Predicting nodal metastases in papillary thyroid carcinoma using artificial intelligence - 29/11/21
, Jordan P. Redemann b, Andrew C. Sanchez b, Garth T. Olson a, Joshua A. Hanson b, Shweta Agarwal b, Nathan H. Boyd a, David R. Martin bAbstract |
Background |
The presence of nodal metastases is important in the treatment of papillary thyroid carcinoma (PTC). We present our experience using a convolutional neural network (CNN) to predict the presence of nodal metastases in a series of PTC patients using visual histopathology from the primary tumor alone.
Methods |
174 cases of PTC were evaluated for the presence or absence of lymph metastases. The artificial intelligence (AI) algorithm was trained and tested on its ability to discern between the two groups.
Results |
The best performing AI algorithm demonstrated a sensitivity and specificity of 94% and 100%, respectively, when identifying nodal metastases.
Conclusion |
A CNN can be used to accurately predict the likelihood of nodal metastases in PTC using visual data from the primary tumor alone.
Le texte complet de cet article est disponible en PDF.Highlights |
• | Nodal metastases are an important prognostic factor in papillary thyroid carcinoma. |
• | Lymph nodes are not always removed during thyroidectomy. |
• | Convolutional neural networks can been used to analyze visual medical data. |
• | An algorithm can accurately predict nodal metastases from primary tumor data alone. |
Keywords : Papillary thyroid carcinoma, Artificial intelligence, Histopathology
Plan
Vol 222 - N° 5
P. 952-958 - novembre 2021 Retour au numéro
Article précédent
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