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.
Materials and 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
