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Current Medical Imaging

Editor-in-Chief

ISSN (Print): 1573-4056
ISSN (Online): 1875-6603

Research Article

A CT Radiomics Analysis of the Adrenal Masses: Can We Discriminate Lipid-poor Adenomas from the Pheochromocytoma and Malignant Masses?

Author(s): Bökebatur Ahmet Raşit Mendi* and Mutlu Gülbay

Volume 19, Issue 9, 2023

Published on: 13 December, 2022

Article ID: e151122210915 Pages: 13

DOI: 10.2174/1573405619666221115124352

Price: $65

Abstract

Aims: The aim of the study is to demonstrate a non-invasive alternative method to aid the decision making process in the management of adrenal masses.

Background: Lipid-poor adenomas constitute 30% of all adrenal adenomas. When discovered incidentally, additional dynamic adrenal examinations are required to differentiate them from an adrenal malignancy or pheochromocytoma.

Objective: In this retrospective study, we aimed to discriminate lipid-poor adenomas from other lipidpoor adrenal masses by using radiomics analysis in single contrast phase CT scans.

Materials and Methods: A total of 38 histologically proven lipid-poor adenomas (Group 1) and 38 cases of pheochromocytoma or malignant adrenal mass (Group 2) were included in this retrospective study. Lesions were segmented volumetrically by two independent authors, and a total of 63 sizes, shapes, and first- and second-order parameters were calculated. Among these parameters, a logit-fit model was produced by using 6 parameters selected by the LASSO (least absolute shrinkage and selection operator) regression. The model was cross-validated with LOOCV (leave-one-out crossvalidation) and 1000-bootstrap sampling. A random forest model was also generated in order to use all parameters without the risk of multicollinearity. This model was examined with the nested crossvalidation method.

Results: Sensitivity, specificity, accuracy and AUC were calculated in test sets as 84.2%, 81.6%, 82.9% and 0.829 in the logit fit model and 91%, 80%, 82.8% and 0.975 in the RF model, respectively.

Conclusion: Predictive models based on radiomics analysis using single-phase contrast-enhanced CT can help characterize adrenal lesions.

