Machine Learning-Based Texture Analysis in the Characterization of Cortisol Secreting vs. Non-Secreting Adrenocortical Incidentalomas in CT Scan

Maggio, Roberta; Messina, Filippo; D’Arrigo, Benedetta; Maccagno, Giacomo; Lardo, Pina; Palmisano, Claudia; Poggi, Maurizio; Monti, Salvatore; Matarazzo, Iolanda; Laghi, Andrea; Pugliese, G.; Stigliano, Antonio

doi:10.3389/fendo.2022.873189

Cited by 5 publications

(1 citation statement)

References 28 publications

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“…Second, the patients' clinical information, including clinical symptoms related to adrenal gland tumors and hormone secretion levels, were not included in this study. As reported in a previous human study that demonstrated the feasibility of differentiating functional and non-functional adrenal incidentalomas by CT texture analysis, different results could be obtained depending on whether the adrenal gland tumor is functional ( 47 , 48 ). Further studies on the differences in texture features between functional and non-functional adrenal masses would have clinical value.…”

Section: Discussionmentioning

confidence: 93%

Feasibility study of computed tomography texture analysis for evaluation of canine primary adrenal gland tumors

Lee,

Goh,

Jang

et al. 2023

Front. Vet. Sci.

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ObjectiveThis study aimed to investigate the feasibility of computed tomography (CT) texture analysis for distinguishing canine adrenal gland tumors and its usefulness in clinical decision-making.Materials and methodsThe medical records of 25 dogs with primary adrenal masses who underwent contrast CT and a histopathological examination were retrospectively reviewed, of which 12 had adenomas (AAs), 7 had adenocarcinomas (ACCs), and 6 had pheochromocytomas (PHEOs). Conventional CT evaluation of each adrenal gland tumor included the mean, maximum, and minimum attenuation values in Hounsfield units (HU), heterogeneity of the tumor parenchyma, and contrast enhancement (type, pattern, and degree), respectively, in each phase. In CT texture analysis, precontrast and delayed-phase images of 18 adrenal gland tumors, which could be applied for ComBat harmonization were used, and 93 radiomic features (18 first-order and 75 second-order statistics) were extracted. Then, ComBat harmonization was applied to compensate for the batch effect created by the different CT protocols. The area under the receiver operating characteristic curve (AUC) for each significant feature was used to evaluate the diagnostic performance of CT texture analysis.ResultsAmong the conventional features, PHEO showed significantly higher mean and maximum precontrast HU values than ACC (p < 0.05). Eight second-order features on the precontrast images showed significant differences between the adrenal gland tumors (p < 0.05). However, none of them were significantly different between AA and PHEO, or between precontrast images and delayed-phase images. This result indicates that ACC exhibited more heterogeneous and complex textures and more variable intensities with lower gray-level values than AA and PHEO. The correlation, maximal correlation coefficient, and gray level non-uniformity normalized were significantly different between AA and ACC, and between ACC and PHEO. These features showed high AUCs in discriminating ACC and PHEO, which were comparable or higher than the precontrast mean and maximum HU (AUC = 0.865 and 0.860, respectively).ConclusionCanine primary adrenal gland tumor differentiation can be achieved with CT texture analysis on precontrast images and may have a potential role in clinical decision-making. Further prospective studies with larger populations and cross-validation are warranted.

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Section: Discussionmentioning

confidence: 93%

Feasibility study of computed tomography texture analysis for evaluation of canine primary adrenal gland tumors

Lee,

Goh,

Jang

et al. 2023

Front. Vet. Sci.

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The current and upcoming era of radiomics in phaeochromocytoma and paraganglioma

Tüdös,

Veverková,

Baxa

et al. 2024

Best Practice & Research Clinical Endocrinology & Metab

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Patient classification and attribute assessment based on machine learning techniques in the qualification process for surgical treatment of adrenal tumours

Wielogórska-Partyka,

Adamski,

Siewko

et al. 2024

Sci Rep

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Adrenal gland incidentaloma is frequently identified through computed tomography and poses a common clinical challenge. Only selected cases require surgical intervention. The primary aim of this study was to compare the effectiveness of selected machine learning (ML) techniques in proper qualifying patients for adrenalectomy and to identify the most accurate algorithm, providing a valuable tool for doctors to simplify their therapeutic decisions. The secondary aim was to assess the significance of attributes for classification accuracy. In total, clinical data were collected from 33 patients who underwent adrenalectomy. Histopathological assessments confirmed the proper selection of 21 patients for surgical intervention according to the guidelines, with accuracy reaching 64%. Statistical analysis showed that Supported Vector Machines (linear) were significantly better than the baseline (p < 0.05), with accuracy reaching 91%, and imaging features of the tumour were found to be the most crucial attributes. In summarise, ML methods may be helpful in qualifying patients for adrenalectomy.

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Machine Learning-Based Texture Analysis in the Characterization of Cortisol Secreting vs. Non-Secreting Adrenocortical Incidentalomas in CT Scan

Cited by 5 publications

References 28 publications

Feasibility study of computed tomography texture analysis for evaluation of canine primary adrenal gland tumors

Feasibility study of computed tomography texture analysis for evaluation of canine primary adrenal gland tumors

The current and upcoming era of radiomics in phaeochromocytoma and paraganglioma

Patient classification and attribute assessment based on machine learning techniques in the qualification process for surgical treatment of adrenal tumours

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