Ultrasound radiomics models based on multimodal imaging feature fusion of papillary thyroid carcinoma for predicting central lymph node metastasis

Quan Dai; Yi Tao; Dongmei Liu; Chen Zhao; Dong Sui; Jinshun Xu; Tiefeng Shi; Xiaoping Leng; Man Lu

doi:10.3389/fonc.2023.1261080

Ultrasound radiomics models based on multimodal imaging feature fusion of papillary thyroid carcinoma for predicting central lymph node metastasis

Front Oncol. 2023 Oct 30:13:1261080. doi: 10.3389/fonc.2023.1261080. eCollection 2023.

Authors

Quan Dai¹, Yi Tao², Dongmei Liu³, Chen Zhao³, Dong Sui^{4

5}, Jinshun Xu¹, Tiefeng Shi⁶, Xiaoping Leng³, Man Lu¹

Affiliations

¹ Department of Ultrasound, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Medicine & Laboratory of Translational Research in Ultrasound Theranostics, Chengdu, China.
² Department of Ultrasound, West China Hospital of Sichuan University, Chengdu, China.
³ Department of Ultrasound, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China.
⁴ State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China.
⁵ School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing, China.
⁶ Department of General Surgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China.

Abstract

Objective: This retrospective study aimed to establish ultrasound radiomics models to predict central lymph node metastasis (CLNM) based on preoperative multimodal ultrasound imaging features fusion of primary papillary thyroid carcinoma (PTC).

Methods: In total, 498 cases of unifocal PTC were randomly divided into two sets which comprised 348 cases (training set) and 150 cases (validition set). In addition, the testing set contained 120 cases of PTC at different times. Post-operative histopathology was the gold standard for CLNM. The following steps were used to build models: the regions of interest were segmented in PTC ultrasound images, multimodal ultrasound image features were then extracted by the deep learning residual neural network with 50-layer network, followed by feature selection and fusion; subsequently, classification was performed using three classical classifiers-adaptive boosting (AB), linear discriminant analysis (LDA), and support vector machine (SVM). The performances of the unimodal models (Unimodal-AB, Unimodal-LDA, and Unimodal-SVM) and the multimodal models (Multimodal-AB, Multimodal-LDA, and Multimodal-SVM) were evaluated and compared.

Results: The Multimodal-SVM model achieved the best predictive performance than the other models (P < 0.05). For the Multimodal-SVM model validation and testing sets, the areas under the receiver operating characteristic curves (AUCs) were 0.910 (95% CI, 0.894-0.926) and 0.851 (95% CI, 0.833-0.869), respectively. The AUCs of the Multimodal-SVM model were 0.920 (95% CI, 0.881-0.959) in the cN0 subgroup-1 cases and 0.828 (95% CI, 0.769-0.887) in the cN0 subgroup-2 cases.

Conclusion: The ultrasound radiomics model only based on the PTC multimodal ultrasound image have high clinical value in predicting CLNM and can provide a reference for treatment decisions.

Keywords: lymph node metastasis; multimodality; papillary thyroid carcinoma; radiomics; ultrasound.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This study was supported by the National Natural Science Foundation of China (Grant No. 61702026). The funders had no role in study design, data collection, data analysis, interpretation, or writing of the report.