Deep learning radiomics of dual-modality ultrasound images for hierarchical diagnosis of unexplained cervical lymphadenopathy

doi:10.1186/s12916-022-02469-z

CORC > 自动化研究所 > 中国科学院自动化研究所 > 中国科学院分子影像重点实验室

	Deep learning radiomics of dual-modality ultrasound images for hierarchical diagnosis of unexplained cervical lymphadenopathy
	Zhu,Yangyang 7,8; Meng,Zheling 6,7; Fan,Xiao 8; Duan,Yin 5; Jia,Yingying 4; Dong,Tiantian 8; Wang,Yanfang 8; Song,Juan 4; Tian,Jie 3,6,7; Wang,Kun 6,7
刊名	BMC Medicine
	2022-08-26
卷号	20 期号:1
关键词	Deep learning Cervical lymphadenopathy Ultrasound Reactive hyperplasia Tuberculous lymphadenitis Lymphoma Metastatic carcinoma
DOI	10.1186/s12916-022-02469-z
通讯作者	Wang,Kun(kun.wang@ia.ac.cn) ; Nie,Fang(ery_nief@lzu.edu.cn)
英文摘要	AbstractBackgroundAccurate diagnosis of unexplained cervical lymphadenopathy (CLA) using medical images heavily relies on the experience of radiologists, which is even worse for CLA patients in underdeveloped countries and regions, because of lack of expertise and reliable medical history. This study aimed to develop a deep learning (DL) radiomics model based on B-mode and color Doppler ultrasound images for assisting radiologists to improve their diagnoses of the etiology of unexplained CLA.MethodsPatients with unexplained CLA who received ultrasound examinations from three hospitals located in underdeveloped areas of China were retrospectively enrolled. They were all pathologically confirmed with reactive hyperplasia, tuberculous lymphadenitis, lymphoma, or metastatic carcinoma. By mimicking the diagnosis logic of radiologists, three DL sub-models were developed to achieve the primary diagnosis of benign and malignant, the secondary diagnosis of reactive hyperplasia and tuberculous lymphadenitis in benign candidates, and of lymphoma and metastatic carcinoma in malignant candidates, respectively. Then, a CLA hierarchical diagnostic model (CLA-HDM) integrating all sub-models was proposed to classify the specific etiology of each unexplained CLA. The assistant effectiveness of CLA-HDM was assessed by comparing six radiologists between without and with using the DL-based classification and heatmap guidance.ResultsA total of 763 patients with unexplained CLA were enrolled and were split into the training cohort (n=395), internal testing cohort (n=171), and external testing cohorts 1 (n=105) and 2 (n=92). The CLA-HDM for diagnosing four common etiologies of unexplained CLA achieved AUCs of 0.873 (95% CI: 0.838–0.908), 0.837 (95% CI: 0.789–0.889), and 0.840 (95% CI: 0.789–0.898) in the three testing cohorts, respectively, which was systematically more accurate than all the participating radiologists. With its assistance, the accuracy, sensitivity, and specificity of six radiologists with different levels of experience were generally improved, reducing the false-negative rate of 2.2–10% and the false-positive rate of 0.7–3.1%.ConclusionsMulti-cohort testing demonstrated our DL model integrating dual-modality ultrasound images achieved accurate diagnosis of unexplained CLA. With its assistance, the gap between radiologists with different levels of experience was narrowed, which is potentially of great significance for benefiting CLA patients in underdeveloped countries and regions worldwide.
语种	英语
出版者	BioMed Central
WOS记录号	BMC:10.1186/S12916-022-02469-Z
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/49852]
专题	自动化研究所_中国科学院分子影像重点实验室
通讯作者	Wang,Kun; Nie,Fang
作者单位	1.Gansu Province Medical Engineering Research Center for Intelligence Ultrasound 2.Gansu Province Clinical Research Center for Ultrasonography 3.Beihang University; Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering 4.People’s Hospital of Ningxia Hui Autonomous Region; Department of Ultrasound 5.Gansu Provincial Cancer Hospital; Department of Ultrasound 6.University of Chinese Academy of Sciences; School of Artificial Intelligence 7.CAS Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences 8.Lanzhou University Second Hospital, Lanzhou University; Ultrasound Medical Center
推荐引用方式 GB/T 7714	Zhu,Yangyang,Meng,Zheling,Fan,Xiao,et al. Deep learning radiomics of dual-modality ultrasound images for hierarchical diagnosis of unexplained cervical lymphadenopathy[J]. BMC Medicine,2022,20(1).
APA	Zhu,Yangyang.,Meng,Zheling.,Fan,Xiao.,Duan,Yin.,Jia,Yingying.,...&Nie,Fang.(2022).Deep learning radiomics of dual-modality ultrasound images for hierarchical diagnosis of unexplained cervical lymphadenopathy.BMC Medicine,20(1).
MLA	Zhu,Yangyang,et al."Deep learning radiomics of dual-modality ultrasound images for hierarchical diagnosis of unexplained cervical lymphadenopathy".BMC Medicine 20.1(2022).