Ultrasound Radiogenomics-based Prediction Models for Gene Mutation Status in Breast Cancer
Ultrasound radiogenomics, an emerging field at the intersection of radiology and genomics, employs high-throughput methods to convert radiological images into high-dimensional data, which are then processed to extract and analyze radiomic features. These features, including shape, texture, and inten...
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Editorial Office of Advanced Ultrasound in Diagnosis and Therapy
2025-03-01
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| Series: | Advanced Ultrasound in Diagnosis and Therapy |
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| Online Access: | https://www.journaladvancedultrasound.com/fileup/2576-2516/PDF/1738998921685-1874707131.pdf |
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| author | Zhai Yue, Tan Dianhuan, Lin Xiaona, Lv Heng, Chen Yan, Li Yongbin, Luo Haiyu, Dan Qing, Zhao Chenyang, Xiang Hongjin, Zheng Tingting, Sun Desheng |
| author_facet | Zhai Yue, Tan Dianhuan, Lin Xiaona, Lv Heng, Chen Yan, Li Yongbin, Luo Haiyu, Dan Qing, Zhao Chenyang, Xiang Hongjin, Zheng Tingting, Sun Desheng |
| author_sort | Zhai Yue, Tan Dianhuan, Lin Xiaona, Lv Heng, Chen Yan, Li Yongbin, Luo Haiyu, Dan Qing, Zhao Chenyang, Xiang Hongjin, Zheng Tingting, Sun Desheng |
| collection | DOAJ |
| description | Ultrasound radiogenomics, an emerging field at the intersection of radiology and genomics, employs high-throughput methods to convert radiological images into high-dimensional data, which are then processed to extract and analyze radiomic features. These features, including shape, texture, and intensity variations, are correlated with specific genetic mutations such as TP53 and PIK3CA, critical for cancer progression and treatment response. By integrating clinical data with ultrasonic features, predictive models are developed using machine learning techniques, aiming to refine the capability to diagnose and personalize treatment plans for breast cancer patients. This approach reduces the need for invasive biopsies and medical costs for patients through a better understanding of the tumor’s biological behavior using ultrasound images. This review focuses on the application of ultrasound radiogenomics for predicting gene mutations in breast cancer, highlighting its transformative potential in clinical practice and discussing ongoing challenges and future directions in this field. |
| format | Article |
| id | doaj-art-6a44dd9bce2f4c76a1c7b485266a0fa2 |
| institution | Kabale University |
| issn | 2576-2516 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Editorial Office of Advanced Ultrasound in Diagnosis and Therapy |
| record_format | Article |
| series | Advanced Ultrasound in Diagnosis and Therapy |
| spelling | doaj-art-6a44dd9bce2f4c76a1c7b485266a0fa22025-02-12T05:45:03ZengEditorial Office of Advanced Ultrasound in Diagnosis and TherapyAdvanced Ultrasound in Diagnosis and Therapy2576-25162025-03-0191102010.37015/AUDT.2025.240010Ultrasound Radiogenomics-based Prediction Models for Gene Mutation Status in Breast CancerZhai Yue, Tan Dianhuan, Lin Xiaona, Lv Heng, Chen Yan, Li Yongbin, Luo Haiyu, Dan Qing, Zhao Chenyang, Xiang Hongjin, Zheng Tingting, Sun Desheng0aDepartment of Ultrasound, Peking University Shenzhen Hospital, Shenzhen, China;bShenzhen Key Laboratory for Drug Addiction and Medication Safety, Department of Ultrasound, Institute of Ultrasonic Medicine, Peking University Shenzhen Hospital, Shenzhen Peking University-The Hong Kong University of Science and Technology Medical Center, Shenzhen, Guangdong, P. R. ChinaUltrasound radiogenomics, an emerging field at the intersection of radiology and genomics, employs high-throughput methods to convert radiological images into high-dimensional data, which are then processed to extract and analyze radiomic features. These features, including shape, texture, and intensity variations, are correlated with specific genetic mutations such as TP53 and PIK3CA, critical for cancer progression and treatment response. By integrating clinical data with ultrasonic features, predictive models are developed using machine learning techniques, aiming to refine the capability to diagnose and personalize treatment plans for breast cancer patients. This approach reduces the need for invasive biopsies and medical costs for patients through a better understanding of the tumor’s biological behavior using ultrasound images. This review focuses on the application of ultrasound radiogenomics for predicting gene mutations in breast cancer, highlighting its transformative potential in clinical practice and discussing ongoing challenges and future directions in this field.https://www.journaladvancedultrasound.com/fileup/2576-2516/PDF/1738998921685-1874707131.pdf|ultrasound|radiogenomics|breast cancer|gene mutation|prediction models |
| spellingShingle | Zhai Yue, Tan Dianhuan, Lin Xiaona, Lv Heng, Chen Yan, Li Yongbin, Luo Haiyu, Dan Qing, Zhao Chenyang, Xiang Hongjin, Zheng Tingting, Sun Desheng Ultrasound Radiogenomics-based Prediction Models for Gene Mutation Status in Breast Cancer Advanced Ultrasound in Diagnosis and Therapy |ultrasound|radiogenomics|breast cancer|gene mutation|prediction models |
| title | Ultrasound Radiogenomics-based Prediction Models for Gene Mutation Status in Breast Cancer |
| title_full | Ultrasound Radiogenomics-based Prediction Models for Gene Mutation Status in Breast Cancer |
| title_fullStr | Ultrasound Radiogenomics-based Prediction Models for Gene Mutation Status in Breast Cancer |
| title_full_unstemmed | Ultrasound Radiogenomics-based Prediction Models for Gene Mutation Status in Breast Cancer |
| title_short | Ultrasound Radiogenomics-based Prediction Models for Gene Mutation Status in Breast Cancer |
| title_sort | ultrasound radiogenomics based prediction models for gene mutation status in breast cancer |
| topic | |ultrasound|radiogenomics|breast cancer|gene mutation|prediction models |
| url | https://www.journaladvancedultrasound.com/fileup/2576-2516/PDF/1738998921685-1874707131.pdf |
| work_keys_str_mv | AT zhaiyuetandianhuanlinxiaonalvhengchenyanliyongbinluohaiyudanqingzhaochenyangxianghongjinzhengtingtingsundesheng ultrasoundradiogenomicsbasedpredictionmodelsforgenemutationstatusinbreastcancer |