Application of Deep Learning to the Diagnosis of Cervical Lymph Node Metastasis from Thyroid Cancer on Preoperative Computed Tomography
Thyroid World Congress ePoster Library. HA E. 06/22/19; 272143; 9
Dr. Eun Ju HA
Dr. Eun Ju HA
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Abstract
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Purpose: To develop a deep learning-based computer-aided diagnosis (CAD) system for use in the preoperative computed tomography (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. Pre-trained 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, with important sub-regions highlighted for further clinical review.

Results: The area under the receiver operating characteristic 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 showed evidence of CNN model-based classification of benign and metastatic lymph nodes by directly identifying the region of interest for 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.

 


Purpose: To develop a deep learning-based computer-aided diagnosis (CAD) system for use in the preoperative computed tomography (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. Pre-trained 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, with important sub-regions highlighted for further clinical review.

Results: The area under the receiver operating characteristic 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 showed evidence of CNN model-based classification of benign and metastatic lymph nodes by directly identifying the region of interest for 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.

 


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