%0 Journal Article
%@ 1438-8871
%I JMIR Publications
%V 27
%N 
%P e65937
%T Localization and Classification of Adrenal Masses in Multiphase Computed Tomography: Retrospective Study
%A Yang,Liuyang
%A Zhang,Xinzhang
%A Li,Zhenhui
%A Wang,Jian
%A Zhang,Yiwen
%A Shan,Liyu
%A Shi,Xin
%A Si,Yapeng
%A Wang,Shuailong
%A Li,Lin
%A Wu,Ping
%A Xu,Ning
%A Liu,Lizhu
%A Yang,Junfeng
%A Leng,Jinjun
%A Yang,Maolin
%A Zhang,Zhuorui
%A Wang,Junfeng
%A Dong,Xingxiang
%A Yang,Guangjun
%A Yan,Ruiying
%A Li,Wei
%A Liu,Zhimin
%A Li,Wenliang
%+ Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, No. 519, Kunzhou Road, Xishan District, Kunming, 650118, China, 86 0871 6818903, liwenliang@kmmu.edu.cn
%K MA-YOLO model
%K multi-class adrenal masses
%K multi-phase CT images
%K localization
%K classification
%D 2025
%7 24.4.2025
%9 Original Paper
%J J Med Internet Res
%G English
%X Background: The incidence of adrenal incidentalomas is increasing annually, and most types of adrenal masses require surgical intervention. Accurate classification of common adrenal masses based on tumor computed tomography (CT) images by radiologists or clinicians requires extensive experience and is often challenging, which increases the workload of radiologists and leads to unnecessary adrenal surgeries. There is an urgent need for a fully automated, noninvasive, and precise approach for the identification and accurate classification of common adrenal masses. Objective: This study aims to enhance diagnostic efficiency and transform the current clinical practice of preoperative diagnosis of adrenal masses. Methods: This study is a retrospective analysis that includes patients with adrenal masses who underwent adrenalectomy from January 1, 2021, to May 31, 2023, at Center 1 (internal dataset), and from January 1, 2016, to May 31, 2023, at Center 2 (external dataset). The images include unenhanced, arterial, and venous phases, with 21,649 images used for the training set, 2406 images used for the validation set, and 12,857 images used for the external test set. We invited 3 experienced radiologists to precisely annotate the images, and these annotations served as references. We developed a deep learning–based adrenal mass detection model, Multi-Attention YOLO (MA-YOLO), which can automatically localize and classify 6 common types of adrenal masses. In order to scientifically evaluate the model performance, we used a variety of evaluation metrics, in addition, we compared the improvement in diagnostic efficacy of 6 doctors after incorporating model assistance. Results: A total of 516 patients were included. In the external test set, the MA-YOLO model achieved an intersection over union of 0.838, 0.885, and 0.890 for the localization of 6 types of adrenal masses in unenhanced, arterial, and venous phase CT images, respectively. The corresponding mean average precision for classification was 0.885, 0.913, and 0.915, respectively. Additionally, with the assistance of this model, the classification diagnostic performance of 6 radiologists and clinicians for adrenal masses improved. Except for adrenal cysts, at least 1 physician significantly improved diagnostic performance for the other 5 types of tumors. Notably, in the categories of adrenal adenoma (for senior clinician: P=.04, junior radiologist: P=.01, and senior radiologist: P=.01) and adrenal cortical carcinoma (junior clinician: P=.02, junior radiologist: P=.01, and intermediate radiologist: P=.001), half of the physicians showed significant improvements after using the model for assistance. Conclusions: The MA-YOLO model demonstrates the ability to achieve efficient, accurate, and noninvasive preoperative localization and classification of common adrenal masses in CT examinations, showing promising potential for future applications. 
%R 10.2196/65937
%U https://www.jmir.org/2025/1/e65937
%U https://doi.org/10.2196/65937