Machine learning‑based texture analysis for diferentiation of radiologically indeterminate small adrenal tumors on adrenal protocol CT scans

Ahmed W. Moawad, Ayahallah Ahmed, David T. Fuentes, John D. Hazle, Mouhammed A. Habra, Khaled M. Elsayes - Department of Body Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, USA ; Department of Radiology, Trinity Health Mid-Atlantic, Mercy Catholic Medical Center, Darby, PA, USA ; Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Houston, TX 77030, USA ; Department of Endocrine Neoplasia and Hormonal Disorders, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

To evaluate the ability of radiomic feature extraction and a machine learning algorithm to diferentiate between benign
and malignant indeterminate adrenal lesions on contrast-enhanced computed tomography (CT) studies.

Adrenal “incidentalomas” are adrenal lesions that are accidentally discovered during workup not related to the adrenal glands; they have an incidence as high as 5%. Small adrenal incidentalomas (<4 cm) with high attenuation values on pre-contrast CT(>10 HU) need further evaluation to calculate the absolute percentage of washout (APW). If the APW is<60%, these lesions are considered non-adenomas and commonly classifed as indeterminate adrenal lesions. Further workup for indeterminate lesions includes more complicated and expensive radiological studies or invasive procedures like biopsy or surgical resection.

How Amira-Avizo Software is used

Manual segmentations were performed in pre-contrast,
venous, and delayed phases using the segmentation tool
available in Amira software to delineate the adrenal lesion.