Potential to use textural features extracted from CT images to predict the grade of clear cell renal cell cancer

In order to select the most appropriate and effective treatment tactics for patients with clear cell renal cancer (ccRC), a multi-phase computed tomography (CT) with intravenous contrast is performed during the preoperative stage. It is possible that textural analysis data obtained from segmentation of CT data can be used to predict the likelihood of high-grade ccRC, which could potentially adjust treatment tactics.

Purpose: to perform a textural analysis of the preoperative multiphase CT data in patients with verified ссRC, to identify texture features that allow predicting ccRC grade

Material and methods: Retrospective analysis of data from 189 pre-operative CT studies was performed on 164 patients who underwent renal resection or nephrectomy, and the ccRC was morphologicaly verified using the WHO/ISUP grade classification system. CcRC grade 1–2 was verified in 130 cases, ccRC grade 3–4 was verified in 59 cases. For the performance of textural analysis, data from the corticomedullar, nephrographic and excretory CT phases were used, segmentation was carried out with 2D and 3D methods. The study was conducted retrospectively.

Results. The most informative in ccRC grade predicting were the textural features obtained from 3D tumor segmentation in the corticomedullar and excretory CT phases.

The largest number of textural features linearly associated with the ccRC grade 3–4 odds logarithm was obtained from a textural analysis of 3D tumor segmentation data in the corticomedullar and nephrographic CT phases.

The smallest number of textural features statistically significantly associated with the ccRC grade was obtained from a textural analysis of 2D segmentation data in the excretory phase of CT.

In conducting a multi-factor analysis of texture features as predictors of the ccRC grade, the most effective way to obtain texture features was to analyze the results of 3D segmentation into the excretory phase using the first 6 main components as predictors, explaining the 80% and more dispersion of the values of textural features.

The highest values of Nagelkerke pseudo-R² (0.49) and AUC (0.87) were found in a multi-factor logistic regression model, which included SHAPE_Sphericity and SHAPE_Compacity as precursors to 3D segmentation in the excretory phase when using the Reflect filter.

The model with one textural indicator included – DISCRETIZED_TLG – in 2D segmentation into the excretory phase using a Reflect filter was characterized by slightly lower Nagelkerke pseudo-R² (0.44) and AUC (0.85).

Based on the data obtained, we have developed two multi-factor models for predicting the probability of a grade 3–4 ccRC, based on 3D and 2D methods of tumor segmentation in CT images. The model using 3D segmentation data was characterized by predictive accuracy, sensitivity and specificity of 80%, 79.7% and 80.2%, respectively. Discriminative characteristics of the model using 3D segmentation data have surpassed those of the model using 2D segmentation data.

Conclusion. In the course of our work, we proved that it is possible to use textural features obtained from the analysis of CT data with contrast enhancement to predict the ccRC grade and developed a high-precision diagnostic model for predicting ccRC grade.

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