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Article(id=1322218753006355299, tenantId=1146029695717560320, journalId=1319373162502258839, issueId=1322218730805904072, articleNumber=null, orderNo=null, doi=10.16016/j.2097-0927.202503007, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1740931200000, receivedDateStr=2025-03-03, revisedDate=1745510400000, revisedDateStr=2025-04-25, acceptedDate=null, acceptedDateStr=null, onlineDate=1793171116352, onlineDateStr=2026-10-28, pubDate=1751212800000, pubDateStr=2025-06-30, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793171116352, onlineIssueDateStr=2026-10-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793171116352, creator=13701087609, updateTime=1793171116352, updator=13701087609, issue=Issue{id=1322218730805904072, tenantId=1146029695717560320, journalId=1319373162502258839, year='2025', volume='47', issue='12', pageStart='1261', pageEnd='1400', issueExtLink='null', onlineDate='null', pubDate='1751212800000', pubDateStr='2025-06-30', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793171111059, creator='13701087609', updateTime=1793325445066, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1322866054611714261, tenantId=1146029695717560320, journalId=1319373162502258839, issueId=1322218730805904072, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1322866054611714262, tenantId=1146029695717560320, journalId=1319373162502258839, issueId=1322218730805904072, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=1367, endPage=1375, ext={EN=ArticleExt(id=1322218753241236324, articleId=1322218753006355299, tenantId=1146029695717560320, journalId=1319373162502258839, language=EN, title=Construction and validation of a prediction model for central lymph node metastasis of papillary thyroid carcinoma based on contrast-enhanced venous phase CT radiomics, columnId=1322190400052380037, journalTitle=Journal of Army Medical University, columnName=Biomedical Engineering, runingTitle=null, highlight=null, articleAbstract=
Objective‍

To construct and validate an interpretable machine learning model integrating contrast-enhanced venous phase CT radiomics and clinical features for preoperative prediction of central lymph node metastasis (CLNM) of papillary thyroid carcinoma (PTC).

Methods‍

A case-control study was conducted on 243 histologically confirmed PTC patients. Among them, 196 patients from the First Affiliated Hospital of Army Medical University were randomly allocated into a training set (n=137) and an internal validation set (n=59) at a 7:3 ratio, while the left 47 patients from No. 958 Hospital of PLA Army were assigned into an external validation set. All participants completed standardized preoperative contrast-enhanced neck CT scanning. Quantitative radiomic features were systematically extracted from venous phase CT images through an open-source big data AI platform. Six machine learning classifiers, eXtreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Random Forest (RF), Logistic Regression (LR), k-Nearest Neighbors (KNN), and Decision Tree (DT) were implemented to construct clinical-radiomics integration models. The predictive performance was quantitatively assessed through receiver operating characteristic (ROC) curve analysis, with area under the curve (AUC) values calculated for training, internal validation, and external validation sets. Model interpretability was achieved using Shapley additive explanations (SHAP) framework, with particular focus on elucidating feature contributions in the best-performing model.

Results‍

The XGBoost model had an AUC value of 0.936 (95%CI: 0.895~0.976), 0.832 (95%CI: 0.724~0.941), and 0.772 (95%CI: 0.632~0.912) in training, internal and external validation sets, respectively. SHAP analysis revealed age as the most influential clinical predictor, with younger patients showing higher CLNM risk (OR=0.968, P=0.042).

Conclusion‍

Our machine learning-based clinic-radiomic prediction model demonstrates satisfactory performance in predicting CLNM of PTC, providing valuable references for clinical decision-making.

, authors=Xingyun HE1, Chen LIU1, Junze DU2, Qian LI3, Kang CHEN1, Rui FAN1, Jian WANG1, authorsList=Xingyun HE, Chen LIU, Junze DU, Qian LI, Kang CHEN, Rui FAN, Jian WANG, authorCompany=null, correspAuthors=Jian WANG, authorNote=null, correspAuthorsNote=null, copyrightStatement=© 2025 Journal of Army Medical University, copyrightOwner=Editorial Office of Journal of Army Medical University, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1322218754537276269, articleId=1322218753006355299, tenantId=1146029695717560320, journalId=1319373162502258839, language=CN, title=基于增强静脉期CT放射组学的甲状腺乳头状癌中央区淋巴结转移预测模型构建与验证, columnId=1322190400245318023, journalTitle=陆军军医大学学报, columnName=生物医学工程, runingTitle=null, highlight=null, articleAbstract=
目的

