CT Texture Analysis of Adrenal Pheochromocytomas: A Pilot Study.
Filippo Crimi'Elena AgostiniAlessandro TonioloFrancesca TorresanMaurizio IacoboneIrene TizianelCarla ScaroniEmilio QuaiaCristina CampiFilippo CeccatoPublished in: Current oncology (Toronto, Ont.) (2023)
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.
Keyphrases
- contrast enhanced
- diffusion weighted
- magnetic resonance imaging
- dual energy
- computed tomography
- magnetic resonance
- data analysis
- diffusion weighted imaging
- end stage renal disease
- ejection fraction
- newly diagnosed
- chronic kidney disease
- image quality
- prognostic factors
- peritoneal dialysis
- metabolic syndrome
- squamous cell carcinoma
- minimally invasive
- positron emission tomography
- patient reported outcomes
- skeletal muscle
- coronary artery bypass
- coronary artery disease
- machine learning
- optical coherence tomography
- lymph node metastasis
- artificial intelligence
- electronic health record