Deep learning radiomics of multimodal ultrasound for classifying metastatic cervical lymphadenopathy into primary cancer sites: a feasibility study

doi:10.1055/a-2161-9369

	Deep learning radiomics of multimodal ultrasound for classifying metastatic cervical lymphadenopathy into primary cancer sites: a feasibility study
	Zhu, Yangyang 1,2; Meng, Zheling2,3 ; Wu, Hao 1; Fan, Xiao 1; Lv, Wenhao 1; Tian, Jie 2,3; Wang, Kun2,3 ; Nie, Fang 1,4
发表期刊	ULTRASCHALL IN DER MEDIZIN
ISSN	0172-4614
	2023-12-05
页码	11
通讯作者	Wang, Kun(kun.wang@ia.ac.cn) ; Nie, Fang(ery_nief@lzu.edu.cn)
摘要	Purpose To investigate the feasibility of deep learning radiomics (DLR) based on multimodal ultrasound to differentiate the primary cancer sites of metastatic cervical lymphadenopathy (CLA). Materials and Methods This study analyzed 280 biopsy-confirmed metastatic CLAs from 280 cancer patients, including 54 from head and neck squamous cell carcinoma (HNSCC), 58 from thyroid cancer (TC), 92 from lung cancer (LC), and 76 from gastrointestinal cancer (GIC). Before biopsy, patients underwent conventional ultrasound (CUS), ultrasound elastography (UE), and contrast-enhanced ultrasound (CEUS). Based on CUS, DLR models using CUS, CUS+UE, CUS+CEUS, and CUS+UE+CEUS data were developed and compared. The best model was integrated with key clinical indicators selected by univariate analysis to achieve the best classification performance. Results All DLR models achieved similar performance with respect to classifying four primary tumor sites of metastatic CLA (AUC:0.708 similar to 0.755). After integrating key clinical indicators (age, sex, and neck level), the US+UE+CEUS+clinical model yielded the best performance with an overall AUC of 0.822 in the validation cohort, but there was no significance compared with the basal CUS+clinical model (P>0.05), both of which identified metastasis from HNSCC, TC, LC, and GIC with 0.869 and 0.911, 0.838 and 0.916, 0.750 and 0.610, and 0.829 and 0.769, respectively. Conclusion The ultrasound-based DLR model can be used to classify the primary cancer sites of metastatic CLA, and the CUS combined with clinical indicators is adequate to provide a high discriminatory performance. The addition of the combination of UE and CEUS data is expected to further improve performance.
关键词	Multimodal Ultrasound Metastatic Cervical Lymphadenopathy Deep Learning Primary Cancer Sites
DOI	10.1055/a-2161-9369
关键词[WOS]	SHEAR-WAVE ELASTOGRAPHY ; LYMPH-NODES ; SONOGRAPHY
收录类别	SCI
语种	英语
资助项目	Beijing Science Fund for Distinguished Young Scholars
项目资助者	Beijing Science Fund for Distinguished Young Scholars
WOS研究方向	Acoustics ; Radiology, Nuclear Medicine & Medical Imaging
WOS类目	Acoustics ; Radiology, Nuclear Medicine & Medical Imaging
WOS记录号	WOS:001124362700002
出版者	GEORG THIEME VERLAG KG
引用统计	被引频次：1[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/54902
专题	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.Lanzhou Univ Second Hosp, Med Ctr Ultrasound, Lanzhou 730030, Peoples R China 2.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci Sch, Sch Artificial Intelligence, Beijing, Peoples R China 4.Gansu Prov Med Engn Res Ctr Intelligence Ultrasou, Lanzhou, Peoples R China
第一作者单位	中国科学院自动化研究所
通讯作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Zhu, Yangyang,Meng, Zheling,Wu, Hao,et al. Deep learning radiomics of multimodal ultrasound for classifying metastatic cervical lymphadenopathy into primary cancer sites: a feasibility study[J]. ULTRASCHALL IN DER MEDIZIN,2023:11.
APA	Zhu, Yangyang.,Meng, Zheling.,Wu, Hao.,Fan, Xiao.,Lv, Wenhao.,...&Nie, Fang.(2023).Deep learning radiomics of multimodal ultrasound for classifying metastatic cervical lymphadenopathy into primary cancer sites: a feasibility study.ULTRASCHALL IN DER MEDIZIN,11.
MLA	Zhu, Yangyang,et al."Deep learning radiomics of multimodal ultrasound for classifying metastatic cervical lymphadenopathy into primary cancer sites: a feasibility study".ULTRASCHALL IN DER MEDIZIN (2023):11.