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CC BY-NC-ND 4.0 · Indian J Radiol Imaging 2025; 35(04): 573-581
DOI: 10.1055/s-0045-1806751
Original Article

Role of DECT-Based Imaging Biomarkers and Machine Learning to Predict Renal Cell Carcinoma Subtypes

Neha Baijal 
1   Department of Radio-Diagnosis and Interventional Radiology, All India Institute of Medical Sciences, New Delhi, India
,
Amit Gupta
1   Department of Radio-Diagnosis and Interventional Radiology, All India Institute of Medical Sciences, New Delhi, India
,
Sanil Garg
1   Department of Radio-Diagnosis and Interventional Radiology, All India Institute of Medical Sciences, New Delhi, India
,
Neel Yadav
1   Department of Radio-Diagnosis and Interventional Radiology, All India Institute of Medical Sciences, New Delhi, India
,
Rohan R. Dhanakshirur
2   Amarnath and Shashi Khosla School of Information Technology, Indian Institute of Technology Delhi, New Delhi, India
,
Kshitiz Jain
3   Yardi School of Artificial Intelligence, Indian Institute of Technology Delhi, New Delhi, India
,
Chandan J. Das 
1   Department of Radio-Diagnosis and Interventional Radiology, All India Institute of Medical Sciences, New Delhi, India
› Author Affiliations
Funding None.
› Further Information
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Abstract

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.

Materials and Methods

This was a retrospective study including RCC patients who underwent DECT prior to surgery between January 2017 and December 2022. Two DECT parameters—iodine concentration (IC) and iodine ratio (IR)—were measured by two independent readers who manually drew circular regions of interest on the most enhancing part of the tumor. Inter-reader agreement was calculated using the intraclass correlation coefficient. Machine learning (ML) models trained to classify the histologic subtype as ccRCC and non-ccRCC, and grade of ccRCC as low or high, were evaluated for their accuracy.

Results

A total of 112 patients (mean age: 65 years; male:female: 61:51), with 87 ccRCCs and 25 non-ccRCCs, were included. There was good inter-reader agreement for both IC and IR with a Pearson coefficient of 0.89. The individual DECT parameters had an accuracy of 77.7% (IC) and 77.5% (IR) for distinguishing ccRCC and non-ccRCC. Random Forest classifier and AdaBoost were the best ML models with an accuracy of 89.2% each. When ML algorithms were combined, the performance was improved, with AdaBoost performing the best with an accuracy of 100%. To distinguish low- and high-grade ccRCCs, IC and IR had an accuracy of 77.9 and 77.6%, respectively, while the ML models all did equally well with an accuracy of 77.6%. Combining ML algorithms again led to improved performance, with AdaBoost being the best overall ML model.

Conclusion

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.

Keywords

clear cell renal cell carcinoma - RCC subtyping - machine learning

Ethical Approval

Ethical approval was waived by the Institutional Ethics Committee in view of the retrospective nature of the study.




Publication History

Article published online:
27 March 2025

© 2025. Indian Radiological Association. This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)

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