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CT Texture Analysis of Renal Masses

Posted Nov 30, 2014 Updated Apr 10, 2023
By Siva P. Raman MD , Yifei Chen BS , James L. Schroeder PhD , Peng Huang PhD , Elliot K. Fishman MD
14 min read

Rationale and Objectives

Computed tomography texture analysis (CTTA) allows quantification of heterogeneity within a region of interest. This study investigates the possibility of distinguishing between several common renal masses using CTTA-derived parameters by developing and validating a predictive model.

Materials and Methods

CTTA software was used to analyze 20 clear cell renal cell carcinomas (RCCs), 20 papillary RCCs, 20 oncocytomas, and 20 renal cysts. Regions of interest were drawn around each mass on multiple slices in the arterial, venous, and delayed phases on renal mass protocol CT scans. Unfiltered images and spatial band-pass filtered images were analyzed to quantify heterogeneity. Random forest method was used to construct a predictive model to classify lesions using quantitative parameters. The model was externally validated on a separate set of 19 unknown cases.

Results

The random forest model correctly categorized oncocytomas in 89% of cases (sensitivity = 89%, specificity = 99%), clear cell RCCs in 91% of cases (sensitivity = 91%, specificity = 97%), cysts in 100% of cases (sensitivity = 100%, specificity = 100%), and papillary RCCs in 100% of cases (sensitivity = 100%, specificity = 98%).

Conclusions

CTTA, in conjunction with random forest modeling, demonstrates promise as a tool to characterize lesions. Various renal masses were accurately classified using quantitative information derived from routine scans.

When confronted with an indeterminate renal mass on computed tomography (CT), the ability of a radiologist to accurately differentiate common histologic subtypes (such as clear cell renal cell carcinoma [RCC], papillary RCC, chromophobe RCC, oncocytomas, etc.) is limited to either an analysis of morphologic features or Hounsfield attenuations. Many studies have used some combinations of enhancement characteristics and lesion morphology to differentiate these masses, and although these various tumor types as groups tend to have different appearances and enhancement patterns, there is a significant overlap between lesion categories that prevents prospective prediction of a lesion’s underlying histology with high confidence . As a result, solid renal masses are usually surgically resected unless the patient is a poor surgical candidate or the lesion measures less than 2 cm in size. If more confident prospective characterization were possible, it is conceivable that lesions that were strongly thought to represent more indolent variants of RCC (ie, papillary and chromophobe RCC) or benign oncocytomas could be followed rather than resected.

Computed tomography texture analysis (CTTA) is a quantitative technique that allows users to characterize heterogeneity within a region of interest (ROI) based on the distribution of pixel intensities and gray-level values using both unfiltered and frequency filtered images by deriving quantitative texture parameters based on attributes of the pixel values themselves and the image histogram. This quantitative technique has been primarily used in a number of studies as a means of predicting patient outcomes and prognosis . However, there has been only limited application of this method toward lesion characterization and the differentiation of lesions with similar radiographic appearance (such as various types of solid renal masses), and although the few works that have dealt with this topic have demonstrated quantitative differences in texture variables between different lesions, they have not sought to create true predictive models using texture data .

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Materials and methods

Patient Selection

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Table 1

Demographic and Patient Information for the 70 Patients (80 Lesions) Used in the Construction of the Random Forest Models and the 18 Patients (19 Lesions) Used for External Validation of the Models

Oncocytoma Clear Cell Papillary Simple Cyst Random Forest Modeled Subset Number of Patients 20 20 18 12 Average Age (y) 66 65 64 65 Female (%) 25% 50% 28% 42% Total Number of Lesions 20 20 20 20 Total Number of Slices 154 160 158 150 Average Number of Slices/Lesion 7.7 8.0 7.9 7.5 Validation Subset Number of Patients 4 4 5 5 Average Age (y) 66 47 65 60 Female (%) 25% 25% 0% 40% Total Number of Lesions 4 5 5 5 Total Number of Slices 21 31 36 25 Average Number of Slices/Lesion 5.3 6.2 7.2 5.0

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CT Technique

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CT Texture Analysis

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Figure 1, Examples of the region of interest selection and matching for different lesions on the arterial, venous, and delayed phase images.

