{"title":"基于ct的放射组学模型鉴别肾上腺皮质癌和嗜铬细胞瘤:一项多中心回顾性研究。","authors":"Yinyao Chao, Hongzhang Zhu, Wenyi Yang, Haohua Yao, Nan Ma, Xianda Chen, Jing Zhao, Huali Ma, Zhenhua Liu, Hui Han, Zhuowei Liu, Kai Yao, Yiyao Li, Peng Wu, Jingtong Zhang, Bin Li, Shengjie Guo","doi":"10.1186/s12880-025-01842-7","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Adrenocortical 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.</p><p><strong>Methods: </strong>In 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.</p><p><strong>Results: </strong>We 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.</p><p><strong>Conclusions: </strong>The interpretable CT-based radiomics model outperforms radiologists in differentiating between ACC and PHEO, especially when hormone testing results are atypical.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"310"},"PeriodicalIF":3.2000,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12317479/pdf/","citationCount":"0","resultStr":"{\"title\":\"Differentiating between adrenocortical carcinoma and pheochromocytoma by a CT-based radiomics model: a multicenter retrospective study.\",\"authors\":\"Yinyao Chao, Hongzhang Zhu, Wenyi Yang, Haohua Yao, Nan Ma, Xianda Chen, Jing Zhao, Huali Ma, Zhenhua Liu, Hui Han, Zhuowei Liu, Kai Yao, Yiyao Li, Peng Wu, Jingtong Zhang, Bin Li, Shengjie Guo\",\"doi\":\"10.1186/s12880-025-01842-7\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Adrenocortical 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.</p><p><strong>Methods: </strong>In 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.</p><p><strong>Results: </strong>We 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.</p><p><strong>Conclusions: </strong>The interpretable CT-based radiomics model outperforms radiologists in differentiating between ACC and PHEO, especially when hormone testing results are atypical.</p>\",\"PeriodicalId\":9020,\"journal\":{\"name\":\"BMC Medical Imaging\",\"volume\":\"25 1\",\"pages\":\"310\"},\"PeriodicalIF\":3.2000,\"publicationDate\":\"2025-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12317479/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"BMC Medical Imaging\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1186/s12880-025-01842-7\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"BMC Medical Imaging","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1186/s12880-025-01842-7","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
引用次数: 0
摘要
背景:肾上腺皮质癌(ACC)是一种罕见且高度恶性的肾上腺肿瘤,其计算机断层扫描(CT)特征与恶性程度较低的嗜铬细胞瘤(PHEO)相似。虽然生物化学评价被广泛接受用于区分ACC和PHEO,但无功能肿瘤仍然是一个诊断挑战。CT图像的相似性和非典型激素水平可能导致诊断准确性不理想,导致不适当的临床干预。本研究旨在区分基于增强CT放射组学特征的大(≥4cm) ACC和PHEO。方法:本回顾性研究纳入2011年1月至2023年9月来自3家机构的158例病理诊断为ACC或PHEO的患者。从对比增强CT的不同阶段提取放射组学特征,然后通过两步程序选择。放射组学模型在来自机构1的109名患者队列中开发,然后在来自机构2和机构3的49名患者的外部测试队列中评估模型的性能。使用DeLong测试比较两名放射科医生放射组学模型的接收者工作特征曲线下面积(AUC)。收集激素测试结果以确定是否存在过量的皮质醇或儿茶酚胺。SHapley加性解释(SHAP)用于提高放射组学模型的可解释性。结果:我们开发并评估了一个放射组学模型,该模型由十个选定的基于ct的放射组学特征组成。在外部检测队列中,所提出的放射组学模型在区分ACC和PHEO方面具有较高的准确性(86%)、特异性(88%)和敏感性(81%),优于2名放射科医生(AUC 0.920 vs. 0.786, 0.629)。该放射组学模型在区分生化阴性ACC和PHEO方面表现出很强的能力(准确率为80%)。此外,即使皮质醇和儿茶酚胺水平同时升高,它的表现也保持一致。此外,SHAP为放射组学模型提供了定量解释,并使诊断过程可视化。结论:可解释的基于ct的放射组学模型在区分ACC和PHEO方面优于放射科医生,特别是当激素检测结果不典型时。
Differentiating between adrenocortical carcinoma and pheochromocytoma by a CT-based radiomics model: a multicenter retrospective study.
Background: Adrenocortical 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.
Methods: In 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.
Results: We 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.
Conclusions: The interpretable CT-based radiomics model outperforms radiologists in differentiating between ACC and PHEO, especially when hormone testing results are atypical.
期刊介绍:
BMC Medical Imaging is an open access journal publishing original peer-reviewed research articles in the development, evaluation, and use of imaging techniques and image processing tools to diagnose and manage disease.