Feasibility study of computed tomography texture analysis for evaluation of canine primary adrenal gland tumors

Sibylle Maria Kneissl, Silvia Burti, Kyungsoo Lee, Jinhyong Goh, Jaeyoung Jang, Jeongyeon Hwang, Jungmin Kwak, Jaehwan Kim, Kidong Eom

Introduction

Canine primary adrenal gland tumors are rare but can be classified by origin, behavior, and functionality. Adenoma and adenocarcinoma are common, followed by pheochromocytoma. CT plays a crucial role in preoperative tumor diagnosis, but has limitations in distinguishing tumor types. Texture analysis is a promising quantitative method for evaluating tumor heterogeneity. It has been used in human medicine but not extensively in veterinary medicine, particularly in the differentiation of primary adrenal gland masses. This study aimed to characterize the texture features of canine primary adrenal gland tumors using CT and assess its diagnostic performance using the ROC curve. The classification of tumors by origin and functionality, as well as the potential of CT texture analysis as a method for differentiating malignant from benign adrenal gland tumors, were investigated. The study underscores the need for further investigation of CT texture analysis in canine adrenal gland tumors.

CT image acquisition

The CT images for the study were acquired from various multi-detector CT scanners using standardized abdominal imaging protocols. The specific scanner models, slice thickness, tube voltage (kVP), and mAs are detailed in the Supplementary Table. Postcontrast images were obtained either using the bolus tracking technique or at specific time intervals after the injection of the contrast medium, including arterial phase (∼20 s), portal phase (∼40 s), and delayed phase (70 s to 2 min post-injection). Due to the retrospective nature of the study, detailed protocols such as the manufacturer of the contrast medium, dosage, injection rate, and specific scanner parameters were not available for analysis.

Analysis

The study involved retrospective review of CT images of canine adrenal gland tumors by a single investigator using Digital Imaging and Communications in Medicine (DICOM) viewing software. The evaluation, supervised by a professor, was blinded to the histopathological diagnosis. Measurements included maximal diameter on short and long axes, and mean, maximum, and minimum attenuation values in Hounsfield units (HU). Circular or ovoid regions of interest (ROIs) were drawn on the transverse plane, excluding calcification and macroscopic fat. ROIs were placed consistently on pre-contrast and post-contrast images, and the difference in mean attenuation value on pre-contrast images was calculated on each post-contrast image. This conventional CT analysis aimed to characterize the tumors. The methodology employed in this study involved meticulous and comprehensive quantitative assessment of CT images, providing important insights into the characterization of canine adrenal gland tumors.

Qualitative features

The qualitative features evaluated in the study included location (right or left), shape (round, oval, lobulated), mass contour (smooth, irregular), contrast enhancement type (homogeneous, heterogeneous), pattern (stable, progressive, washout), degree of enhancement (none, minimal, mild, moderate, intense), rim enhancement, intratumoral calcification, and adhesion or invasion of adjacent vessels. The enhancement pattern was categorized as stable (minimal enhancement), progressive (gradual increase), or washout (peak followed by reduction). Degree of enhancement was assessed using mean HU differences. A 7-point CT grading system was used to evaluate vessel adhesion or invasion, with higher grades indicating a stronger possibility of invasion. These qualitative features were instrumental in characterizing canine adrenal gland tumors and assessing their potential malignancy, aiding in the differentiation between benign and malignant tumors based on their CT features.

Lesion segmentation and texture analysis

The study performed texture analysis on canine adrenal lesions using pre-contrast and delayed-phase CT images with 3D Slicer software. Eighteen out of 25 dogs were included due to variations in CT scanners and protocols that couldn't be harmonized using ComBat method. The method proved effective in removing batch effects while preserving texture characteristics. Semi-automatic segmentation and feature extraction were conducted on the adrenal lesions, extracting 18 first-order and 75 second-order statistics using various matrices. The process was repeated by a single investigator, blinded to histopathologic results, and supervised by a radiologist. Additionally, the ComBat harmonization method was applied to the extracted texture features. This technique addresses the interscanner variability in radiomic features and shows promise for harmonizing data in multicenter radiomic studies. However, it's essential to consider the consistency of image acquisition and reconstruction for reproducibility of radiomic features.

Statistical analysis

In the statistical analysis of the study, qualitative and quantitative CT features were evaluated using various tests. Qualitative features were assessed using the Fisher exact test, while quantitative features underwent normality testing and one-way analysis of variance to compare differences between tumor types. The intraobserver intraclass correlation coefficients for radiomic features were >0.90, indicating good consistency in feature extraction. Radiomic features were compared between tumor types using the Kruskal-Wallis H test. The diagnostic performance of differentiating tumor types was evaluated using the area under the ROC curve (AUC), with values classified as fail, poor, fair, good, or excellent. The maximum Youden index was used to determine the cut-off value for features with the highest AUC. Diagnostic parameters such as sensitivity, specificity, PPV, and NPV were obtained using the cut-off value. MedCalc and SPSS software were used for statistical analyses, with a significance level of p<0.05. Overall, the histopathological diagnosis served as the gold standard for reference in the study's statistical analyses.

