CT Texture Analysis of Adrenal Pheochromocytomas: A Pilot Study

Abstract

Radiomics is a promising research field that combines big data analysis (from tissue texture analysis) with clinical questions. We studied the application of CT texture analysis in adrenal pheochromocytomas (PCCs) to define the correlation between the extracted features and the secretory pattern, the histopathological data, and the natural history of the disease. A total of 17 patients affected by surgically removed PCCs were retrospectively enrolled. Before surgery, all patients underwent contrast-enhanced CT and complete endocrine evaluation (catecholamine secretion and genetic evaluation). The pheochromocytoma adrenal gland scaled score (PASS) was determined upon histopathological examination. After a resampling of all CT images, the PCCs were delineated using LifeX software in all three phases (unenhanced, arterial, and venous), and 58 texture parameters were extracted for each volume of interest. Using the Mann–Whitney test, the correlations between the hormonal hypersecretion, the malignancy score of the lesion (PASS > 4), and texture parameters were studied. The parameters DISCRETIZED_HUpeak and GLZLM_GLNU in the unenhanced phase and GLZLM_SZE, CONVENTIONAL_HUmean, CONVENTIONAL_HUQ3, DISCRETIZED_HUmean, DISCRETIZED_AUC_CSH, GLRLM_HGRE, and GLZLM_SZHGE in the venous phase were able to differentiate secreting PCCs (p < 0.01), and the parameters GLZLM_GLNU in the unenhanced phase and GLRLM_GLNU and GLRLM_RLNU in the venous differentiated tumors with low and high PASS. CT texture analysis of adrenal PCCs can be a useful tool for the early identification of secreting or malignant tumors.

Application of a Radiomics Machine Learning Model for Differentiating Aldosterone-Producing Adenoma from Non-Functioning Adrenal Adenoma

“…Radiomics has been increasingly applied for diagnosing adrenal gland diseases in recent years; however, most studies have focused on its ability to identify benign, malignant, and low-fat tumors [13,33,34]. Relevant studies have been conducted on the function of preoperative adenomas in the field of pheochromocytoma [14,35,36]. However, only a few studies on the utility of radiomics in assessing the function of preoperative APA have been reported to date.…”

Image-Guided Precision Medicine in the Diagnosis and Treatment of Pheochromocytomas and Paragangliomas

“…An other predictive calculation model based on imaging characteristics on unenhanced CT, such as sharp-edged necrosis, unsharp necrosis, ring sign, and spherical shape, also had good specificity and sensitivity (80%/95%) for diagnosing PCCs [ 197 ]. Machine-learning-based quantitative texture analysis (QTA) could also differentiate subclinical PCCs from lipid-poor adenomas [ 198 , 199 ] or be a useful tool to early identify malignant PPCs subtypes from CE-CT scans [ 200 ]. Several anatomical biomarkers have also been proposed to improve tumor monitoring, and others are still being validated ( Table S3, Supplementary Material ) [ 201 ].…”

Editorial: Predictive tools in pheochromocytoma and paraganglioma

“…Radiomics uses image-based texture analysis from conventional imaging to provide quantitative parameters that may be useful to measure the heterogeneity of tumours ( 18 ), and has been proposed to differentiate between benign and malignant adrenal lesions ( 19 ). Texture analysis was studied in a small number of patients (17 with surgically confirmed PPGL) and some selected features were able to identify secreting or malignant tumours ( 20 ). Patients with germline pathogenic variants involved in multiple cellular processes (hypoxia, MAPK/ERK or WNT signaling) presented a particular clinical picture with head/neck or thoracic/abdominal PGLs, secreting or not, and the genotype was able to predict the clinical behaviour ( 21 ).…”