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Application of deep learning to the diagnosis of cervical lymph node metastasis from thyroid cancer with CT

  • Jeong Hoon Lee
  • Eun Ju HaEmail author
  • Ju Han Kim
Imaging Informatics and Artificial Intelligence

Abstract

Purpose

To develop a deep learning–based computer-aided diagnosis (CAD) system for use in the CT diagnosis of cervical lymph node metastasis (LNM) in patients with thyroid cancer.

Methods

A total of 995 axial CT images that included benign (n = 647) and malignant (n = 348) lymph nodes were collected from 202 patients with thyroid cancer who underwent CT for surgical planning between July 2017 and January 2018. The datasets were randomly split into training (79.0%), validation (10.5%), and test (10.5%) datasets. Eight deep convolutional neural network (CNN) models were used to classify the images into metastatic or benign lymph nodes. Pretrained networks were used on the ImageNet and the best-performing algorithm was selected. Class-specific discriminative regions were visualized with attention heatmap using a global average pooling method.

Results

The area under the ROC curve (AUROC) for the tested algorithms ranged from 0.909 to 0.953. The sensitivity, specificity, and accuracy of the best-performing algorithm were all 90.4%, respectively. Attention heatmap highlighted important subregions for further clinical review.

Conclusion

A deep learning–based CAD system could accurately classify cervical LNM in patients with thyroid cancer on preoperative CT with an AUROC of 0.953. Whether this approach has clinical utility will require evaluation in a clinical setting.

Key Points

• A deep learning–based CAD system could accurately classify cervical lymph node metastasis. The AUROC for the eight tested algorithms ranged from 0.909 to 0.953.

• Of the eight models, the ResNet50 algorithm was the best-performing model for the validation dataset with 0.953 AUROC. The sensitivity, specificity, and accuracy of the ResNet50 model were all 90.4%, respectively, in the test dataset.

• Based on its high accuracy of 90.4%, we consider that this model may be useful in a clinical setting to detect LNM on preoperative CT in patients with thyroid cancer.

Keywords

Artificial intelligence Lymphatic metastasis Thyroid cancer Multidetector computed tomography 

Abbreviations

AUROC

Area under the receiver operating characteristic curve

CAD

Computer-aided diagnosis

CAM

Class activation mapping

CNN

Convolutional neural network

FNA

Fine needle aspiration

LNM

Lymph node metastasis

PTC

Papillary thyroid carcinoma

US

Ultrasonography

Notes

Funding

The authors state that this work was supported by the National Research Foundation of Korea (no. 2017R1C1B5016217).

Compliance with ethical standards

Guarantor

The scientific guarantor of this publication is Eun Ju Ha.

Conflict of interest

The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was obtained from all patients before they underwent US.

Ethical approval

This study was approved by our institutional review board.

Methodology

• retrospective

• case-control study

Supplementary material

330_2019_6098_MOESM1_ESM.doc (11.4 mb)
ESM 1 (DOC 11717 kb)

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Copyright information

© European Society of Radiology 2019

Authors and Affiliations

  1. 1.Division of Biomedical InformaticsSeoul National University Biomedical Informatics (SNUBI), Seoul National University College of MedicineSeoulRepublic of Korea
  2. 2.Department of RadiologyAjou University School of MedicineSuwonSouth Korea

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