Evidence map›Paper›PMID 40750857›Full record

ArticleBMC medical imaging2025

Differentiating between adrenocortical carcinoma and pheochromocytoma by a CT-based radiomics model: a multicenter retrospective study.

Yinyao Chao, Hongzhang Zhu, Wenyi Yang, Haohua Yao, Nan Ma, Xianda Chen, Jing Zhao, Huali Ma, Zhenhua Liu, Hui Han and 7 more

Abstract readMulticenter Study
In one paragraph

Article in BMC medical imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Observational
  2. Review
  3. An interpretableFrontiers in oncology · 2025
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

17 authors.

Yinyao Chao *Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China.
Hongzhang Zhu *Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
Wenyi Yang *School of Automation Science and Engineering, South China University of Technology, Guangzhou, 510640, China.
Haohua Yao *Department of Urology, Guangdong Provincial People's Hospital, Guangzhou, China.
Nan MaDepartment of Urology, Sun Yat-sen University Cancer Center, Sun Yat-sen University, Guangzhou, 510060, China.
Xianda ChenDepartment of Urology, Sun Yat-sen University Cancer Center, Sun Yat-sen University, Guangzhou, 510060, China.
Jing ZhaoState Key Laboratory of Oncology in Southern China, Collaborative Innovation Center of Cancer Medicine, Guangzhou, China.
Huali MaState Key Laboratory of Oncology in Southern China, Collaborative Innovation Center of Cancer Medicine, Guangzhou, China.
Zhenhua LiuDepartment of Urology, Sun Yat-sen University Cancer Center, Sun Yat-sen University, Guangzhou, 510060, China.
Hui HanDepartment of Urology, Sun Yat-sen University Cancer Center, Sun Yat-sen University, Guangzhou, 510060, China.
Zhuowei LiuDepartment of Urology, Sun Yat-sen University Cancer Center, Sun Yat-sen University, Guangzhou, 510060, China.
Kai YaoDepartment of Urology, Sun Yat-sen University Cancer Center, Sun Yat-sen University, Guangzhou, 510060, China.
Yiyao LiDepartment of Urology, Nanfang Hospital, Southern Medical University, Guangzhou, China.
Peng WuDepartment of Urology, Nanfang Hospital, Southern Medical University, Guangzhou, China.
Jingtong ZhangDepartment of Urology, Sun Yat-sen University Cancer Center, Sun Yat-sen University, Guangzhou, 510060, China. zhangjt@sysucc.org.cn.
Bin LiSchool of Automation Science and Engineering, South China University of Technology, Guangzhou, 510640, China. binlee@scut.edu.cn.
Shengjie GuoDepartment of Urology, Sun Yat-sen University Cancer Center, Sun Yat-sen University, Guangzhou, 510060, China. guoshj@sysucc.org.cn.

Funding

Fostering Program for NSFC Young Applicants (Tulip Talent Training Program) of Sun Yat-sen University Cancer Center 2025yfd11Guangdong Basic and Applied Basic Research Foundation 2022A1515111119Guangdong Basic and Applied Basic Research Foundation 2023A1515110515
6 · The paper itself

Abstract

backgroundAdrenocortical carcinoma (ACC), a rare and highly malignant adrenal gland tumor, exhibits computed tomography (CT) characteristics that resemble those of the less malignant pheochromocytoma (PHEO). While biochemical evaluation is widely accepted for differentiating between ACC and PHEO, non-functioning tumors remain a diagnostic challenge. The similarity in CT imaging and atypical hormone levels can lead to suboptimal accuracy in diagnosis, leading to inappropriate clinical interventions. This study aims to differentiate between large (≥ 4 cm) ACC and PHEO with radiomics features based on contrast-enhanced CT.

methodsIn this retrospective study, 158 patients who received pathological diagnoses of ACC or PHEO between January 2011 and September 2023 were enrolled from three institutions. Radiomics features were extracted from different phases of contrast-enhanced CT and then selected by a two-step procedure. The radiomics model was developed in a cohort of 109 patients from Institution 1, then the model performance was evaluated in the external test cohort of 49 patients from Institutions 2 and 3. The area under the receiver operating characteristic curve (AUC) of the radiomics model was compared with two radiologists using the DeLong test. Hormone testing results were collected to determine the presence of excess cortisol or catecholamines. SHapley Additive exPlanations (SHAP) was used to improve the interpretability of the radiomics model.

resultsWe developed and evaluated a radiomics model consisting of ten selected CT-based radiomics features. In the external test cohort, the proposed radiomics model achieved high accuracy (86%), specificity (88%), and sensitivity (81%) in differentiating between ACC and PHEO and outperformed 2 radiologists (AUC 0.920 vs. 0.786, 0.629). This radiomics model showed strong capabilities in differentiating biochemically negative ACC and PHEO (with an accuracy of 80%). Moreover, its performance remained consistent even when cortisol and catecholamine levels were simultaneously elevated. Furthermore, SHAP provided quantitative explanations for the radiomics model and visualized the diagnostic process.

conclusionsThe interpretable CT-based radiomics model outperforms radiologists in differentiating between ACC and PHEO, especially when hormone testing results are atypical.

Indexed as

Adrenal Cortex NeoplasmsAdrenal Gland NeoplasmsAdrenocortical CarcinomaPheochromocytomaTomography, X-Ray ComputedAdolescentAdultAgedContrast MediaDiagnosis, DifferentialFemaleHumansMaleMiddle AgedRadiomicsRetrospective StudiesContrast MediaAdrenocortical carcinomaContrast-enhanced CTPheochromocytomaRadiomics modelSHapley additive explanations

Identifiers

PMID40750857
PMCPMC12317479

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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.