Role of DECT-Based Imaging Biomarkers and Machine Learning to Predict Renal Cell Carcinoma Subtypes
Objective The aim of the study was to assess and compare dual-energy CT (DECT) based quantitative parameters to differentiate between clear cell renal cell carcinoma (ccRCC) and non-ccRCC.
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| Format: | Article |
| Language: | English |
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Thieme Medical and Scientific Publishers Pvt. Ltd.
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| Series: | Indian Journal of Radiology and Imaging |
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| Online Access: | http://www.thieme-connect.de/DOI/DOI?10.1055/s-0045-1806751 |
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| author | Neha Baijal Amit Gupta Sanil Garg Neel Yadav Rohan R. Dhanakshirur Kshitiz Jain Chandan J. Das |
| author_facet | Neha Baijal Amit Gupta Sanil Garg Neel Yadav Rohan R. Dhanakshirur Kshitiz Jain Chandan J. Das |
| author_sort | Neha Baijal |
| collection | DOAJ |
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Objective The aim of the study was to assess and compare dual-energy CT (DECT) based quantitative parameters to differentiate between clear cell renal cell carcinoma (ccRCC) and non-ccRCC. |
| format | Article |
| id | doaj-art-8916ecdf09454bbf8cc79c49dcc3f20e |
| institution | Kabale University |
| issn | 0971-3026 1998-3808 |
| language | English |
| publisher | Thieme Medical and Scientific Publishers Pvt. Ltd. |
| record_format | Article |
| series | Indian Journal of Radiology and Imaging |
| spelling | doaj-art-8916ecdf09454bbf8cc79c49dcc3f20e2025-08-20T03:42:06ZengThieme Medical and Scientific Publishers Pvt. Ltd.Indian Journal of Radiology and Imaging0971-30261998-380810.1055/s-0045-1806751Role of DECT-Based Imaging Biomarkers and Machine Learning to Predict Renal Cell Carcinoma SubtypesNeha Baijal0https://orcid.org/0000-0003-1767-8363Amit Gupta1Sanil Garg2Neel Yadav3Rohan R. Dhanakshirur4Kshitiz Jain5Chandan J. Das6https://orcid.org/0000-0001-6505-5940Department of Radio-Diagnosis and Interventional Radiology, All India Institute of Medical Sciences, New Delhi, IndiaDepartment of Radio-Diagnosis and Interventional Radiology, All India Institute of Medical Sciences, New Delhi, IndiaDepartment of Radio-Diagnosis and Interventional Radiology, All India Institute of Medical Sciences, New Delhi, IndiaDepartment of Radio-Diagnosis and Interventional Radiology, All India Institute of Medical Sciences, New Delhi, IndiaAmarnath and Shashi Khosla School of Information Technology, Indian Institute of Technology Delhi, New Delhi, IndiaYardi School of Artificial Intelligence, Indian Institute of Technology Delhi, New Delhi, IndiaDepartment of Radio-Diagnosis and Interventional Radiology, All India Institute of Medical Sciences, New Delhi, India Objective The aim of the study was to assess and compare dual-energy CT (DECT) based quantitative parameters to differentiate between clear cell renal cell carcinoma (ccRCC) and non-ccRCC.http://www.thieme-connect.de/DOI/DOI?10.1055/s-0045-1806751clear cell renal cell carcinomaRCC subtypingmachine learning |
| spellingShingle | Neha Baijal Amit Gupta Sanil Garg Neel Yadav Rohan R. Dhanakshirur Kshitiz Jain Chandan J. Das Role of DECT-Based Imaging Biomarkers and Machine Learning to Predict Renal Cell Carcinoma Subtypes Indian Journal of Radiology and Imaging clear cell renal cell carcinoma RCC subtyping machine learning |
| title | Role of DECT-Based Imaging Biomarkers and Machine Learning to Predict Renal Cell Carcinoma Subtypes |
| title_full | Role of DECT-Based Imaging Biomarkers and Machine Learning to Predict Renal Cell Carcinoma Subtypes |
| title_fullStr | Role of DECT-Based Imaging Biomarkers and Machine Learning to Predict Renal Cell Carcinoma Subtypes |
| title_full_unstemmed | Role of DECT-Based Imaging Biomarkers and Machine Learning to Predict Renal Cell Carcinoma Subtypes |
| title_short | Role of DECT-Based Imaging Biomarkers and Machine Learning to Predict Renal Cell Carcinoma Subtypes |
| title_sort | role of dect based imaging biomarkers and machine learning to predict renal cell carcinoma subtypes |
| topic | clear cell renal cell carcinoma RCC subtyping machine learning |
| url | http://www.thieme-connect.de/DOI/DOI?10.1055/s-0045-1806751 |
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