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Prediction of Cervical Lymph Node Metastasis in Clinically Node-Negative T1 and T2 Papillary Thyroid Carcinoma Using Supervised Machine Learning Approach
Papillary thyroid carcinoma (PTC) is generally considered an indolent cancer. However, patients with cervical lymph node metastasis (LNM) have a higher risk of local recurrence. This study evaluated and compared four machine learning (ML)-based classifiers to predict the presence of cervical LNM in...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10253788/ https://www.ncbi.nlm.nih.gov/pubmed/37297835 http://dx.doi.org/10.3390/jcm12113641 |
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author | Popović Krneta, Marina Šobić Šaranović, Dragana Mijatović Teodorović, Ljiljana Krajčinović, Nemanja Avramović, Nataša Bojović, Živko Bukumirić, Zoran Marković, Ivan Rajšić, Saša Djorović, Biljana Bazić Artiko, Vera Karličić, Mihajlo Tanić, Miljana |
author_facet | Popović Krneta, Marina Šobić Šaranović, Dragana Mijatović Teodorović, Ljiljana Krajčinović, Nemanja Avramović, Nataša Bojović, Živko Bukumirić, Zoran Marković, Ivan Rajšić, Saša Djorović, Biljana Bazić Artiko, Vera Karličić, Mihajlo Tanić, Miljana |
author_sort | Popović Krneta, Marina |
collection | PubMed |
description | Papillary thyroid carcinoma (PTC) is generally considered an indolent cancer. However, patients with cervical lymph node metastasis (LNM) have a higher risk of local recurrence. This study evaluated and compared four machine learning (ML)-based classifiers to predict the presence of cervical LNM in clinically node-negative (cN0) T1 and T2 PTC patients. The algorithm was developed using clinicopathological data from 288 patients who underwent total thyroidectomy and prophylactic central neck dissection, with sentinel lymph node biopsy performed to identify lateral LNM. The final ML classifier was selected based on the highest specificity and the lowest degree of overfitting while maintaining a sensitivity of 95%. Among the models evaluated, the k-Nearest Neighbor (k-NN) classifier was found to be the best fit, with an area under the receiver operating characteristic curve of 0.72, and sensitivity, specificity, positive and negative predictive values, F1 and F2 scores of 98%, 27%, 56%, 93%, 72%, and 85%, respectively. A web application based on a sensitivity-optimized kNN classifier was also created to predict the potential of cervical LNM, allowing users to explore and potentially build upon the model. These findings suggest that ML can improve the prediction of LNM in cN0 T1 and T2 PTC patients, thereby aiding in individual treatment planning. |
format | Online Article Text |
id | pubmed-10253788 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-102537882023-06-10 Prediction of Cervical Lymph Node Metastasis in Clinically Node-Negative T1 and T2 Papillary Thyroid Carcinoma Using Supervised Machine Learning Approach Popović Krneta, Marina Šobić Šaranović, Dragana Mijatović Teodorović, Ljiljana Krajčinović, Nemanja Avramović, Nataša Bojović, Živko Bukumirić, Zoran Marković, Ivan Rajšić, Saša Djorović, Biljana Bazić Artiko, Vera Karličić, Mihajlo Tanić, Miljana J Clin Med Article Papillary thyroid carcinoma (PTC) is generally considered an indolent cancer. However, patients with cervical lymph node metastasis (LNM) have a higher risk of local recurrence. This study evaluated and compared four machine learning (ML)-based classifiers to predict the presence of cervical LNM in clinically node-negative (cN0) T1 and T2 PTC patients. The algorithm was developed using clinicopathological data from 288 patients who underwent total thyroidectomy and prophylactic central neck dissection, with sentinel lymph node biopsy performed to identify lateral LNM. The final ML classifier was selected based on the highest specificity and the lowest degree of overfitting while maintaining a sensitivity of 95%. Among the models evaluated, the k-Nearest Neighbor (k-NN) classifier was found to be the best fit, with an area under the receiver operating characteristic curve of 0.72, and sensitivity, specificity, positive and negative predictive values, F1 and F2 scores of 98%, 27%, 56%, 93%, 72%, and 85%, respectively. A web application based on a sensitivity-optimized kNN classifier was also created to predict the potential of cervical LNM, allowing users to explore and potentially build upon the model. These findings suggest that ML can improve the prediction of LNM in cN0 T1 and T2 PTC patients, thereby aiding in individual treatment planning. MDPI 2023-05-24 /pmc/articles/PMC10253788/ /pubmed/37297835 http://dx.doi.org/10.3390/jcm12113641 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Popović Krneta, Marina Šobić Šaranović, Dragana Mijatović Teodorović, Ljiljana Krajčinović, Nemanja Avramović, Nataša Bojović, Živko Bukumirić, Zoran Marković, Ivan Rajšić, Saša Djorović, Biljana Bazić Artiko, Vera Karličić, Mihajlo Tanić, Miljana Prediction of Cervical Lymph Node Metastasis in Clinically Node-Negative T1 and T2 Papillary Thyroid Carcinoma Using Supervised Machine Learning Approach |
title | Prediction of Cervical Lymph Node Metastasis in Clinically Node-Negative T1 and T2 Papillary Thyroid Carcinoma Using Supervised Machine Learning Approach |
title_full | Prediction of Cervical Lymph Node Metastasis in Clinically Node-Negative T1 and T2 Papillary Thyroid Carcinoma Using Supervised Machine Learning Approach |
title_fullStr | Prediction of Cervical Lymph Node Metastasis in Clinically Node-Negative T1 and T2 Papillary Thyroid Carcinoma Using Supervised Machine Learning Approach |
title_full_unstemmed | Prediction of Cervical Lymph Node Metastasis in Clinically Node-Negative T1 and T2 Papillary Thyroid Carcinoma Using Supervised Machine Learning Approach |
title_short | Prediction of Cervical Lymph Node Metastasis in Clinically Node-Negative T1 and T2 Papillary Thyroid Carcinoma Using Supervised Machine Learning Approach |
title_sort | prediction of cervical lymph node metastasis in clinically node-negative t1 and t2 papillary thyroid carcinoma using supervised machine learning approach |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10253788/ https://www.ncbi.nlm.nih.gov/pubmed/37297835 http://dx.doi.org/10.3390/jcm12113641 |
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