Characterization of adrenal glands on computed tomography with a 3D V-Net-based model.

IF 4.1 2区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Insights into Imaging Pub Date : 2025-01-14 DOI:10.1186/s13244-025-01898-7

Yuanchong Chen, Yaofeng Zhang, Xiaodong Zhang, Xiaoying Wang

{"title":"Characterization of adrenal glands on computed tomography with a 3D V-Net-based model.","authors":"Yuanchong Chen, Yaofeng Zhang, Xiaodong Zhang, Xiaoying Wang","doi":"10.1186/s13244-025-01898-7","DOIUrl":null,"url":null,"abstract":"Objectives: To evaluate the performance of a 3D V-Net-based segmentation model of adrenal lesions in characterizing adrenal glands as normal or abnormal.Methods: A total of 1086 CT image series with focal adrenal lesions were retrospectively collected, annotated, and used for the training of the adrenal lesion segmentation model. The dice similarity coefficient (DSC) of the test set was used to evaluate the segmentation performance. The other cohort, consisting of 959 patients with pathologically confirmed adrenal lesions (external validation dataset 1), was included for validation of the classification performance of this model. Then, another consecutive cohort of patients with a history of malignancy (N = 479) was used for validation in the screening population (external validation dataset 2). Parameters of sensitivity, accuracy, etc., were used, and the performance of the model was compared to the radiology report in these validation scenes.Results: The DSC of the test set of the segmentation model was 0.900 (0.810-0.965) (median (interquartile range)). The model showed sensitivities and accuracies of 99.7%, 98.3% and 87.2%, 62.2% in external validation datasets 1 and 2, respectively. It showed no significant difference comparing to radiology reports in external validation datasets 1 and lesion-containing groups of external validation datasets 2 (p = 1.000 and p > 0.05, respectively).Conclusion: The 3D V-Net-based segmentation model of adrenal lesions can be used for the binary classification of adrenal glands.Critical relevance statement: A 3D V-Net-based segmentation model of adrenal lesions can be used for the detection of abnormalities of adrenal glands, with a high accuracy in the pre-surgical scene as well as a high sensitivity in the screening scene.Key points: Adrenal lesions may be prone to inter-observer variability in routine diagnostic workflow. The study developed a 3D V-Net-based segmentation model of adrenal lesions with DSC 0.900 in the test set. The model showed high sensitivity and accuracy of abnormalities detection in different scenes.","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"17"},"PeriodicalIF":4.1000,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11732807/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Insights into Imaging","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1186/s13244-025-01898-7","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}

引用次数: 0

Abstract

Objectives: To evaluate the performance of a 3D V-Net-based segmentation model of adrenal lesions in characterizing adrenal glands as normal or abnormal.

Methods: A total of 1086 CT image series with focal adrenal lesions were retrospectively collected, annotated, and used for the training of the adrenal lesion segmentation model. The dice similarity coefficient (DSC) of the test set was used to evaluate the segmentation performance. The other cohort, consisting of 959 patients with pathologically confirmed adrenal lesions (external validation dataset 1), was included for validation of the classification performance of this model. Then, another consecutive cohort of patients with a history of malignancy (N = 479) was used for validation in the screening population (external validation dataset 2). Parameters of sensitivity, accuracy, etc., were used, and the performance of the model was compared to the radiology report in these validation scenes.

Results: The DSC of the test set of the segmentation model was 0.900 (0.810-0.965) (median (interquartile range)). The model showed sensitivities and accuracies of 99.7%, 98.3% and 87.2%, 62.2% in external validation datasets 1 and 2, respectively. It showed no significant difference comparing to radiology reports in external validation datasets 1 and lesion-containing groups of external validation datasets 2 (p = 1.000 and p > 0.05, respectively).

Conclusion: The 3D V-Net-based segmentation model of adrenal lesions can be used for the binary classification of adrenal glands.

Critical relevance statement: A 3D V-Net-based segmentation model of adrenal lesions can be used for the detection of abnormalities of adrenal glands, with a high accuracy in the pre-surgical scene as well as a high sensitivity in the screening scene.

Key points: Adrenal lesions may be prone to inter-observer variability in routine diagnostic workflow. The study developed a 3D V-Net-based segmentation model of adrenal lesions with DSC 0.900 in the test set. The model showed high sensitivity and accuracy of abnormalities detection in different scenes.

查看原文本刊更多论文

基于三维 V-Net 模型的计算机断层扫描肾上腺特征。

目的：评价基于v - net的肾上腺病变三维分割模型在肾上腺正常或异常的表现。方法：回顾性收集1086张肾上腺局灶性病变的CT图像序列，进行注释，用于肾上腺病灶分割模型的训练。使用测试集的骰子相似系数（DSC）来评价分割性能。另一组包括959例经病理证实的肾上腺病变患者（外部验证数据集1），用于验证该模型的分类性能。然后，在筛选人群（外部验证数据集2）中使用另一组有恶性肿瘤病史的连续队列（N = 479）进行验证。使用敏感性、准确性等参数，并将模型的性能与这些验证场景中的放射学报告进行比较。结果：分割模型的检验集DSC为0.900(0.810-0.965)（中位数（四分位间距））。在外部验证数据集1和2中，该模型的灵敏度和准确度分别为99.7%、98.3%和87.2%、62.2%。与外部验证数据集1的放射学报告和外部验证数据集2的含病变组相比，差异无统计学意义（p = 1.000, p = 0.05）。结论：基于v - net的肾上腺病变三维分割模型可用于肾上腺的二元分类。关键相关性声明：基于3D v - net的肾上腺病变分割模型可用于肾上腺异常的检测，在术前场景具有较高的准确性，在筛查场景具有较高的灵敏度。重点：在常规诊断流程中，肾上腺病变可能容易出现观察者之间的差异。本研究建立了基于3D v - net的肾上腺病变分割模型，测试集DSC为0.900。该模型对不同场景的异常检测具有较高的灵敏度和准确性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Insights into Imaging Medicine-Radiology, Nuclear Medicine and Imaging

CiteScore

7.30

自引率

4.30%

发文量

182

审稿时长

13 weeks

期刊介绍： Insights into Imaging (I³) is a peer-reviewed open access journal published under the brand SpringerOpen. All content published in the journal is freely available online to anyone, anywhere! I³ continuously updates scientific knowledge and progress in best-practice standards in radiology through the publication of original articles and state-of-the-art reviews and opinions, along with recommendations and statements from the leading radiological societies in Europe. Founded by the European Society of Radiology (ESR), I³ creates a platform for educational material, guidelines and recommendations, and a forum for topics of controversy. A balanced combination of review articles, original papers, short communications from European radiological congresses and information on society matters makes I³ an indispensable source for current information in this field. I³ is owned by the ESR, however authors retain copyright to their article according to the Creative Commons Attribution License (see Copyright and License Agreement). All articles can be read, redistributed and reused for free, as long as the author of the original work is cited properly. The open access fees (article-processing charges) for this journal are kindly sponsored by ESR for all Members. The journal went open access in 2012, which means that all articles published since then are freely available online.