开发并验证一种整合静脉期增强CT放射组学特征与临床特征的机器学习模型，用于术前预测甲状腺乳头状癌（papillary thyroid carcinoma，PTC）患者中央区淋巴结转移（central lymph nod metastasis，CLNM）。

方法

采用病例-对照研究方法。共纳入经病理证实为PTC的患者243例：陆军军医大学第一附属医院196例，以7∶3的比例简单随机抽样分为训练集（n=137）和内部测试集（n=59）；中国人民解放军陆军第九五八医院的47例患者作为外部测试集。患者术前均行颈部CT增强检查。通过大数据人工智能能力开放平台从静脉期CT图像中提取放射组学特征。采用极端梯度提升（extreme gradient boosting，XGBoost）、支持向量机（support vector machine，SVM）、随机森林（random forest，RF）、逻辑回归（logistic regression，LR）、K最邻近算法（k-nearest neighbor，KNN）和决策树（decision tree，DT）6种机器学习算法构建临床-影像组学模型。通过受试者工作特征曲线（receiver operating characteristic curve，ROC）、ROC曲线下面积（area under curve，AUC）分析评估模型在训练集、内部测试集和外部测试集中的性能。对表现最佳模型使用沙普利加性解释（Shapley additive explanations，SHAP）进行解释。

结果

XGBoost模型在训练集、内部测试集和外部测试集的AUC分别为0.936（95%CI：0.895~0.976）、0.832（95%CI：0.724~0.941）和0.772（95%CI：0.632~0.912）。SHAP分析显示，年龄是最重要的预测因子，年轻患者CLNM风险更高（OR=0.968，P=0.042）。

结论

基于机器学习算法构建的临床-影像组学预测模型性能良好，通过预测甲状腺乳头状癌中央区淋巴结转移，为医生临床决策提供参考。

, authors=贺星云1, 刘晨1, 杜俊泽2, 李倩3, 陈康1, 范锐1, 王健1, authorsList=贺星云, 刘晨, 杜俊泽, 李倩, 陈康, 范锐, 王健, authorCompany=null, correspAuthors=王健, authorNote=null, correspAuthorsNote=
王健，E-mail：
WANG Jian，E-mail：
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Surgery，2013，154（4）：697-701； discussion701-3., articleTitle=The utility of routine preoperative cervical ultrasonography in patients undergoing thyroidectomy for differentiated thyroid cancer, refAbstract=null), Reference(id=1322218771910083537, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322218753006355299, doi=10.3389/fendo. 2022.937049, pmid=null, pmcid=null, year=2022, volume=13, issue=null, pageStart=937049, pageEnd=null, url=null, language=null, rfNumber=24, rfOrder=28, authorNames=CHANG Q G, ZHANG J M, WANG Y Q, journalName=Front Endocrinol （Lausanne）, refType=null, unstructuredReference=CHANG Q G，ZHANG J M，WANG Y Q，et al. Nomogram model based on preoperative serum thyroglobulin and clinical characteristics of papillary thyroid carcinoma to predict cervical lymph node metastasis［J］. 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PLoS One，2023，18（11）：e0294594., articleTitle=Predictive factors of lymph node metastasis in papillary thyroid cancer, refAbstract=null), Reference(id=1322218772128187347, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322218753006355299, doi=10.3389/fendo.2021.741698, pmid=null, pmcid=null, year=2021, volume=12, issue=null, pageStart=741698, pageEnd=null, url=null, language=null, rfNumber=26, rfOrder=30, authorNames=LI J J, WU X X, MAO N, journalName=Front Endocrinol （Lausanne）, refType=null, unstructuredReference=LI J J，WU X X，MAO N，et al. Computed tomography-based radiomics model to predict central cervical lymph node metastases in papillary thyroid carcinoma：a multicenter study［J］. 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Sci Rep，2020，10（1）：21336., articleTitle=Elucidating the constitutive relationship of calcium-silicate-hydrate gel using high throughput reactive molecular simulations and machine learning, refAbstract=null), Reference(id=1322218772304348117, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322218753006355299, doi=10.1155/2023/3270137, pmid=null, pmcid=null, year=2023, volume=2023, issue=null, pageStart=3270137, pageEnd=null, url=null, language=null, rfNumber=28, rfOrder=32, authorNames=HU W J, ZHUANG Y Z, TANG L, journalName=J Oncol, refType=null, unstructuredReference=HU W J，ZHUANG Y Z，TANG L，et al. Preoperative cervical lymph node metastasis prediction in papillary thyroid carcinoma：a noninvasive clinical multimodal radiomics （CMR） nomogram analysis［J］. J Oncol，2023，2023：3270137., articleTitle=Preoperative cervical lymph node metastasis prediction in papillary thyroid carcinoma：a noninvasive clinical multimodal radiomics （CMR） nomogram analysis, refAbstract=null)], funds=null, companyList=[AuthorCompany(id=1322218754780545902, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322218753006355299, xref=1, ext=[AuthorCompanyExt(id=1322218754788934511, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322218753006355299, companyId=1322218754780545902, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1Department of Radiology, Chongqing，China), AuthorCompanyExt(id=1322218754793128816, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322218753006355299, companyId=1322218754780545902, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1陆军军医大学（第三军医大学）第一附属医院：放射科，重庆)]), AuthorCompany(id=1322218756483433329, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322218753006355299, xref=2, ext=[AuthorCompanyExt(id=1322218756487627634, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322218753006355299, companyId=1322218756483433329, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2Department of Breast and Thyroid Surgery，First Affiliated Hospital，Army Medical University （Third Military Medical University），Chongqing, Chongqing，China), AuthorCompanyExt(id=1322218756496016243, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322218753006355299, companyId=1322218756483433329, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2陆军军医大学（第三军医大学）第一附属医院：乳腺甲状腺外科，重庆)]), AuthorCompany(id=1322218756626039668, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322218753006355299, xref=3, ext=[AuthorCompanyExt(id=1322218756634428277, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322218753006355299, companyId=1322218756626039668, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=3Department of Radiology，No. 958 Hospital of PLA Army，Chongqing，China), AuthorCompanyExt(id=1322218756642816886, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322218753006355299, companyId=1322218756626039668, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=3中国人民解放军陆军第九五八医院放射科，重庆)])], figs=[ArticleFig(id=1322218761462072226, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322218753006355299, language=EN, label=null, caption=null, figureFileSmall=cqk99e18EubnS39CecLzlA==, figureFileBig=KotJMh5LAQWRBfb7g/HgOw==, tableContent=null), ArticleFig(id=1322218761550152611, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322218753006355299, language=CN, label=图1, caption=运用LASSO回归进行影像组学特征筛选