Results

The study included a total of 40 dogs, with 25 meeting the inclusion criteria. Exclusions were made for cases without histopathological results, benign lesions, loss of CT data, and adrenal mass rupture. Of the 25 included dogs, 18 were involved in the texture analysis. Tumors were diagnosed as adrenocortical adenoma (AA) in 48% of cases, adrenocortical carcinoma (ACC) in 28% of cases, and pheochromocytoma (PHEO) in 24% of cases, based on surgical excisional biopsy findings. Some cases of AA were found to have concurrent nodular hyperplasia and extramedullary hematopoiesis. Capsular invasion was confirmed in only one of the PHEOs. The study involved various dog breeds, with no significant differences in age and sex between the tumor types.

Quantitative Features

The study found that in conventional CT evaluation of primary adrenal gland tumors, the mean and maximum Hounsfield units (HUmean and HUmax) on pre-contrast images were significantly higher in pheochromocytomas (PHEO) than in adrenocortical carcinomas (ACC). No significant differences were observed between adrenocortical adenomas (AA) and ACC, or between AA and PHEO. The HUmax in the arterial phase was notably higher in PHEO compared to other neoplasms, although this difference did not reach statistical significance. The study also noted that the mean HU difference and maximum diameter did not show significant correlation with tumor type. Additionally, ACC exhibited the smallest size among the three tumor types. Overall, the study's findings on the quantitative CT features of adrenal gland tumors highlight distinct characteristics that may aid in their differentiation, particularly between PHEO and ACC.

Qualitative features

The qualitative features of canine primary adrenal gland tumors were evaluated using conventional CT evaluation. The study found that none of the features were significantly associated with the tumor type. In the AA group, all cases showed smooth tumor margins, but the difference was not statistically significant between the tumor types. No direct vessel invasion or compression was observed in any case. Most tumors had a moderate possibility of tumor vascular invasion or adhesion. Additionally, the majority of tumors in the pre-contrast phase and all tumors in the portal and delayed phases showed heterogeneous enhancement. The washout pattern of contrast enhancement was identified in only 1 case, and progressive patterns were most frequently observed regardless of the tumor type. These findings provide insights into the qualitative features of canine adrenal gland tumors, with implications for their classification by origin and functionality using CT evaluation.

CT texture analysis

The study compared CT texture features of primary adrenal gland tumors to differentiate tumor types and identify potential malignancy. Eight second-order statistics from pre-contrast images showed significant differences between tumor types, while no significant features were identified in the delayed phase. Features such as correlation, maximal correlation coefficient, and gray-level non-uniformity normalized were notable for showing significant differences between adrenocortical adenoma (AA) and adrenocortical carcinoma (ACC), as well as between ACC and pheochromocytoma (PHEO). However, no significant differences were found between AA and PHEO. The study demonstrated the potential of CT texture analysis in differentiating tumor types, especially between AA, ACC, and PHEO. These findings suggest that CT texture analysis may be a promising method for characterizing primary adrenal gland tumors and distinguishing malignant from benign tumors, particularly in the pre-contrast phase.

Evaluation of diagnostic performance

The research paper evaluated the diagnostic performance of CT texture features for differentiating adrenal gland tumors. The HUmean and HUmax on the pre-contrast image demonstrated high area under the curve (AUC) values for diagnosing adrenocortical carcinoma (ACC) and pheochromocytoma (PHEO). The AUC values for distinguishing ACC from other adrenal gland tumors were high, with the maximum correlation coefficient (MCC) showing the highest AUC (0.969). Additionally, radiomic features exhibited AUC values indicative of fair to excellent diagnostic performance for each tumor type. Specifically, run entropy showed the highest AUC (0.787) for discriminating adrenal adenoma (AA), while short‐long‐run high‐gray‐level emphasis (SLDGLE) showed the highest AUC (0.889) for discriminating PHEO. The study also provided cutoff values for differentiating ACC and PHEO, along with the radiomic features' corresponding AUC values. These findings demonstrate the potential of CT texture analysis in accurately characterizing and differentiating adrenal gland tumors.

Discussion

The study evaluated the feasibility of using CT texture analysis to differentiate adrenal gland tumors, including cortical AA, ACC, and PHEO. It identified several texture features on pre-contrast images with statistical significance, showing the potential of CT texture analysis to distinguish adrenal gland tumors. Notably, the study found significant differences in texture features between tumor types, particularly ACC and PHEO, indicating the potential of CT texture analysis in canine adrenal glands using pre-contrast imaging. The study acknowledged some limitations, including the small sample size and the need for further validation and model establishment in larger prospective studies. Overall, the findings suggest that CT texture analysis may be useful for distinguishing adrenal gland tumor types and aiding in clinical decision-making, highlighting the potential of this approach in canine primary adrenal gland tumor characterization.

Link to Article: Frontiers | Feasibility study of computed tomography texture analysis for evaluation of canine primary adrenal gland tumors (frontiersin.org)

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