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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...

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Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
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.
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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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