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Statistics

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Power Analysis

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Results

Random Forest Model

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Table 2

Confusion Matrices for Random Forest Models (Based on the Out-of-Bag Data) Using all Three Contrast Phases in Conjunction and Models Using Only the Arterial, Venous, or Delayed Phases Alone

Lesion Type Oncocytoma Clear Cell RCC Papillary RCC Simple Cyst Sensitivity (%) Specificity (%) Total Class Error (%) 3-Phase Oncocytoma 137 16 1 0 89 99 11.0 Clear Cell RCC 7 146 7 0 91 97 8.8 Papillary RCC 0 0 158 0 100 98 0.0 Simple Cyst 0 0 0 150 100 100 0.0 Arterial Oncocytoma 136 15 3 0 88 98 11.7 Clear Cell RCC 10 145 5 0 91 97 9.4 Papillary RCC 1 0 156 1 99 98 1.3 Simple Cyst 0 0 1 149 99 100 0.7 Venous Oncocytoma 142 12 0 0 92 97 7.8 Clear Cell RCC 16 136 7 1 85 97 15.0 Papillary RCC 0 3 153 2 97 98 3.2 Simple Cyst 0 0 1 149 99 99 0.7 Delayed Oncocytoma 125 23 5 1 81 94 18.8 Clear Cell RCC 24 132 4 0 83 94 17.5 Papillary RCC 2 4 151 1 96 98 4.4 Simple Cyst 0 0 1 149 99 100 0.7

RCC, renal cell carcinoma.

The results are presented on a per-slice basis. The “true” pathologic diagnosis is represented by the heading for each row, whereas the classification provided by the model is represented by the heading for each column.

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Variable Importance Plots

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Figure 2, Variable importance plot for three phases (arterial, venous, delayed) in conjunction of the constructed random forest model . The plot details the relative importance of each of the quantitative texture parameters (which is listed on the y-axis of the plot) to the model's accuracy. The x-axis is the mean decrease in the Gini coefficient that results when that variable is included in the model. The Gini coefficient is a measure of inequality among the trees in the random forest, and in this case represents the performance of the random forest model with and without a variable included. Those variables that have the highest decrease in the Gini coefficient were most likely to create consensus among the individual decision trees used in the model (or reduce inequality) when included in the model. These variables are therefore most predictive of the outcome of the model overall. Those variables with a small decrease in the mean Gini coefficient are relatively less important to the prediction made by the random forest model.

Figure 3, Variable importance plot for the arterial phase of the random forest model. The plot details the relative importance of each of the quantitative texture parameters (which is listed on the y-axis of the plot) to the model's accuracy. The x-axis is the mean decrease in the Gini coefficient that results when that variable is included in the model.

Figure 4, Variable importance plot for the venous phase of the random forest model. The plot details the relative importance of each of the quantitative texture parameters (which is listed on the y-axis of the plot) to the model's accuracy. The x-axis is the mean decrease in the Gini coefficient that results when that variable is included in the model.

Figure 5, Variable importance plot for the delayed phase of the random forest model. The plot details the relative importance of each of the quantitative texture parameters (which is listed on the y-axis of the plot) to the model's accuracy. The x-axis is the mean decrease in the Gini coefficient that results when that variable is included in the model.

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Performance of Model Using Validation Data Set

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Table 3

Confusion Matrices Detailing the Accuracy of the Random Forest Models when Analyzing the 19 Validation Cases

Oncocytoma Clear Cell RCC Papillary RCC Simple Cyst Sensitivity (%) Specificity (%) Total Class Error (%) 3-Phase Oncocytoma 18 3 0 0 85.7 97.8 14.3 Clear Cell RCC 2 29 0 0 93.5 96.3 6.5 Papillary RCC 0 0 36 0 100 100 0.0 Simple Cyst 0 0 0 25 100 100 0.0 Arterial Oncocytoma 17 4 0 0 81.0 97.8 19.0 Clear Cell RCC 2 29 0 0 93.5 95.1 6.5 Papillary RCC 0 0 36 0 100 100 0.0 Simple Cyst 0 0 0 25 100 100 0.0 Venous Oncocytoma 19 2 0 0 90.5 73.9 9.5 Clear Cell RCC 24 7 0 0 22.6 97.6 77.4 Papillary RCC 0 0 36 0 100 97.4 0.0 Simple Cyst 0 0 2 23 92.0 100 8.0 Delayed Oncocytoma 18 3 0 0 85.7 83.7 14.3 Clear Cell RCC 11 15 5 0 48.4 93.9 51.6 Papillary RCC 4 2 30 0 83.3 93.5 16.7 Simple Cyst 0 0 0 25 100 100 0.0

RCC, renal cell carcinoma.

The results are presented on a per-slice basis. The true pathologic diagnosis is represented by the heading for each row, whereas the classification provided by the model is represented by the heading for each column.

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Discussion

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Conclusions

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Acknowledgements

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Academic Radiology, Volume 21, Issue 12
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