Machine learning-based texture analysis for differentiation of large adrenal cortical tumours on CT. Issue 10 (October 2019)

• Record Type: Journal Article
• Authors: Elmohr, M.M.; Fuentes, D.; Habra, M.A.; Bhosale, P.R.; Qayyum, A.A.; Gates, E.; Morshid, A.I.; Hazle, J.D.; Elsayes, K.M.
• Is Part Of: Clinical radiology. Volume 74:Issue 10(2019)
• Page Start: 818.e1
• Page End: 818.e7
• Publication Date: 2019-10
• Subjects: Medical radiology -- Periodicals; Radiotherapy -- Periodicals; Radiotherapy -- Periodicals; Radiology -- Periodicals; Societies, Medical -- Periodicals; Medical radiology; Radiotherapy; Electronic journals; Periodicals; 616.0757
• DOI: 10.1016/j.crad.2019.06.021
• Languages: English
• ISSNs: 0009-9260

Abstract

Abstract : AIM: To compare the efficacy of computed tomography (CT) texture analysis and conventional evaluation by radiologists for differentiation between large adrenal adenomas and carcinomas. MATERIALS AND METHODS: Quantitative CT texture analysis was used to evaluate 54 histopathologically proven adrenal masses (mean size=5.9 cm; range=4.1–10 cm) from 54 patients referred to Anderson Cancer Center from January 2002 through April 2014. The patient group included 32 women (mean age at mass evaluatio n= 59 years) and 22 men (mean age at mass evaluatio n= 61 years). Adrenal lesions seen on precontrast and venous-phase CT images were labelled by three different readers, and the labels were used to generate intensity- and geometry-based textural features. The textural features and the attenuation values were considered as input values for a random forest-based classifier. Similarly, the adrenal lesions were classified by two different radiologists based on morphological criteria. Prediction accuracy and interobserver agreement were compared. RESULTS: The textural predictive model achieved a mean accuracy of 82%, whereas the mean accuracy for the radiologists was 68.5% ( p< 0 . 0001). The interobserver agreements between the predictive model and radiologists 1 and 2 were 0.44 ( p< 0 . 0005; 95% confidence interval [CI]: 0.25–0.62) and 0.47 ( p< 0.0005; 95% CI: 0.28–0.66), respectively. The Dice similarity coefficient between the readers' image labels was 0.875±0.04. CONCLUSION: CT texture analysis of large adrenal adenomas and carcinomas is likely to improve CT evaluation of adrenal cortical tumours. Highlights: Inter-reader variability between repeated segmentations is minimal. ACC showed higher median attenuation and more heterogeneity of the masses on CT texture analysis. Overall accuracy of the machine learning model was 82% compared to 68.5% for the conventional evaluation. Machine-learning based texture analysis may improve CT evaluation of adrenal cortical tumors.