Evidence map›Paper›PMID 39425754›Full record

ArticleAbdominal radiology (New York)2026

Differentiate adrenal lipid-poor adenoma from nodular hyperplasia with CT quantitative parameters: a feasibility study.

Xin Bai, Lili Xu, Xiaoxiao Zhang, Huimin Zheng, Hong Zhang, Yan Zhang, Jiahui Zhang, Li Chen, Qianyu Peng, Erjia Guo and 4 more

Abstract read
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In one paragraph

Article in Abdominal radiology (New York), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

14 authors.

Xin BaiDepartment of Radiology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China.
Lili XuDepartment of Radiology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China.
Xiaoxiao ZhangDepartment of Radiology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China.
Huimin ZhengDepartment of Radiology, Longkou Second People's Hospital, Longkou, China.
Hong ZhangDepartment of Radiology, Hospital Peoples of Daye City, The Second Affiliated Hospital of Hubei Polytechnic University, Daye, China.
Yan ZhangDepartment of Medical Imaging, Qujing Maternal and Children Health-care Hospital, Qujing Maternal and Children Hospital, Qujing, China.
Jiahui ZhangDepartment of Radiology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China.
Li ChenDepartment of Radiology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China.
Qianyu PengDepartment of Radiology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China.
Erjia GuoDepartment of Radiology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China.
Gumuyang ZhangDepartment of Radiology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China. zhang_stacey@163.com.
Lin LuDepartment of Endocrinology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China. lulin@pumch.cn.
Zhengyu JinDepartment of Radiology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China. jin_zhengyu@163.com.
Hao SunDepartment of Radiology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China. sunhao_robert@126.com.

Funding

2021 Key Clinical Specialty Program of Beijing, Beijing Municipal Key Clinical NoneCAMS Innovation Fund for Medical Sciences 2022-I2M-C&T-B-019National High-Level Hospital Clinical Research Funding 2022-PUMCH-A-033National High-Level Hospital Clinical Research Funding 2022-PUMCH-A-035National Key Research & Development Plan of China, Major Project of Prevention and Treatment for Common Diseases 2022YFC2505300, subproject 2022YFC2505304Natural Science Foundation of China 81901742
6 · The paper itself

Abstract

objectivesTo explore the potential of CT quantitative parameters in differentiating adrenal lipid-poor adenoma (LPA) from nodular hyperplasia and evaluate diagnostic performance. MATERIALS AND

methodsPatients with LPA or nodular hyperplasia who underwent contrast-enhanced CT before adrenalectomy were analyzed retrospectively. The study included 128 patients (83 with LPA and 45 with nodular hyperplasia). Each lesion's unenhanced attenuation, portal-venous phase attenuation (CTp), and the portal-venous phase attenuation of the abdominal aorta were evaluated. We subsequently calculated absolute enhancement [a lesion's portal-venous phase attenuation minus unenhanced attenuation (in HUs)], relative enhancement (absolute enhancement divided by unenhanced attenuation), and the relative enhancement ratio [(absolute enhancement divided by abdominal aorta's portal-venous phase attenuation) ×100%]. Lesion number and size were recorded. Volume was assessed by ITK-snap software and the ratio of lesion volume to ipsilateral adrenal volume (volume ratio) was determined. Intergroup differences were analyzed using Student's t-test and chi-squared test. Logistic regression models were developed, and receiver operating characteristic (ROC) curves were constructed to determine the area under the ROC curve (AUC), sensitivity, and specificity. The model's performance was then compared against radiologists' subjective assessments, and the inter- and intra-reader agreement values among radiologists were calculated.

resultsPortal-venous phase attenuation, volume ratio, and lesion number were independent predictors of LPA. The logistic regression model incorporating CTp, volume ratio, and lesion number achieved an AUC of 0.835, with a sensitivity of 73.5% and a specificity of 80.0%. The radiologists' diagnostic specificity and accuracy appeared to be inferior to the model. The inter-reader agreement among radiologists ranged from 0.082 to 0.535, and the intra-reader agreement of two radiologists were 0.734 and 0.583.

conclusionThe portal-venous phase CT demonstrated potential in distinguishing LPA from nodular hyperplasia. The diagnostic performance of the model integrating CTp, volume ratio, and lesion number outperformed radiologists in terms of variability and reproducibility.

Indexed as

AdenomaAdrenal Gland NeoplasmsTomography, X-Ray ComputedAdultAgedContrast MediaDiagnosis, DifferentialFeasibility StudiesFemaleHumansMaleMiddle AgedRetrospective StudiesSensitivity and SpecificityContrast MediaAdrenal nodular hyperplasiaAdrenocortical adenomaComputed tomographyQuantitative analysis

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.