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

Author:

Zhu Yangyang12,Meng Zheling23,Wu Hao1,Fan Xiao1,lv Wenhao1,Tian Jie23ORCID,Wang Kun23,Nie Fang14ORCID

Affiliation:

1. Medical Center of Ultrasound, Lanzhou University Second Hospital, Lanzhou, China

2. CAS Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China

3. School of Artificial Intelligence, University of the Chinese Academy of Sciences School, Beijing, China

4. Gansu Province Medical Engineering Research Center for Intelligence Ultrasound, Lanzhou, China

Abstract

Abstract 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:0.708~0.755). 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.

Funder

Beijing Science Fund for Distinguished Young Scholars

Gansu Province Science and Technology Plan Project

Data Center of Management Science, National Natural Science Foundation of China - Peking University

National Natural Science Foundation of China

The Excellent Member Project of Youth Innovation Promotion Association CAS

Publisher

Georg Thieme Verlag KG

Subject

Radiology, Nuclear Medicine and imaging

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