{"title":"利用机器学习预测分化型甲状腺癌患者对放射性碘的良好反应","authors":"Ogün Bülbül, Demet Nak","doi":"10.14639/0392-100X-N3029","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>If excellent response (ER) occurs after radioactive iodine (RAI) treatment in patients with differentiated thyroid carcinoma (DTC), the recurrence rate is low. Our study aims to predict ER at 6-24 months after RAI by using machine learning (ML) methods in which clinicopathological parameters are included in patients with DTC without distant metastasis.</p><p><strong>Methods: </strong>Treatment response of 151 patients with DTC without distant metastasis and who received RAI treatment was determined (ER/nonER). Thyroidectomy ± neck dissection pathology data, laboratory, and imaging findings before and after RAI treatment were introduced to ML models.</p><p><strong>Results: </strong>After RAI treatment, 118 patients had ER and 33 had nonER. Before RAI treatment, TgAb was positive in 29% of patients with ER and 55% of patients with nonER (p = 0.007). Eight of the ML models predicted ER with high area under the ROC curve (AUC) values (> 0.700). The model with the highest AUC value was extreme gradient boosting (AUC = 0.871), the highest accuracy shown by gradient boosting (81%).</p><p><strong>Conclusions: </strong>ML models may be used to predict ER in patients with DTC without distant metastasis.</p>","PeriodicalId":6890,"journal":{"name":"Acta Otorhinolaryngologica Italica","volume":"44 4","pages":"261-268"},"PeriodicalIF":2.1000,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11441520/pdf/","citationCount":"0","resultStr":"{\"title\":\"Predicting excellent response to radioiodine in differentiated thyroid cancer using machine learning.\",\"authors\":\"Ogün Bülbül, Demet Nak\",\"doi\":\"10.14639/0392-100X-N3029\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objective: </strong>If excellent response (ER) occurs after radioactive iodine (RAI) treatment in patients with differentiated thyroid carcinoma (DTC), the recurrence rate is low. Our study aims to predict ER at 6-24 months after RAI by using machine learning (ML) methods in which clinicopathological parameters are included in patients with DTC without distant metastasis.</p><p><strong>Methods: </strong>Treatment response of 151 patients with DTC without distant metastasis and who received RAI treatment was determined (ER/nonER). Thyroidectomy ± neck dissection pathology data, laboratory, and imaging findings before and after RAI treatment were introduced to ML models.</p><p><strong>Results: </strong>After RAI treatment, 118 patients had ER and 33 had nonER. Before RAI treatment, TgAb was positive in 29% of patients with ER and 55% of patients with nonER (p = 0.007). Eight of the ML models predicted ER with high area under the ROC curve (AUC) values (> 0.700). The model with the highest AUC value was extreme gradient boosting (AUC = 0.871), the highest accuracy shown by gradient boosting (81%).</p><p><strong>Conclusions: </strong>ML models may be used to predict ER in patients with DTC without distant metastasis.</p>\",\"PeriodicalId\":6890,\"journal\":{\"name\":\"Acta Otorhinolaryngologica Italica\",\"volume\":\"44 4\",\"pages\":\"261-268\"},\"PeriodicalIF\":2.1000,\"publicationDate\":\"2024-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11441520/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Acta Otorhinolaryngologica Italica\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.14639/0392-100X-N3029\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"OTORHINOLARYNGOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Acta Otorhinolaryngologica Italica","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.14639/0392-100X-N3029","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"OTORHINOLARYNGOLOGY","Score":null,"Total":0}
引用次数: 0
摘要
目的：如果分化型甲状腺癌（DTC）患者在接受放射性碘（RAI）治疗后出现极佳反应（ER），则复发率较低。我们的研究旨在通过机器学习（ML）方法预测RAI治疗后6-24个月的ER，其中包括无远处转移的DTC患者的临床病理参数：确定151名接受RAI治疗的无远处转移DTC患者的治疗反应（ER/nonER）。将 RAI 治疗前后的甲状腺切除术±颈部切除术病理数据、实验室和影像学结果引入 ML 模型：RAI治疗后，118名患者出现ER，33名患者出现非ER。RAI 治疗前，29% 的 ER 患者和 55% 的非 ER 患者 TgAb 呈阳性（p = 0.007）。八个 ML 模型预测 ER 的 ROC 曲线下面积（AUC）值较高（> 0.700）。AUC值最高的模型是极梯度增强模型（AUC = 0.871），梯度增强模型的准确率最高（81%）：结论：ML模型可用于预测无远处转移的DTC患者的ER。
Predicting excellent response to radioiodine in differentiated thyroid cancer using machine learning.
Objective: If excellent response (ER) occurs after radioactive iodine (RAI) treatment in patients with differentiated thyroid carcinoma (DTC), the recurrence rate is low. Our study aims to predict ER at 6-24 months after RAI by using machine learning (ML) methods in which clinicopathological parameters are included in patients with DTC without distant metastasis.
Methods: Treatment response of 151 patients with DTC without distant metastasis and who received RAI treatment was determined (ER/nonER). Thyroidectomy ± neck dissection pathology data, laboratory, and imaging findings before and after RAI treatment were introduced to ML models.
Results: After RAI treatment, 118 patients had ER and 33 had nonER. Before RAI treatment, TgAb was positive in 29% of patients with ER and 55% of patients with nonER (p = 0.007). Eight of the ML models predicted ER with high area under the ROC curve (AUC) values (> 0.700). The model with the highest AUC value was extreme gradient boosting (AUC = 0.871), the highest accuracy shown by gradient boosting (81%).
Conclusions: ML models may be used to predict ER in patients with DTC without distant metastasis.
期刊介绍：
Acta Otorhinolaryngologica Italica first appeared as “Annali di Laringologia Otologia e Faringologia” and was founded in 1901 by Giulio Masini.
It is the official publication of the Italian Hospital Otology Association (A.O.O.I.) and, since 1976, also of the Società Italiana di Otorinolaringoiatria e Chirurgia Cervico-Facciale (S.I.O.Ch.C.-F.).
The journal publishes original articles (clinical trials, cohort studies, case-control studies, cross-sectional surveys, and diagnostic test assessments) of interest in the field of otorhinolaryngology as well as clinical techniques and technology (a short report of unique or original methods for surgical techniques, medical management or new devices or technology), editorials (including editorial guests – special contribution) and letters to the Editor-in-Chief.
Articles concerning science investigations and well prepared systematic reviews (including meta-analyses) on themes related to basic science, clinical otorhinolaryngology and head and neck surgery have high priority.
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