Deep learning techniques for imaging diagnosis of renal cell carcinoma: current and emerging trends
This study summarizes the latest achievements, challenges, and future research directions in deep learning technologies for the diagnosis of renal cell carcinoma (RCC). This is the first review of deep learning in RCC applications. This review aims to show that deep learning technologies hold great...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2023-09-01
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| Series: | Frontiers in Oncology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2023.1152622/full |
| _version_ | 1827854694452559872 |
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| author | Zijie Wang Xiaofei Zhang Xinning Wang Jianfei Li Yuhao Zhang Tianwei Zhang Shang Xu Wei Jiao Haitao Niu |
| author_facet | Zijie Wang Xiaofei Zhang Xinning Wang Jianfei Li Yuhao Zhang Tianwei Zhang Shang Xu Wei Jiao Haitao Niu |
| author_sort | Zijie Wang |
| collection | DOAJ |
| description | This study summarizes the latest achievements, challenges, and future research directions in deep learning technologies for the diagnosis of renal cell carcinoma (RCC). This is the first review of deep learning in RCC applications. This review aims to show that deep learning technologies hold great promise in the field of RCC diagnosis, and we look forward to more research results to meet us for the mutual benefit of renal cell carcinoma patients. Medical imaging plays an important role in the early detection of renal cell carcinoma (RCC), as well as in the monitoring and evaluation of RCC during treatment. The most commonly used technologies such as contrast enhanced computed tomography (CECT), ultrasound and magnetic resonance imaging (MRI) are now digitalized, allowing deep learning to be applied to them. Deep learning is one of the fastest growing fields in the direction of medical imaging, with rapidly emerging applications that have changed the traditional medical treatment paradigm. With the help of deep learning-based medical imaging tools, clinicians can diagnose and evaluate renal tumors more accurately and quickly. This paper describes the application of deep learning-based imaging techniques in RCC assessment and provides a comprehensive review. |
| first_indexed | 2024-03-12T11:26:30Z |
| format | Article |
| id | doaj.art-adf4b8b09e454f5892010039682eaa5f |
| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-03-12T11:26:30Z |
| publishDate | 2023-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj.art-adf4b8b09e454f5892010039682eaa5f2023-09-01T08:16:06ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2023-09-011310.3389/fonc.2023.11526221152622Deep learning techniques for imaging diagnosis of renal cell carcinoma: current and emerging trendsZijie Wang0Xiaofei Zhang1Xinning Wang2Jianfei Li3Yuhao Zhang4Tianwei Zhang5Shang Xu6Wei Jiao7Haitao Niu8Department of Vascular Intervention, ShengLi Oilfield Center Hospital, Dongying, ChinaDepartment of Education and Training, The Affiliated Hospital of Qingdao University, Qingdao, ChinaDepartment of Urology, Affiliated Hospital of Qingdao University, Qingdao, ChinaExtenics Specialized Committee, Chinese Association of Artificial Intelligence (ESCCAAI), Beijing, ChinaDepartment of Urology, Affiliated Hospital of Qingdao University, Qingdao, ChinaDepartment of Urology, Affiliated Hospital of Qingdao University, Qingdao, ChinaDepartment of Urology, Affiliated Hospital of Qingdao University, Qingdao, ChinaDepartment of Urology, Affiliated Hospital of Qingdao University, Qingdao, ChinaDepartment of Urology, Affiliated Hospital of Qingdao University, Qingdao, ChinaThis study summarizes the latest achievements, challenges, and future research directions in deep learning technologies for the diagnosis of renal cell carcinoma (RCC). This is the first review of deep learning in RCC applications. This review aims to show that deep learning technologies hold great promise in the field of RCC diagnosis, and we look forward to more research results to meet us for the mutual benefit of renal cell carcinoma patients. Medical imaging plays an important role in the early detection of renal cell carcinoma (RCC), as well as in the monitoring and evaluation of RCC during treatment. The most commonly used technologies such as contrast enhanced computed tomography (CECT), ultrasound and magnetic resonance imaging (MRI) are now digitalized, allowing deep learning to be applied to them. Deep learning is one of the fastest growing fields in the direction of medical imaging, with rapidly emerging applications that have changed the traditional medical treatment paradigm. With the help of deep learning-based medical imaging tools, clinicians can diagnose and evaluate renal tumors more accurately and quickly. This paper describes the application of deep learning-based imaging techniques in RCC assessment and provides a comprehensive review.https://www.frontiersin.org/articles/10.3389/fonc.2023.1152622/fulldeep learningartificial intelligencecarcinomaprediction modelimaging diagnosis |
| spellingShingle | Zijie Wang Xiaofei Zhang Xinning Wang Jianfei Li Yuhao Zhang Tianwei Zhang Shang Xu Wei Jiao Haitao Niu Deep learning techniques for imaging diagnosis of renal cell carcinoma: current and emerging trends Frontiers in Oncology deep learning artificial intelligence carcinoma prediction model imaging diagnosis |
| title | Deep learning techniques for imaging diagnosis of renal cell carcinoma: current and emerging trends |
| title_full | Deep learning techniques for imaging diagnosis of renal cell carcinoma: current and emerging trends |
| title_fullStr | Deep learning techniques for imaging diagnosis of renal cell carcinoma: current and emerging trends |
| title_full_unstemmed | Deep learning techniques for imaging diagnosis of renal cell carcinoma: current and emerging trends |
| title_short | Deep learning techniques for imaging diagnosis of renal cell carcinoma: current and emerging trends |
| title_sort | deep learning techniques for imaging diagnosis of renal cell carcinoma current and emerging trends |
| topic | deep learning artificial intelligence carcinoma prediction model imaging diagnosis |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2023.1152622/full |
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