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CC BY-NC-ND 4.0 · Indian J Radiol Imaging
DOI: 10.1055/s-0044-1796639
Original Article

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

Amit Gupta
1   Department of Radiodiagnosis and Interventional Radiology, All India Institute of Medical Sciences, New Delhi, India
,
Sanil Garg
1   Department of Radiodiagnosis and Interventional Radiology, All India Institute of Medical Sciences, New Delhi, India
,
Neel Yadav
1   Department of Radiodiagnosis and Interventional Radiology, All India Institute of Medical Sciences, New Delhi, India
,
Rohan Raju 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
,
Rishi Nayyar
4   Department of Urology, All India Institute of Medical Sciences, New Delhi, India
,
Seema Kaushal
5   Department of Pathology, All India Institute of Medical Sciences, New Delhi, India
,
Chandan J. Das 
1   Department of Radiodiagnosis and Interventional Radiology, All India Institute of Medical Sciences, New Delhi, India
› Author Affiliations Funding None.
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Abstract

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.

Methods Conducted retrospectively, the study included 144 patients with RCCs (108 ccRCCs and 36 non-ccRCCs) who underwent preoperative multiphasic CT. Ninety ccRCCs were categorized into 71 low-grade and 19 high-grade ccRCCs. Tumor was marked on the largest axial tumor slice using “LabelMe” across different CT phases. First- and second-order texture features were computed using Python's scipy, numpy, and opencv libraries. Multivariable logistic regression analysis and machine learning (ML) models were used to evaluate CTTA parameters from different CT phases for RCC classification. The best ML model for distinguishing ccRCC and non-ccRCC was externally validated using data from the 2019 Kidney and Kidney Tumor Segmentation Challenge.

Results Entropy in the corticomedullary (CM) phase was the best individual parameter for distinguishing ccRCC from non-ccRCC with (F1 score: 0.83). The support vector machine (SVM) based ML model, incorporating CM phase features, performed the best, with an F1 score of 0.87. External validation for the same model yielded an accuracy of 0.82 and an F1 score of 0.81. ML models and individual texture parameters showed less accuracy for classifying low- versus high-grade ccRCCs, with a maximum F1 score of 0.76 for the CM phase SVM model. Other CT phases yielded inferior results for both classification tasks.

Conclusion CTTA employing open-source Python tools is a viable tool for differentiating ccRCCs from non-ccRCCs and predicting ccRCC grade.

Keywords

texture analysis - machine learning - renal cell carcinoma

Ethical Approval

Ethical approval was obtained from the institutional review board. The need for patient consent was waived off by the institutional review board due to the retrospective nature of the analysis.




Publication History

Article published online:
11 December 2024

© 2024. 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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