Predicting Renal Cell Carcinoma Subtypes and Fuhrman Grading Using Multiphasic CT-Based Texture Analysis and Machine Learning Techniques

Predicting Renal Cell Carcinoma Subtypes and Fuhrman Grading Using Multiphasic CT-Based Texture Analysis and Machine Learning Techniques

Objectives The aim of this study is to evaluate computed tomography texture analysis (CTTA) on multiphase CT scans for distinguishing clear cell renal cell carcinoma (ccRCC) from non-ccRCC and predicting Fuhrman's grade in ccRCC using open-source Python libraries.

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Bibliographic Details
Main Authors: Amit Gupta, Sanil Garg, Neel Yadav, Rohan Raju Dhanakshirur, Kshitiz Jain, Rishi Nayyar, Seema Kaushal, Chandan J. Das
Format: Article
Language:English
Published: Thieme Medical and Scientific Publishers Pvt. Ltd. 2025-04-01
Series:Indian Journal of Radiology and Imaging
Subjects:
texture analysis
machine learning
renal cell carcinoma
Online Access:http://www.thieme-connect.de/DOI/DOI?10.1055/s-0044-1796639
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Summary:Objectives The aim of this study is to evaluate computed tomography texture analysis (CTTA) on multiphase CT scans for distinguishing clear cell renal cell carcinoma (ccRCC) from non-ccRCC and predicting Fuhrman's grade in ccRCC using open-source Python libraries.
ISSN:0971-3026
1998-3808

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