Role of DECT-Based Imaging Biomarkers and Machine Learning to Predict Renal Cell Carcinoma Subtypes
DECT-based quantitative imaging biomarkers have moderate diagnostic accuracy, which can be greatly improved using ML to differentiate between ccRCC and non-ccRCC and predict the grade of ccRCC.
Authors
- Rohan Raju Dhanakshirur
- Kamlesh Jain (ORCID: https://orcid.org/0000-0002-0233-8369)
- Neha Baijal (ORCID: https://orcid.org/0000-0003-1767-8363)
- Sanil Garg (ORCID: https://orcid.org/0009-0009-5508-3468)
- Chandan Jyoti Das (ORCID: https://orcid.org/0000-0001-6505-5940)
- Amit Gupta (ORCID: https://orcid.org/0000-0003-0192-3512)
- Nidhi Yadav
Institutions
- All India Institute of Medical Sciences (IN)
- Indian Institute of Technology Delhi (IN)
Publication Details
- Journal
- Indian journal of radiology and imaging - new series/Indian journal of radiology and imaging/Indian Journal of Radiology & Imaging
- Published
- 2025-03-27
- DOI
- https://doi.org/10.1055/s-0045-1806751
- Primary Topic
- Renal cell carcinoma treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.00