Graphical Abstract

[1]
Blake MA, Cronin CG, Boland GW. Adrenal Imaging. AJR Am J Roentgenol 2010; 194(6): 1450-60.
[http://dx.doi.org/10.2214/AJR.10.4547] [PMID: 20489083]
[2]
Dunnick NR, Korobkin M. Imaging of adrenal incidentalomas: Current status. AJR Am J Roentgenol 2002; 179(3): 559-68.
[http://dx.doi.org/10.2214/ajr.179.3.1790559] [PMID: 12185019]
[3]
Lam KY, Lo CY. Metastatic tumours of the adrenal glands: A 30-year experience in a teaching hospital. Clin Endocrinol 2002; 56(1): 95-101.
[http://dx.doi.org/10.1046/j.0300-0664.2001.01435.x] [PMID: 11849252]
[4]
Mitchell IC, Nwariaku FE. Adrenal masses in the cancer patient: Surveillance or excision. Oncologist 2007; 12(2): 168-74.
[http://dx.doi.org/10.1634/theoncologist.12-2-168] [PMID: 17296812]
[5]
Blake MA, Holalkere NS, Boland GW. Imaging techniques for adrenal lesion characterization. Radiol Clin North Am 2008; 46(1): 65-78, vi.
[http://dx.doi.org/10.1016/j.rcl.2008.01.003] [PMID: 18328880]
[6]
Lattin GE Jr, Sturgill ED, Tujo CA, et al. From the radiologic pathology archives: Adrenal tumors and tumor-like conditions in the adult: radiologic-pathologic correlation. Radiographics 2014; 34(3): 805-29.
[http://dx.doi.org/10.1148/rg.343130127] [PMID: 24819798]
[7]
Park BK, Kim B, Ko K, Jeong SY, Kwon GY. Adrenal masses falsely diagnosed as adenomas on unenhanced and delayed contrast-enhanced computed tomography: Pathological correlation. Eur Radiol 2006; 16(3): 642-7.
[http://dx.doi.org/10.1007/s00330-005-0017-0] [PMID: 16215735]
[8]
Peña CS, Boland GWL, Hahn PF, Lee MJ, Mueller PR. Characterization of indeterminate (lipid-poor) adrenal masses: Use of washout characteristics at contrast-enhanced CT. Radiology 2000; 217(3): 798-802.
[http://dx.doi.org/10.1148/radiology.217.3.r00dc29798] [PMID: 11110946]
[9]
Caoili EM, Korobkin M, Francis IR, Cohan RH, Dunnick NR. Delayed enhanced CT of lipid-poor adrenal adenomas. AJR Am J Roentgenol 2000; 175(5): 1411-5.
[http://dx.doi.org/10.2214/ajr.175.5.1751411] [PMID: 11044054]
[10]
Caoili EM, Korobkin M, Francis IR, et al. Adrenal masses: Characterization with combined unenhanced and delayed enhanced CT. Radiology 2002; 222(3): 629-33.
[http://dx.doi.org/10.1148/radiol.2223010766] [PMID: 11867777]
[11]
Johnson PT, Horton KM, Fishman EK. Adrenal mass imaging with multidetector CT: Pathologic conditions, pearls, and pitfalls. Radiographics 2009; 29(5): 1333-51.
[http://dx.doi.org/10.1148/rg.295095027] [PMID: 19755599]
[12]
Varghese BA, Cen SY, Hwang DH, Duddalwar VA. Texture analysis of imaging: What radiologists need to know. AJR Am J Roentgenol 2019; 212(3): 520-8.
[http://dx.doi.org/10.2214/AJR.18.20624] [PMID: 30645163]
[13]
Koçak B, Durmaz ES, Ateş E, Kılıçkesmez O. Radiomics with artificial intelligence: A practical guide for beginners. Diagn Interv Radiol 2019; 25(6): 485-95.
[http://dx.doi.org/10.5152/dir.2019.19321] [PMID: 31650960]
[14]
Radiomics features Release v3.0.1.post4+gad5b2de. PyRadiomics community. 2021. Available from: https://pyradiomics.readthedocs. io/en/latest/features.html
[15]
Free J, Eggermont F, Derikx L, et al. The effect of different CT scanners, scan parameters and scanning setup on Hounsfield units and calibrated bone density: A phantom study. Biomed Phys Eng Express 2018; 4(5): 055013.
[http://dx.doi.org/10.1088/2057-1976/aad66a]
[16]
Gallardo-Estrella L, Lynch DA, Prokop M, et al. Normalizing computed tomography data reconstructed with different filter kernels: Effect on emphysema quantification. Eur Radiol 2016; 26(2): 478-86.
[http://dx.doi.org/10.1007/s00330-015-3824-y] [PMID: 26002132]
[17]
Alves AFF, Miranda JRA, Reis F, et al. Inflammatory lesions and brain tumors: Is it possible to differentiate them based on texture features in magnetic resonance imaging? J Venom Anim Toxins Incl Trop Dis 2020; 26: e20200011.
[18]
Vittinghoff E, McCulloch CE. Relaxing the rule of ten events per variable in logistic and cox regression. Am J Epidemiol 2007; 165(6): 710-8.
[http://dx.doi.org/10.1093/aje/kwk052] [PMID: 17182981]
[19]
Vickers AJ, van Calster B, Steyerberg EW. A simple, step-by-step guide to interpreting decision curve analysis. Diagn Progn Res 2019; 3(1): 18.
[http://dx.doi.org/10.1186/s41512-019-0064-7] [PMID: 31592444]
[20]
Nested Cross-Validation with 'glmnet' and 'caret'. 2022. Available from: https://cran.r-project.org/web/packages/nestedcv/nestedcv.pdf
[21]
Vabalas A, Gowen E, Poliakoff E, Casson AJ. Machine learning algorithm validation with a limited sample size. PLoS One 2019; 14(11): e0224365.
[http://dx.doi.org/10.1371/journal.pone.0224365]
[22]
Zulpe N, Pawar V. GLCM textural features for brain tumor classification. Int J Comput Sci 2012; 9(3): 354.
[23]
Ho LM, Samei E, Mazurowski MA, et al. Can texture analysis be used to distinguish benign from malignant adrenal nodules on unenhanced CT, contrast-enhanced CT, or in-phase and opposed-phase MRI? AJR Am J Roentgenol 2019; 212(3): 554-61.
[http://dx.doi.org/10.2214/AJR.18.20097] [PMID: 30620676]
[24]
Yu H, Parakh A, Blake M, McDermott S. Texture analysis as a radiomic marker for differentiating benign from malignant adrenal tumors. J Comput Assist Tomogr 2020; 44(5): 766-71.
[http://dx.doi.org/10.1097/RCT.0000000000001051] [PMID: 32842071]
[25]
Torresan F, Crimì F, Ceccato F, et al. Radiomics: A new tool to differentiate adrenocortical adenoma from carcinoma. BJS Open 2021; 5(1): zraa061.
[http://dx.doi.org/10.1093/bjsopen/zraa061]
[26]
Elmohr MM, Fuentes D, Habra MA, et al. Machine learning-based texture analysis for differentiation of large adrenal cortical tumours on CT. Clin Radiol 2019; 74(10): 818.e1-7.
[http://dx.doi.org/10.1016/j.crad.2019.06.021] [PMID: 31362884]
[27]
Crimì F, Quaia E, Cabrelle G, et al. Diagnostic accuracy of CT texture analysis in adrenal masses: A systematic review. Int J Mol Sci 2022; 23(2): 637.
[http://dx.doi.org/10.3390/ijms23020637] [PMID: 35054823]
[28]
Yi X, Guan X, Zhang Y, et al. Radiomics improves efficiency for differentiating subclinical pheochromocytoma from lipid-poor adenoma: A predictive, preventive and personalized medical approach in adrenal incidentalomas. EPMA J 2018; 9(4): 421-9.
[http://dx.doi.org/10.1007/s13167-018-0149-3] [PMID: 30538793]
[29]
Shi B, Zhang GMY, Xu M, Jin ZY, Sun H. Distinguishing metastases from benign adrenal masses: what can CT texture analysis do? Acta Radiol 2019; 60(11): 1553-61.
[http://dx.doi.org/10.1177/0284185119830292] [PMID: 30799636]
[30]
Chen HL, Liu K. The SAFER score in predicting in-hospital cardiac arrest: A decision curve analysis. Resuscitation 2018; 128: e1-2.
[http://dx.doi.org/10.1016/j.resuscitation.2018.03.015] [PMID: 29907378]
[31]
Kerr KF, Brown MD, Zhu K, Janes H. Assessing the clinical impact of risk prediction models with decision curves: Guidance for correct interpretation and appropriate use. J Clin Oncol 2016; 34(21): 2534-40.
[http://dx.doi.org/10.1200/JCO.2015.65.5654] [PMID: 27247223]

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