A：纳入变量的LASSO系数曲线 不同色线示：特征变量的相应系数；B：5折交叉验证方法确定LASSO模型中最佳λ值的过程图

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A：XGboost模型蜂巢图；B：XGboost模型特征重要性图

, figureFileSmall=7SRBQTn1uDdlDa1XdZlZIQ==, figureFileBig=oMQYJ6AGWY0tWuVRI/DJ5w==, tableContent=null), ArticleFig(id=1322218762598728618, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322218753006355299, language=EN, label=null, caption=null, figureFileSmall=null, figureFileBig=null, tableContent=
指标训练集（n=137）内部测试集（n=59）外部测试集（n=47）
未转移（n=67）转移（n=70）P未转移（n=30）转移（n=29）P未转移（n=22）转移（n=25）P
性别0.2771.0000.808
女性55（82）51（73）22（73）21（72）16（73）20（80）
男性12（18）19（27）8（27）8（28）6（27）5（20）
年龄/岁43.03±11.2938.51±11.560.02047.87±10.1437．00±12.15<0.00144.73±11.8844.24±11.190.880
BMI/（kg/m2）23.90
（21.76，26.54）		23.69
（21.16，25.77）		0.26023.76
（22.05，25.45）		23.70
（21.20，27.06）		0.80025.00
（24.00，27.00）		24.00
（22.00，26.00）		0.250
包膜是否受侵0.7190.3531.000
否58（87）63（90）29（97）26（90）20（91）22（88）
是9（13）7（10）1（3）3（10）2（9）3（12）
是否有结节性甲状腺肿1.0000.6020.052
否55（82）57（81）21（70）23（79）22（100）20（80）
是12（18）13（19）9（30）6（21）05（20）
是否有甲状腺炎0.5631.0000.480
否49（73）47（67）20（67）20（69）16（73）21（84）
是18（27）23（33）10（33）9（31）6（27）4（16）
原发肿瘤最大直径/cm1.36±1.291.51±0.840.4201.27±0.601.64±0.750.0401.11±0.401.15±0.520.800
病程/月4.51±7.666.86±10.790.1403.82±5.095.23±10.380.5105.33±7.974.19±5.320.570
T3/（nmol/L）2.02（1.77，2.34）1.87（1.62，2.20）0.0501.93（1.80，2.03）1.95（1.58，2.35）0.8701.98（1.85，2.11）1.79（1.63，2.03）0.050
T4/（nmol/L）111.41±20.75103.65±18.140.020110.71±21.87105.25±20.750.33099.03±12.2998.59±20.460.930
FT3/（pmol/L）5.06（4.46，5.67）5.12（4.67，5.64）0.5504.85（4.58，5.42）5.13（4.60，5.69）0.6304.98（4.71，5.16）4.80（4.56，5.15）0.460
FT4/（pmol/L）16.94
（15.12，19.48）		17.09
（15.14，18.90）		0.63016.89（15.18，18.13）16.84（15.28，19.13）0.86014.20（13.97，16.09）15.79（13.50，17.63）0.370
TSH/（µIU/mL）2.48±1.832.19±1.760.3402.65±2.073.10±3.800.5802.04±0.962.58±2.120.250
TG/（ng/mL）36.18±88.5034.44±88.900.91039.15±92.1038.62±91.970.98013.35±18.5822.99±40.980.300
TGAb/（IU/mL）86.42±268.02237.89±558.360.04092.21±189.67221.30±577.640.26078.26±108.8752.23±87.830.380
TMAb/%9.17±9.4011.98±10.460.1008.65±8.7512.63±10.900.13011.09±9.087.32±7.140.130
TPoab/（IU/mL）53.15±73.8283.84±162.550.16034.91±48.6995.80±156.530.05087.56±165.4353.41±142.460.460
), ArticleFig(id=1322218762686809003, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322218753006355299, language=CN, label=表1, caption=

