Lymph Node Metastases in Papillary Thyroid Carcinoma Can be Predicted by a Convolutional Neural Network: a Multi-Institution Study

Authors

Antoinette Esce, Department of Surgery, Division of Otolaryngology Head and Neck Surgery, University of New Mexico Health Sciences Center, Albuquerque, NM, USA.
Jordan P. Redemann, Department of Pathology, University of New Mexico Health Sciences Center, Albuquerque, NM, USA.
Garth T. Olson, Department of Surgery, Division of Otolaryngology Head and Neck Surgery, University of New Mexico Health Sciences Center, Albuquerque, NM, USA.
Joshua A. Hanson, Department of Pathology, University of New Mexico Health Sciences Center, Albuquerque, NM, USA.
Shweta Agarwal, Department of Pathology, University of New Mexico Health Sciences Center, Albuquerque, NM, USA.
Leonard Yenwongfai, Department of Pathology, University of Kentucky College of Medicine, Lexington, KY, USA.
Juanita Ferreira, Department of Pathology, University of Kentucky College of Medicine, Lexington, KY, USA.
Nathan H Boyd, Department of Surgery, Division of Otolaryngology Head and Neck Surgery, University of New Mexico Health Sciences Center, Albuquerque, NM, USA
Thèrése Bocklage, Department of Pathology, University of Kentucky College of Medicine, Lexington, KY, USA
David R. Martin, Department of Pathology, University of New Mexico Health Sciences Center, Albuquerque, NM, USA

Document Type

Article

Publication Date

11-1-2023

Abstract

OBJECTIVES: The presence of nodal metastases in patients with papillary thyroid carcinoma (PTC) has both staging and treatment implications. However, lymph nodes are often not removed during thyroidectomy. Prior work has demonstrated the capability of artificial intelligence (AI) to predict the presence of nodal metastases in PTC based on the primary tumor histopathology alone. This study aimed to replicate these results with multi-institutional data.

METHODS: Cases of conventional PTC were identified from the records of 2 large academic institutions. Only patients with complete pathology data, including at least 3 sampled lymph nodes, were included in the study. Tumors were designated "positive" if they had at least 5 positive lymph node metastases. First, algorithms were trained separately on each institution's data and tested independently on the other institution's data. Then, the data sets were combined and new algorithms were developed and tested. The primary tumors were randomized into 2 groups, one to train the algorithm and another to test it. A low level of supervision was used to train the algorithm. Board-certified pathologists annotated the slides. HALO-AI convolutional neural network and image software was used to perform training and testing. Receiver operator characteristic curves and the Youden J statistic were used for primary analysis.

RESULTS: There were 420 cases used in analyses, 45% of which were negative. The best performing single institution algorithm had an area under the curve (AUC) of 0.64 with a sensitivity and specificity of 65% and 61% respectively, when tested on the other institution's data. The best performing combined institution algorithm had an AUC of 0.84 with a sensitivity and specificity of 68% and 91% respectively.

CONCLUSION: A convolutional neural network can produce an accurate and robust algorithm that is capable of predicting nodal metastases from primary PTC histopathology alone even in the setting of multi-institutional data.

Recommended Citation

Esce A, Redemann JP, Olson GT, Hanson JA, Agarwal S, Yenwongfai L, Ferreira J, Boyd NH, Bocklage T, Martin DR. Lymph Node Metastases in Papillary Thyroid Carcinoma can be Predicted by a Convolutional Neural Network: a Multi-Institution Study. Ann Otol Rhinol Laryngol. 2023 Nov;132(11):1373-1379. doi: 10.1177/00034894231158464. Epub 2023 Mar 10. PMID: 36896865.