{"id":2875,"date":"2023-06-14T11:47:53","date_gmt":"2023-06-14T10:47:53","guid":{"rendered":"https:\/\/archive.belbi.bg.ac.rs\/2023\/?post_type=abstract&p=2875"},"modified":"2023-06-14T18:37:23","modified_gmt":"2023-06-14T17:37:23","slug":"supervised-machine-learning-approach-for-prediction-of-occult-lymph-node-metastasis-in-t1-t2-papillary-thyroid-carcinoma","status":"publish","type":"abstract","link":"https:\/\/archive.belbi.bg.ac.rs\/2023\/abstract\/supervised-machine-learning-approach-for-prediction-of-occult-lymph-node-metastasis-in-t1-t2-papillary-thyroid-carcinoma\/","title":{"rendered":"Supervised Machine Learning Approach for Prediction of Occult Lymph Node Metastasis in T1-T2 Papillary Thyroid Carcinoma"},"content":{"rendered":"\n

Marina Popovi\u0107 Krneta1*<\/sup>, Nemanja Kraj\u010dinovi\u01072<\/sup>, Zoran Bukumiri\u01073<\/sup>, and Miljana Tani\u01074,5<\/sup><\/p>\n\n\n\n

1<\/sup>Department of nuclear medicine, Institute for oncology and radiology of Serbia, Pasterova 14, 11000 Belgrade, Serbia<\/p>\n\n\n\n

2<\/sup>Faculty of Technical Sciences, University of Novi Sad, Trg Dositeja Obradovi\u0107a 6, 21000 Novi Sad, Serbia<\/p>\n\n\n\n

3<\/sup>Institute of Medical Statistics and Informatics, Faculty of Medicine, University of Belgrade, Dr Suboti\u0107a 8, 11000 Belgrade, Serbia<\/p>\n\n\n\n

4<\/sup>Department of Experimental Oncology, Institute for Oncology and Radiology of Serbia, Pasterova 14, 11000 Belgrade, Serbia<\/p>\n\n\n\n

5<\/sup>UCL Cancer Institute, 72 Huntley St London WC1E 6DD, United Kingdom<\/p>\n\n\n\n

marina.popovic1989 [at] gmail.com<\/p>\n\n\n\n

Abstract<\/strong><\/p>\n\n\n\n

This study aimed to assess and compare four machine learning (ML) based classifiers in predicting occult cervical lymph node metastasis (LNM) in clinically node-negative (cN0), T1-T2 papillary thyroid carcinoma (PTC) patients. <\/p>\n\n\n\n

The study cohort included 288 PTC patients who underwent total thyroidectomy and prophylactic central neck dissection with sentinel lymph node biopsy performed for lateral LNM identification. The classifiers, namely k-Nearest Neighbor (k-NN), Support Vector Machines, Decision Tree, and Logistic Regression were developed using patients\u2019 demographic and clinicopathological variables. Evaluation metrics such as area under the receiver operating characteristic curve (AUC), sensitivity, specificity, positive and negative predictive values (PPV and NPV), accuracy, and F1 and F2 scores were utilized for model comparison. <\/p>\n\n\n\n

The final ML classifier was selected based on achieving the highest specificity and the lowest degree of overfitting while maintaining a sensitivity of 95%. Among the evaluated models, the k-NN emerged as the best-performing, demonstrating an AUC of 0.72. The sensitivity, specificity, PPV, NPV, F1, and F2 scores were 98%, 27%, 56%, 93%, 72%, and 85%, respectively Furthermore, a web application was developed allowing users to predict the potential of cervical LNM and explore possibilities for further model development.<\/p>\n\n\n\n

The k-NN classifier incorporating patients\u2019 clinicopathological information shows potential in predicting LNM. Improved prediction models are necessary to identify patients at higher risk of LNM, guiding appropriate postsurgical treatment for high-risk individuals while minimizing unnecessary interventions for low-risk patients.<\/p>\n\n\n\n

