Optimal and novel imaging of the adrenal glands

Navin, Patrick J.; Moynagh, Michael R.

doi:10.1097/med.0000000000000730

Cited by 1 publication

(1 citation statement)

References 58 publications

(65 reference statements)

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“…Researchers are exploring the far-reaching applications of AI in various clinical practices. The use of AI in CT analysis of adrenal nodules has been recently described [27] and could be very useful in two ways. First, in the case of identification of a nodule suspect of PCC (during abdominal imaging performed for another reason), texture analysis can be useful in identifying the presence of a hormonal hypersecretion, ensuring prompt referral of the patient to an endocrinologist (directly from the radiological report of the CT).…”

Section: Discussionmentioning

confidence: 99%

CT Texture Analysis of Adrenal Pheochromocytomas: A Pilot Study

et al. 2023

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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.

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Section: Discussionmentioning

confidence: 99%