患者临床因素组间各指标比较 ［例（%），x¯±s，M（P25，P75）］

, figureFileSmall=null, figureFileBig=null, tableContent=
指标训练集（n=137）内部测试集（n=59）外部测试集（n=47）
未转移（n=67）转移（n=70）P未转移（n=30）转移（n=29）P未转移（n=22）转移（n=25）P
性别0.2771.0000.808
女性55（82）51（73）22（73）21（72）16（73）20（80）
男性12（18）19（27）8（27）8（28）6（27）5（20）
年龄/岁43.03±11.2938.51±11.560.02047.87±10.1437．00±12.15<0.00144.73±11.8844.24±11.190.880
BMI/（kg/m2）23.90
（21.76，26.54）		23.69
（21.16，25.77）		0.26023.76
（22.05，25.45）		23.70
（21.20，27.06）		0.80025.00
（24.00，27.00）		24.00
（22.00，26.00）		0.250
包膜是否受侵0.7190.3531.000
否58（87）63（90）29（97）26（90）20（91）22（88）
是9（13）7（10）1（3）3（10）2（9）3（12）
是否有结节性甲状腺肿1.0000.6020.052
否55（82）57（81）21（70）23（79）22（100）20（80）
是12（18）13（19）9（30）6（21）05（20）
是否有甲状腺炎0.5631.0000.480
否49（73）47（67）20（67）20（69）16（73）21（84）
是18（27）23（33）10（33）9（31）6（27）4（16）
原发肿瘤最大直径/cm1.36±1.291.51±0.840.4201.27±0.601.64±0.750.0401.11±0.401.15±0.520.800
病程/月4.51±7.666.86±10.790.1403.82±5.095.23±10.380.5105.33±7.974.19±5.320.570
T3/（nmol/L）2.02（1.77，2.34）1.87（1.62，2.20）0.0501.93（1.80，2.03）1.95（1.58，2.35）0.8701.98（1.85，2.11）1.79（1.63，2.03）0.050
T4/（nmol/L）111.41±20.75103.65±18.140.020110.71±21.87105.25±20.750.33099.03±12.2998.59±20.460.930
FT3/（pmol/L）5.06（4.46，5.67）5.12（4.67，5.64）0.5504.85（4.58，5.42）5.13（4.60，5.69）0.6304.98（4.71，5.16）4.80（4.56，5.15）0.460
FT4/（pmol/L）16.94
（15.12，19.48）		17.09
（15.14，18.90）		0.63016.89（15.18，18.13）16.84（15.28，19.13）0.86014.20（13.97，16.09）15.79（13.50，17.63）0.370
TSH/（µIU/mL）2.48±1.832.19±1.760.3402.65±2.073.10±3.800.5802.04±0.962.58±2.120.250
TG/（ng/mL）36.18±88.5034.44±88.900.91039.15±92.1038.62±91.970.98013.35±18.5822.99±40.980.300
TGAb/（IU/mL）86.42±268.02237.89±558.360.04092.21±189.67221.30±577.640.26078.26±108.8752.23±87.830.380
TMAb/%9.17±9.4011.98±10.460.1008.65±8.7512.63±10.900.13011.09±9.087.32±7.140.130
TPoab/（IU/mL）53.15±73.8283.84±162.550.16034.91±48.6995.80±156.530.05087.56±165.4353.41±142.460.460
), ArticleFig(id=1322218762779083692, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322218753006355299, language=EN, label=null, caption=null, figureFileSmall=null, figureFileBig=null, tableContent=
因素BSEOROR（95%CI）Wald χ²P
BMI-0.0640.046220.9380.938（0.854~1.025）1.9130.167
TPoab0.0020.001661.0021.002（0.999~1.006）1.7690.183
TMAb0.0290.017931.0301.030（0.995~1.068）2.6340.105
TGAb0.0010.000651.0011.001（1.000~1.003）2.9340.087
TG0.0000.001941.0001.000（0.996~1.004）0.0130.908
TSH-0.0940.097490.9100.910（0.746~1.099）0.9290.335
FT4-0.0500.056380.9510.951（0.849~1.061）0.7800.377
FT30.1280.179261.1361.136（0.803~1.641）0.5070.477
T4-0.0210.009300.9790.979（0.961~0.997）5.0810.024
T3-0.7630.410680.4660.466（0.203~1.027）3.4520.063
病程0.0280.019421.0281.028（0.991~1.071）2.0740.150
原发肿瘤最大直径0.1370.172441.1471.147（0.833~1.684）0.6340.426
是否有甲状腺炎0.2870.375121.3321.332（0.640~2.804）0.5850.445
是否有结节性甲状腺肿0.0440.442701.0451.045（0.437~2.517）0.0100.920
包膜是否受侵-0.3340.535800.7160.716（0.242~2.043）0.3880.533
年龄-0.0350.015540.9660.966（0.936~0.995）5.0400.025
性别0.5350.416841.7081.708（0.762~3.950）1.6490.199
), ArticleFig(id=1322218762867164077, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322218753006355299, language=CN, label=表2, caption=