Keywords:<\/strong> machine learning, papillary thyroid carcinoma; lymph node metastasis<\/p>\n\n\n\n

Acknowledgement:<\/strong> This research was supported by the Serbian Ministry of Science, Innovation and Technological development (451-03-47\/2023-01\/200043).<\/p>\n","protected":false},"excerpt":{"rendered":"

Marina Popovi\u0107 Krneta1*<\/sup>, Nemanja Kraj\u010dinovi\u01072<\/sup>, Zoran Bukumiri\u01073<\/sup>, and Miljana Tani\u01074,5<\/sup><\/p>\n

1<\/sup>Department of nuclear medicine, Institute for oncology and radiology of Serbia, Pasterova 14, 11000 Belgrade, Serbia<\/p>\n

2<\/sup>Faculty of Technical Sciences, University of Novi Sad, Trg Dositeja Obradovi\u0107a 6, 21000 Novi Sad, Serbia<\/p>\n

3<\/sup>Institute of Medical Statistics and Informatics, Faculty of Medicine, University of Belgrade, Dr Suboti\u0107a 8, 11000 Belgrade, Serbia<\/p>\n

4<\/sup>Department of Experimental Oncology, Institute for Oncology and Radiology of Serbia, Pasterova 14, 11000 Belgrade, Serbia<\/p>\n

5<\/sup>UCL Cancer Institute, 72 Huntley St London WC1E 6DD, United Kingdom<\/p>\n

Continue reading<\/span><\/i><\/a> <\/p>\n","protected":false},"author":162,"featured_media":0,"template":"","categories":[8],"tags":[],"yoast_head":"\nSupervised Machine Learning Approach for Prediction of Occult Lymph Node Metastasis in T1-T2 Papillary Thyroid Carcinoma - BelBi 2023<\/title>\n<meta name=\"robots\" content=\"noindex, follow\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Supervised Machine Learning Approach for Prediction of Occult Lymph Node Metastasis in T1-T2 Papillary Thyroid Carcinoma - BelBi 2023\" \/>\n<meta property=\"og:description\" content=\"Marina Popovi\u0107 Krneta1*, Nemanja Kraj\u010dinovi\u01072, Zoran Bukumiri\u01073, and Miljana Tani\u01074,51Department of nuclear medicine, Institute for oncology and radiology of Serbia, Pasterova 14, 11000 Belgrade, Serbia2Faculty of Technical Sciences, University of Novi Sad, Trg Dositeja Obradovi\u0107a 6, 21000 Novi Sad, Serbia3Institute of Medical Statistics and Informatics, Faculty of Medicine, University of Belgrade, Dr Suboti\u0107a 8, 11000 Belgrade, Serbia4Department of Experimental Oncology, Institute for Oncology and Radiology of Serbia, Pasterova 14, 11000 Belgrade, Serbia5UCL Cancer Institute, 72 Huntley St London WC1E 6DD, United Kingdom Continue reading\" \/>\n<meta property=\"og:url\" content=\"https:\/\/archive.belbi.bg.ac.rs\/2023\/abstract\/supervised-machine-learning-approach-for-prediction-of-occult-lymph-node-metastasis-in-t1-t2-papillary-thyroid-carcinoma\/\" \/>\n<meta property=\"og:site_name\" content=\"BelBi 2023\" \/>\n<meta property=\"article:modified_time\" content=\"2023-06-14T17:37:23+00:00\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"2 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/archive.belbi.bg.ac.rs\/2023\/abstract\/supervised-machine-learning-approach-for-prediction-of-occult-lymph-node-metastasis-in-t1-t2-papillary-thyroid-carcinoma\/\",\"url\":\"https:\/\/archive.belbi.bg.ac.rs\/2023\/abstract\/supervised-machine-learning-approach-for-prediction-of-occult-lymph-node-metastasis-in-t1-t2-papillary-thyroid-carcinoma\/\",\"name\":\"Supervised Machine Learning Approach for Prediction of Occult Lymph Node Metastasis in T1-T2 Papillary Thyroid Carcinoma - 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