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一种用于区分肾上腺腺瘤和嗜铬细胞瘤的CT与MRI综合成像模型。

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

作者信息

Araujo-Castro Marta, García Sanz Iñigo, Mínguez Ojeda César, Calatayud María, Hanzu Felicia A, Mora Mireia, Vicente Delgado Almudena, Carrera Concepción Blanco, de Miguel Novoa Paz, Del Carmen López García María, Manjón-Miguélez Laura, Rodríguez de Vera Gómez Pablo, Del Castillo Tous María, Barahona San Millán Rebeca, Recansens Mónica, Fernández-Ladreda Mariana Tomé, Valdés Nuria, Gracia Gimeno Paola, Robles Lazaro Cristina, Michalopoulou Theodora, Gómez Dos Santos Victoria, Alvarez-Escola Cristina, García Centeno Rogelio, Lamas Cristina, Herrera-Martínez Aura

机构信息

Endocrinology & Nutrition Department, Hospital Universitario Ramón y Cajal, Instituto de Investigación Biomédica Ramón y Cajal (IRYCIS), 28034 Madrid, Spain.

Medicine Departmen, University of Alcalá, 28801 Madrid, Spain.

出版信息

Cancers (Basel). 2023 Jul 23;15(14):3736. doi: 10.3390/cancers15143736.

Abstract

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.

摘要

目的

在一组接受肾上腺切除术的嗜铬细胞瘤(PHEO)和腺瘤患者中,对我们基于平扫CT排除PHEO的预测模型进行外部验证。

方法

该预测模型先前在一个包含1131例肾上腺病变患者的回顾性队列中开发。在本研究中,我们在另一组有可用组织病理学结果的214例患者中对该模型进行了外部验证。

结果

在外部验证中,纳入了115例PHEO患者和99例腺瘤患者。我们先前描述的结合平扫CT中高脂质含量和肿瘤大小变量的预测模型(AUC-ROC:0.961)在我们当前的研究人群中对PHEO预测的诊断准确性较低(AUC:0.750)。然而,当我们排除非典型腺瘤(Hounsfield单位(HU)>10,n = 39)时,诊断准确性提高到87.4%。此外,在整个队列(包括非典型腺瘤)中,当模型中纳入MRI信息时,当纳入肿瘤大小、平扫CT扫描中的高脂质含量以及MRI的T2序列高信号强度变量时,诊断准确性提高到85%。对于肾上腺病变<20 mm、HU>10且T2无高信号强度的患者,PHEO的概率<0.3%。

结论

我们的研究证实,当排除非典型肾上腺病变时,我们结合肿瘤大小和脂质含量的预测模型对PHEO的预测具有高可靠性。然而,对于平扫CT扫描中HU>10的非典型肾上腺病变,MRI信息对于正确排除PHEO诊断是必要的。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7616/10378495/2e445b525c26/cancers-15-03736-g001.jpg

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