Cancer Medicine breast cancer ductal carcinoma in situ machine learning super‐resolution reconstruction ultrasound |
| title | Super‐Resolution Ultrasound Radiomics Can Predict the Upstaging of Ductal Carcinoma In Situ |
| title_full | Super‐Resolution Ultrasound Radiomics Can Predict the Upstaging of Ductal Carcinoma In Situ |
| title_fullStr | Super‐Resolution Ultrasound Radiomics Can Predict the Upstaging of Ductal Carcinoma In Situ |
| title_full_unstemmed | Super‐Resolution Ultrasound Radiomics Can Predict the Upstaging of Ductal Carcinoma In Situ |
| title_short | Super‐Resolution Ultrasound Radiomics Can Predict the Upstaging of Ductal Carcinoma In Situ |
| title_sort | super resolution ultrasound radiomics can predict the upstaging of ductal carcinoma in situ |
| topic | breast cancer ductal carcinoma in situ machine learning super‐resolution reconstruction ultrasound |
| url | https://doi.org/10.1002/cam4.71155 |
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