Multiphase Dual-Energy Spectral CT-Based Deep Learning Method for the Noninvasive Prediction of Head and Neck Lymph Nodes Metastasis in Patients With Papillary Thyroid Cancer

Dan Jin; Xiaoqiong Ni; Xiaodong Zhang; Hongkun Yin; Huiling Zhang; Liang Xu; Rui Wang; Guohua Fan

doi:10.3389/fonc.2022.869895

Multiphase Dual-Energy Spectral CT-Based Deep Learning Method for the Noninvasive Prediction of Head and Neck Lymph Nodes Metastasis in Patients With Papillary Thyroid Cancer

Front Oncol. 2022 Apr 20:12:869895. doi: 10.3389/fonc.2022.869895. eCollection 2022.

Authors

Dan Jin¹, Xiaoqiong Ni¹, Xiaodong Zhang¹, Hongkun Yin², Huiling Zhang², Liang Xu¹, Rui Wang¹, Guohua Fan¹

Affiliations

¹ Department of Radiology, Second Affiliated Hospital of Soochow University, Suzhou, China.
² Department of Advanced Research, Infervision Medical Technology Co., Ltd, Beijing, China.

Abstract

Purpose: To develop deep learning (DL) models based on multiphase dual-energy spectral CT for predicting lymph nodes metastasis preoperatively and noninvasively in papillary thyroid cancer patients.

Methods: A total of 293 lymph nodes from 78 papillary thyroid cancer patients who underwent dual-energy spectral CT before lymphadenectomy were enrolled in this retrospective study. The lymph nodes were randomly divided into a development set and an independent testing set following a 4:1 ratio. Four single-modality DL models based on CT-A model, CT-V model, Iodine-A model and Iodine-V model and a multichannel DL model incorporating all modalities (Combined model) were proposed for the prediction of lymph nodes metastasis. A CT-feature model was also built on the selected CT image features. The model performance was evaluated with respect to discrimination, calibration and clinical usefulness. In addition, the diagnostic performance of the Combined model was also compared with four radiologists in the independent test set.

Results: The AUCs of the CT-A, CT-V, Iodine-A, Iodine-V and CT-feature models were 0.865, 0.849, 0.791, 0.785 and 0.746 in the development set and 0.830, 0.822, 0.744, 0.739 and 0.732 in the testing set. The Combined model had outperformed the other models and achieved the best performance with AUCs yielding 0.890 in the development set and 0.865 in the independent testing set. The Combined model showed good calibration, and the decision curve analysis demonstrated that the net benefit of the Combined model was higher than that of the other models across the majority of threshold probabilities. The Combined model also showed noninferior diagnostic capability compared with the senior radiologists and significantly outperformed the junior radiologists, and the interobserver agreement of junior radiologists was also improved after artificial intelligence assistance.

Conclusion: The Combined model integrating both CT images and iodine maps of the arterial and venous phases showed good performance in predicting lymph nodes metastasis in papillary thyroid cancer patients, which could facilitate clinical decision-making.

Keywords: deep learning; dual-energy CT (DECT); lymph nodes metastasis; multiphase; thyroid cancer.