Risk prediction model establishment with tri-phasic CT image features for differential diagnosis of adrenal pheochromocytomas and lipid-poor adenomas: Grouping method

Niu, Zhiwei; Wang, Jian; Yang, Yang; He, Jie; Wang, Subo; Xie, Zongyu; Shao, Meihua; Zhu, Fu‐Gang

doi:10.3389/fendo.2022.925577

Cited by 3 publications

(2 citation statements)

References 24 publications

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“…It is known that the most challenging task is to differentiate between atypical adrenal adenomas and PHEOs. Previous studies have proposed different methods for their differentiation [17][18][19]. For example, a recent study proposed a formula combining cystic degeneration, attenuation values in CT, and the peak value of enhancement in the arterial and venous phases for the differentiation of both conditions, reaching an accuracy of 95% in the training cohort and 91% in the external validation population [17].…”

Section: Discussionmentioning

confidence: 99%

“…Previous studies have proposed different methods for their differentiation [17][18][19]. For example, a recent study proposed a formula combining cystic degeneration, attenuation values in CT, and the peak value of enhancement in the arterial and venous phases for the differentiation of both conditions, reaching an accuracy of 95% in the training cohort and 91% in the external validation population [17]. Nagayama et al [18] reported that a relative enhancement ratio of the venous phase to unenhanced CT of ≥210% allows an accurate differentiation between lipid-poor adenomas and PHEOs.…”

Section: Discussionmentioning

confidence: 99%

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An Integrated CT and MRI Imaging Model to Differentiate between Adrenal Adenomas and Pheochromocytomas

Araujo-Castro,

García Sanz,

Mínguez Ojeda

et al. 2023

Cancers

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Purpose: to perform an external validation of our predictive model to rule out pheochromocytoma (PHEO) based on unenhanced CT in a cohort of patients with PHEOs and adenomas who underwent adrenalectomy. Methods: The predictive model was previously developed in a retrospective cohort of 1131 patients presenting with adrenal lesions. In the present study, we performed an external validation of the model in another cohort of 214 patients with available histopathological results. Results: For the external validation, 115 patients with PHEOs and 99 with adenomas were included. Our previously described predictive model combining the variables of high lipid content and tumor size in unenhanced CT (AUC-ROC: 0.961) had a lower diagnostic accuracy in our current study population for the prediction of PHEO (AUC: 0.750). However, when we excluded atypical adenomas (with Hounsfield units (HU) > 10, n = 39), the diagnostic accuracy increased to 87.4%. In addition, in the whole cohort (including atypical adenomas), when MRI information was included in the model, the diagnostic accuracy increased to up to 85% when the variables tumor size, high lipid content in an unenhanced CT scan, and hyperintensity in the T2 sequence in MRI were included. The probability of PHEO was <0.3% for adrenal lesions <20 mm with >10 HU and without hyperintensity in T2. Conclusion: Our study confirms that our predictive model combining tumor size and lipid content has high reliability for the prediction of PHEO when atypical adrenal lesions are excluded. However, for atypical adrenal lesions with >10 HU in an unenhanced CT scan, MRI information is necessary for a proper exclusion of the PHEO diagnosis.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

An Integrated CT and MRI Imaging Model to Differentiate between Adrenal Adenomas and Pheochromocytomas

Araujo-Castro,

García Sanz,

Mínguez Ojeda

et al. 2023

Cancers

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CT-Based Radiomics Analysis of Different Machine Learning Models for Discriminating the Risk Stratification of Pheochromocytoma and Paraganglioma: A Multicenter Study

Zhou,

Zhan,

Zhao

et al. 2024

Academic Radiology

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Combined Nomogram for Differentiating Adrenal Pheochromocytoma from Large-Diameter Lipid-poor Adenoma Using Multiphase CT Radiomics and clinico-radiological features

Shan,

Zhang,

Zhang

et al. 2024

Preprint

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Background and Objective: Adrenal incidentalomas (AIs) are predominantly adrenal adenomas (80%), with a smaller proportion (7%) being pheochromocytomas(PHEO). Adenomas are typically non-functional tumors managed through observation or medication, with some cases requiring surgical removal, which is generally safe. In contrast, PHEO secrete catecholamines, causing severe blood pressure fluctuations, making surgical resection the only treatment option. Without adequate preoperative preparation, perioperative mortality risk is significantly high.A specialized adrenal CT scanning protocol is recommended to differentiate between these tumor types. However, distinguishing patients with similar washout characteristics remains challenging, and concerns about efficiency, cost, and risk limit its feasibility. Recently, radiomics has demonstrated efficacy in identifying molecular-level differences in tumor cells, including adrenal tumors. This study develops a combined nomogram model, integrating key clinical-radiological and radiomic features from multiphase CT, to enhance accuracy in distinguishing pheochromocytoma from large-diameter lipid-poor adrenal adenoma (LP-AA). Methods A retrospective analysis was conducted on 202 patients with pathologically confirmed adrenal PHEO and large-diameter LP-AA from three tertiary care centers. Key clinico-radiological and radiomics features were selected to construct models: a clinico-radiological model, a radiomics model, and a combined nomogram model for predicting these two tumor types. Model performance and robustness were evaluated using external validation, calibration curve analysis, machine learning techniques, and Delong's test. Additionally, the Hosmer-Lemeshow test, decision curve analysis, and five-fold cross-validation were employed to assess the clinical translational potential of the combined nomogram model. Results All models demonstrated high diagnostic performance, with AUC values exceeding 0.8 across all cohorts, confirming their reliability. The combined nomogram model exhibited the highest diagnostic accuracy, with AUC values of 0.994, 0.979, and 0.945 for the training, validation, and external test cohorts, respectively. Notably, the unenhanced combined nomogram model was not significantly inferior to the three-phase combined nomogram model (p > 0.05 in the validation and test cohorts; p = 0.049 in the training cohort). Conclusions The combined nomogram model reliably distinguishes between PHEO and LP-AA, shows strong clinical translational potential, and may reduce the need for contrast-enhanced CT scans.

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Risk prediction model establishment with tri-phasic CT image features for differential diagnosis of adrenal pheochromocytomas and lipid-poor adenomas: Grouping method

Cited by 3 publications

References 24 publications

An Integrated CT and MRI Imaging Model to Differentiate between Adrenal Adenomas and Pheochromocytomas

An Integrated CT and MRI Imaging Model to Differentiate between Adrenal Adenomas and Pheochromocytomas

CT-Based Radiomics Analysis of Different Machine Learning Models for Discriminating the Risk Stratification of Pheochromocytoma and Paraganglioma: A Multicenter Study

Combined Nomogram for Differentiating Adrenal Pheochromocytoma from Large-Diameter Lipid-poor Adenoma Using Multiphase CT Radiomics and clinico-radiological features

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