Reliability of adrenal computed tomography in predicting the functionality of adrenal incidentaloma

Al-Waeli, Dheyaa Kadhim; Mansour, Abbas Ali; Haddad, Nazar S.

doi:10.4103/npmj.npmj_156_19

Cited by 5 publications

(4 citation statements)

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“…In contrast, a lesion with an HU value of more than 20 and with rapid washout of less than 50% is considered to be PC, adrenal carcinoma, or metastasis (1,30). Studies have reported that 87% to 100% of patients with PC have a CT value of more than 10 HU and an absolute percentage washout of less than 60% (1,6,31). Al-Waeli et al (6) analyzed the CT characteristics of 38 patients with adrenal incidentaloma.…”

Section: Discussionmentioning

confidence: 99%

“…Studies have reported that 87% to 100% of patients with PC have a CT value of more than 10 HU and an absolute percentage washout of less than 60% (1,6,31). Al-Waeli et al (6) analyzed the CT characteristics of 38 patients with adrenal incidentaloma. They mentioned that native HU was the most significant radiological parameter for predicting the functionality of CS and PC, and that the maximum diameter was significant for the differentiation of PC and HA.…”

Section: Discussionmentioning

confidence: 99%

“…Researchers have evaluated the reliability of radiological parameters of CT images in predicting the functionality of hormonally active tumors. Previous studies have assessed the function of these tumors through different parameters, such as the native attenuation value in Hounsfield units (HU), maximum diameter, absolute percentage washout (which refers to the characteristics of contrast media enhancement and de-enhancement), and heterogeneity (2,6,7). Recent developments in artificial intelligence (AI), especially deep learning, have been promoted extensively for application in a various quantification and distinction tasks of lesions in contrast CT imaging.…”

Section: Introductionmentioning

confidence: 99%

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Artificial intelligence with a deep learning network for the quantification and distinction of functional adrenal tumors based on contrast-enhanced CT images

Alimu¹,

Chen²,

Han³

et al. 2023

Quant Imaging Med Surg

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Background: Functional adrenal tumors (FATs) are mainly diagnosed by biochemical analysis. Traditional imaging tests have limitations and cannot be used alone to diagnose FATs. In this study, we aimed to establish an artificially intelligent diagnostic model based on computed tomography (CT) images to distinguish different types of FATs. Methods: A cohort study of 375 patients diagnosed with hyperaldosteronism (HA), Cushing's syndrome (CS), and pheochromocytoma in our center between March 2015 and June 2020 was conducted. Retrospectively, patients were randomly divided into three data sets: the training set (270 cases), the testing set (60 cases), and the retrospective trial set (45 cases). An artificially intelligent diagnostic model based on CT images was established by transferring data from the training set into the deep learning network. The testing set was then used to evaluate the accuracy of the model compared to that of physicians' judgments. The retrospective trial set was used to evaluate the quantification and distinction performance. Results: The deep learning model achieved an average area under the receiver operating characteristic (ROC) curve (AUC) of 0.915, and the AUCs in all three FAT types were greater than 0.882. The AUC of the model tested on the retrospective dataset reached above 0.849. In the quantitative evaluation of tumor lesion area recognition, the diagnostic model also obtained a segmentation Dice coefficient of 0.69. With the help of the proposed model, clinicians reached 92.5% accuracy in distinguishing FATs, compared to 80.6% accuracy when using only their judgment (P<0.05). Conclusions: The result of our study shows that the diagnostic model based on a deep learning network can distinguish and quantify three common FAT types based on texture features of contrast-enhanced CT images. The model can quantify and distinguish functional tumors without any endocrine tests and can assist clinicians in the diagnostic procedure.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Artificial intelligence with a deep learning network for the quantification and distinction of functional adrenal tumors based on contrast-enhanced CT images

Alimu¹,

Chen²,

Han³

et al. 2023

Quant Imaging Med Surg

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“…Therefore, in our study, based on native HU values, the secretory activity of adenoma cannot be determined. Only one study in a small series of patients reported that native HU value was the most significant radiological parameter in predicting the functionality of adenoma [26].…”

Section: Discussionmentioning

confidence: 99%

Can MDCT Enhancement Patterns Be Helpful in Differentiating Secretory from Non-Functional Adrenal Adenoma?

Kocic,

Vukomanovic,

Djukic

et al. 2023

Medicina

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Background and Objectives: Primary adrenal tumors (AT) are a heterogeneous group of neoplasms due to their functional heterogeneity, which results in the diverse clinical presentation of these tumors. The purpose of this study was to examine cross-sectional imaging characteristics using multi-detector computed tomography (MDCT) to provide insight into the lesion characterization and functional status of these tumors. The radionuclide imaging using Technetium-99m radiolabeled hydrazinonicotinylacid-d-phenylalanyl1-tyrosine3-octreotide (99mTc-HYNIC-TOC), was also used in the diagnostic evaluation of these tumors. Materials and Methods: This cross-sectional study included 50 patients with confirmed diagnoses of AT (21 hormone-secreting and 29 non-functional) at the University Clinical Center, Kragujevac, Serbia, during the 2019–2022 year period. The morphological and dynamic characteristics using MDCT were performed, using qualitative, semi-quantitative, and quantitative analysis. Absolute washout (APW) and relative washout (RPW) values were also calculated. A semi-quantitative analysis of all visual findings with 99mTc-HYNIC-TOC was performed to compare the tumor to non-tumor tracer uptake. Results: A statistically significant difference was found in the MDCT values in the native phase (p < 0.05), the venous phase (p < 0.05), and the delayed phase (p < 0.001) to detect the existence of adrenal tumors. Most of these functional adrenocortical lesions (n = 44) can be differentiated using the delayed phase (p < 0.05), absolute percentage washout (APW) (p < 0.05), and relative percentage washout (RPW) (p < 0.001). Furthermore, 99mTc-HYNIC-TOC could have a high diagnostic yield to detect adrenal tumor existence (p < 0.001). There is a positive correlation between radionuclide imaging scan and APW to detect all AT (p < 0.01) and adrenocortical adenomas as well (p < 0.01). Conclusions: The results can be very helpful in a diagnostic algorithm to quickly and precisely diagnose the expansive processes of the adrenal glands, as well as to learn about the advantages and limitations of the mentioned imaging modalities.

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The good, the bad, and the ugly: uncommon CT appearances of pheochromocytoma

et al. 2022

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Reliability of adrenal computed tomography in predicting the functionality of adrenal incidentaloma

Cited by 5 publications

References 0 publications

Artificial intelligence with a deep learning network for the quantification and distinction of functional adrenal tumors based on contrast-enhanced CT images

Artificial intelligence with a deep learning network for the quantification and distinction of functional adrenal tumors based on contrast-enhanced CT images

Can MDCT Enhancement Patterns Be Helpful in Differentiating Secretory from Non-Functional Adrenal Adenoma?

The good, the bad, and the ugly: uncommon CT appearances of pheochromocytoma

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