Jing Zhou, Daxue Li, Jiahui Ren, Chun Huang, Shiying Yang, Mingyao Chen, Zhaoyu Wan, Jinhang He, Yuchen Zhuang, Song Xue, Lin Chun, Xinliang Su
{"title":"机器学习：预测cN0乳头状甲状腺癌侧淋巴结转移的多中心研究。","authors":"Jing Zhou, Daxue Li, Jiahui Ren, Chun Huang, Shiying Yang, Mingyao Chen, Zhaoyu Wan, Jinhang He, Yuchen Zhuang, Song Xue, Lin Chun, Xinliang Su","doi":"10.1210/clinem/dgaf070","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>The necessity of prophylactic lateral neck dissection for cN0 papillary thyroid carcinoma (PTC) remains debated. This study aimed to compare traditional nomograms with machine learning (ML) models for predicting ipsilateral lateral and level II, III, and IV lymph node metastasis (LNM).</p><p><strong>Methods: </strong>Data from 1616 PTC patients diagnosed via fine-needle aspiration biopsy from hospital A were split into training and testing sets (7:3). Two hundred forty-three patients from hospital B served as a validation set. Four dependent variables-ipsilateral lateral and level II, III, and IV LNM-were analyzed. Eight ML models [logistic regression, decision tree, random forest (RF), gradient boosting, support vector machine, K-nearest neighbor, Gaussian naive Bayes, neural networks] were developed and validated using 10-fold cross-validation and grid search hyperparameter tuning. Models were assessed using 11 metrics including accuracy, area under the curve (AUC), specificity, and sensitivity. The best was compared with nomograms using the probability-based ranking model approach (PMRA).</p><p><strong>Results: </strong>RF outperformed other approaches achieving accuracy, AUC, specificity, and sensitivity of 0.773/0.728, 0.858/0.799, 0.984/0.935, 0.757/0.807 in the testing/validation sets, respectively, for ipsilateral LLNM. A streamlined model based on the top 10 contributing features that includes ipsilateral central lymph node metastasis rate, extrathyroidal extension, and ipsilateral central lymph node metastasis number retained strong performance and clearly surpassed a traditional nomogram approach based on multiple metrics and PMRA analysis. Similar results were obtained for the other dependent variables, with the RF models relying on distinct but overlapping sets of features. Clinical tool implementation is facilitated via a web-based calculator for each of the 4 dependent variables.</p><p><strong>Conclusion: </strong>ML, especially RF, reliably predicts lateral LNM in cN0 PTC patients, outperforming traditional nomograms.</p>","PeriodicalId":50238,"journal":{"name":"Journal of Clinical Endocrinology & Metabolism","volume":" ","pages":"2762-2776"},"PeriodicalIF":5.1000,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12448597/pdf/","citationCount":"0","resultStr":"{\"title\":\"Machine Learning: A Multicenter Study on Predicting Lateral Lymph Node Metastasis in cN0 Papillary Thyroid Carcinoma.\",\"authors\":\"Jing Zhou, Daxue Li, Jiahui Ren, Chun Huang, Shiying Yang, Mingyao Chen, Zhaoyu Wan, Jinhang He, Yuchen Zhuang, Song Xue, Lin Chun, Xinliang Su\",\"doi\":\"10.1210/clinem/dgaf070\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>The necessity of prophylactic lateral neck dissection for cN0 papillary thyroid carcinoma (PTC) remains debated. This study aimed to compare traditional nomograms with machine learning (ML) models for predicting ipsilateral lateral and level II, III, and IV lymph node metastasis (LNM).</p><p><strong>Methods: </strong>Data from 1616 PTC patients diagnosed via fine-needle aspiration biopsy from hospital A were split into training and testing sets (7:3). Two hundred forty-three patients from hospital B served as a validation set. Four dependent variables-ipsilateral lateral and level II, III, and IV LNM-were analyzed. Eight ML models [logistic regression, decision tree, random forest (RF), gradient boosting, support vector machine, K-nearest neighbor, Gaussian naive Bayes, neural networks] were developed and validated using 10-fold cross-validation and grid search hyperparameter tuning. Models were assessed using 11 metrics including accuracy, area under the curve (AUC), specificity, and sensitivity. The best was compared with nomograms using the probability-based ranking model approach (PMRA).</p><p><strong>Results: </strong>RF outperformed other approaches achieving accuracy, AUC, specificity, and sensitivity of 0.773/0.728, 0.858/0.799, 0.984/0.935, 0.757/0.807 in the testing/validation sets, respectively, for ipsilateral LLNM. A streamlined model based on the top 10 contributing features that includes ipsilateral central lymph node metastasis rate, extrathyroidal extension, and ipsilateral central lymph node metastasis number retained strong performance and clearly surpassed a traditional nomogram approach based on multiple metrics and PMRA analysis. Similar results were obtained for the other dependent variables, with the RF models relying on distinct but overlapping sets of features. 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Machine Learning: A Multicenter Study on Predicting Lateral Lymph Node Metastasis in cN0 Papillary Thyroid Carcinoma.
Background: The necessity of prophylactic lateral neck dissection for cN0 papillary thyroid carcinoma (PTC) remains debated. This study aimed to compare traditional nomograms with machine learning (ML) models for predicting ipsilateral lateral and level II, III, and IV lymph node metastasis (LNM).
Methods: Data from 1616 PTC patients diagnosed via fine-needle aspiration biopsy from hospital A were split into training and testing sets (7:3). Two hundred forty-three patients from hospital B served as a validation set. Four dependent variables-ipsilateral lateral and level II, III, and IV LNM-were analyzed. Eight ML models [logistic regression, decision tree, random forest (RF), gradient boosting, support vector machine, K-nearest neighbor, Gaussian naive Bayes, neural networks] were developed and validated using 10-fold cross-validation and grid search hyperparameter tuning. Models were assessed using 11 metrics including accuracy, area under the curve (AUC), specificity, and sensitivity. The best was compared with nomograms using the probability-based ranking model approach (PMRA).
Results: RF outperformed other approaches achieving accuracy, AUC, specificity, and sensitivity of 0.773/0.728, 0.858/0.799, 0.984/0.935, 0.757/0.807 in the testing/validation sets, respectively, for ipsilateral LLNM. A streamlined model based on the top 10 contributing features that includes ipsilateral central lymph node metastasis rate, extrathyroidal extension, and ipsilateral central lymph node metastasis number retained strong performance and clearly surpassed a traditional nomogram approach based on multiple metrics and PMRA analysis. Similar results were obtained for the other dependent variables, with the RF models relying on distinct but overlapping sets of features. Clinical tool implementation is facilitated via a web-based calculator for each of the 4 dependent variables.
Conclusion: ML, especially RF, reliably predicts lateral LNM in cN0 PTC patients, outperforming traditional nomograms.
期刊介绍：
The Journal of Clinical Endocrinology & Metabolism is the world"s leading peer-reviewed journal for endocrine clinical research and cutting edge clinical practice reviews. Each issue provides the latest in-depth coverage of new developments enhancing our understanding, diagnosis and treatment of endocrine and metabolic disorders. Regular features of special interest to endocrine consultants include clinical trials, clinical reviews, clinical practice guidelines, case seminars, and controversies in clinical endocrinology, as well as original reports of the most important advances in patient-oriented endocrine and metabolic research. According to the latest Thomson Reuters Journal Citation Report, JCE&M articles were cited 64,185 times in 2008.
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