Predicting nodal metastases in papillary thyroid carcinoma using artificial intelligence
| Autor: | Antoinette R. Esce, Jordan P. Redemann, Andrew C. Sanchez, Garth T. Olson, David R. Martin, Shweta Agarwal, Joshua A. Hanson, Nathan H. Boyd |
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| Rok vydání: | 2021 |
| Předmět: |
Male
medicine.medical_specialty endocrine system diseases Thyroid Gland Convolutional neural network Sensitivity and Specificity 030218 nuclear medicine & medical imaging Thyroid carcinoma 03 medical and health sciences 0302 clinical medicine Artificial Intelligence medicine Humans Thyroid Neoplasms business.industry General Medicine Middle Aged medicine.disease Primary tumor ROC Curve Thyroid Cancer Papillary 030220 oncology & carcinogenesis Lymphatic Metastasis Surgery Histopathology Female Artificial intelligence Neural Networks Computer business NODAL Algorithms |
| Zdroj: | American journal of surgery. 222(5) |
| ISSN: | 1879-1883 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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