CT影像组学鉴别肾上腺富血供腺瘤和嗜铬细胞瘤

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Abstract Objective To explore the value of the CT radiomics model in the differential diagnosis of hypervascular adrenal adenoma (HAA) and pheochromocytoma (AP). Methods A total of 79 patients (53 HAA and 26 AP) who underwent phase 4 CT scan and were pathologically confirmed in the Department of Radiology of the Third Medical Center of PLA General Hospital from January 2022 to July 2024 were retrospectively included. Clinical indicators and radiomics features extracted from CT images through scientific research platform were analyzed. Using the F_Test to reduce dimensionality and screen radiomics features, factors with statistically significant differences were included in binary logistic regression analysis and subsequent modeling. Finally, ROC curve and decision curve of the predictive model were plotted to evaluate its predictive performance and clinical application value. Results The value of plain CT scan (OR=1.166, P<0.01) and the long diameter of tumor (OR=2.226, P<0.01) were independent risk factors for the discrimination of the two groups. The AUC based on CTpre, arterial phase (CTa), late arterial phase (CTla), venous phase (CTv), four-phase combination, imaging features, and clinical indicator model validation set were 0.93, 0.92, 0.88, 0.86, 0.87, 0.88 and 0.72, respectively. The CTpre-based radiomics model was characterized by the best performance, with accuracy, sensitivity, and specificity of 0.86, 0.89, and 0.85, respectively. Conclusions The model based om CT radiomics has good predictive performance and application value in the discrimination of HAA from AP.

Key words： CT radiomics hypervascular adrenal adenoma adrenal pheochromocytoma discrimination

Received: 18 September 2024

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	Articles by authors
	CHENG Yuanhua
	LU Jing
	HUA Rongrong
	HE Xucheng
	ZHANG Yanan
	WANG Guisheng
	CHEN Xiaoxia

Cite this article:

CHENG Yuanhua,LU Jing,HUA Rongrong等. Value of CT radiomics in discrimination of hypervascular adrenal adenoma from pheochromocytoma[J]. Med. J. Chin. Peop. Armed Poli. Forc., 2025, 36(2): 154-158.

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https://journal08.magtechjournal.com/Jwk_wjyx/EN/ OR https://journal08.magtechjournal.com/Jwk_wjyx/EN/Y2025/V36/I2/154

[1]	Fassnacht M, Tsagarakis S, Terzolo M, et al. European society of endocrinology clinical practice guidelines on the management of adrenal incidentalomas, in collaboration with the european network for the study of adrenal tumors[J]. Eur J Endocrinol, 2023,189(1):G1-G42.
[2]	Aggarwal S, Prete A, Chortis V, et al. Pheochromocytomas most commonly present as adrenal incidentalomas: a large tertiary center experience[J]. J Clin Endocrinol Metab, 2023,109(1):e389-e396.
[3]	Bancos I, Prete A. Approach to the patient with adrenal incidentaloma[J]. The J of Clini End & Met, 2021,106(11):3331-3353.
[4]	Mete O, Asa S L, Gill A J, et al. Overview of the 2022 WHO Classification of Paragangliomas and Pheochromocytomas[J]. Endocr Pathol, 2022,33(1):90-114.
[5]	Amin M B, Greene F L, Edge S B, et al. The eighth edition AJCC cancer staging manual: continuing to build a bridge from a population‐based to a more “personalized” approach to cancer staging[J]. CA: A Cancer Journal for Clinicians, 2017,67(2):93-99.
[6]	张磊, 石浪勇, 谢宝君. CT与MRI鉴别诊断肾上腺皮质腺瘤与嗜铬细胞瘤的价值研究[J]. 现代医用影像学, 2023,32(10):1924-1927.
[7]	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[J]. EPMA J, 2018,9(4):421-429.
[8]	项林爱, 胡红杰, 王健, 等. 无脂性肾上腺腺瘤和无囊变嗜铬细胞瘤的CT鉴别诊断[J]. 临床放射学杂志, 2019,38(03):484-488.
[9]	Aerts H J, Velazquez E R, Leijenaar R T, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach[J]. Nat Commun, 2014,5:4006.
[10]	周怡, 张贝, 黄科峰, 等. 高分辨CT联合Ki-67表达预测低分化浸润性非黏液肺腺癌的临床应用[J]. 武警医学, 2024,35(09):777-780.
[11]	吴春梅, 殷小平. 肾上腺腺瘤的影像组学研究进展[J]. 医学研究与教育, 2022,39(3):29-34.
[12]	Reiazi R, Abbas E, Famiyeh P, et al. The impact of the variation of imaging parameters on the robustness of Computed Tomography radiomic features: A review[J]. Computers in Bio Med, 2021,133:104400.
[13]	Fassnacht M, Arlt W, Bancos I, et al. Management of adrenal incidentalomas: european society of endocrinology clinical practice guideline in collaboration with the european network for the study of adrenal tumors[J]. Eur J Endocrinol, 2016,175(2):G1-G34.
[14]	Lam A K. Update on Adrenal Tumours in 2017 world health organization (WHO) of endocrine tumours[J]. Endocr Pathol, 2017,28(3):213-227.
[15]	史展, 张亚斌, 张丹卉. 能谱CT成像在鉴别肾上腺意外瘤的价值研究[J]. 中国CT和MRI杂志, 2024,22(05):123-125.
[16]	刘芳, 王明亮, 许晓杰, 等. 肾上腺嗜铬细胞瘤的MRI表现:常见特征及少见特征[J]. 临床放射学杂志, 2024,43(04):615-620.
[17]	Gerson R, Tu W, Abreu-Gomez J, et al. Evaluation of the T2-weighted (T2W) adrenal MRI calculator to differentiate adrenal pheochromocytoma from lipid-poor adrenal adenoma[J]. Eur Radiol, 2022,32(12):8247-8255.
[18]	Niu Z, Wang J, Yang Y, et al. Risk prediction model establishment with tri-phasic CT image features for differential diagnosis of adrenal pheochromocytomas and lipid-poor adenomas: Grouping method[J]. Front Endocrinol (Lausanne), 2022,13:925577.
[19]	陈威, 陶宇峰, 王伟. 基于CT影像特征构建鉴别肾上腺嗜铬细胞瘤和乏脂性腺瘤的预测模型[J]. 浙江医学, 2022,44(16):1714-1719.
[20]	An Y Y, Yang G Z, Lin B, et al. Differentiation of lipid-poor adenoma from pheochromocytoma on biphasic contrast-enhanced CT[J]. Abdom Radiol (NY), 2021,46(9):4353-4361.
[21]	Yi X, Guan X, Chen C, et al. Adrenal incidentaloma: machine learning-based quantitative texture analysis of unenhanced CT can effectively differentiate sPHEO from lipid-poor adrenal adenoma[J]. J Cancer, 2018,9(19):3577-3582.