临床特征单因素Logistic回归

, figureFileSmall=null, figureFileBig=null, tableContent=
因素BSEOROR（95%CI）Wald χ²P
BMI-0.0640.046220.9380.938（0.854~1.025）1.9130.167
TPoab0.0020.001661.0021.002（0.999~1.006）1.7690.183
TMAb0.0290.017931.0301.030（0.995~1.068）2.6340.105
TGAb0.0010.000651.0011.001（1.000~1.003）2.9340.087
TG0.0000.001941.0001.000（0.996~1.004）0.0130.908
TSH-0.0940.097490.9100.910（0.746~1.099）0.9290.335
FT4-0.0500.056380.9510.951（0.849~1.061）0.7800.377
FT30.1280.179261.1361.136（0.803~1.641）0.5070.477
T4-0.0210.009300.9790.979（0.961~0.997）5.0810.024
T3-0.7630.410680.4660.466（0.203~1.027）3.4520.063
病程0.0280.019421.0281.028（0.991~1.071）2.0740.150
原发肿瘤最大直径0.1370.172441.1471.147（0.833~1.684）0.6340.426
是否有甲状腺炎0.2870.375121.3321.332（0.640~2.804）0.5850.445
是否有结节性甲状腺肿0.0440.442701.0451.045（0.437~2.517）0.0100.920
包膜是否受侵-0.3340.535800.7160.716（0.242~2.043）0.3880.533
年龄-0.0350.015540.9660.966（0.936~0.995）5.0400.025
性别0.5350.416841.7081.708（0.762~3.950）1.6490.199
), ArticleFig(id=1322218763034936238, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322218753006355299, language=EN, label=null, caption=null, figureFileSmall=null, figureFileBig=null, tableContent=
因素BSEOROR（95%CI）Wald χ²P
T4-0.0190.009430.9810.980（0.962~0.998）4.1980.040
年龄-0.0320.015810.9680.968 （0.937~0.998）4.1290.042
), ArticleFig(id=1322218763127210927, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322218753006355299, language=CN, label=表3, caption=

临床特征多因素Logistic回归

, figureFileSmall=null, figureFileBig=null, tableContent=
因素BSEOROR（95%CI）Wald χ²P
T4-0.0190.009430.9810.980（0.962~0.998）4.1980.040
年龄-0.0320.015810.9680.968 （0.937~0.998）4.1290.042
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基于增强静脉期CT放射组学的甲状腺乳头状癌中央区淋巴结转移预测模型构建与验证
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贺星云 1 , 刘晨 1 , 杜俊泽 2 , 李倩 3 , 陈康 1 , 范锐 1 , 王健 1
陆军军医大学学报 | 生物医学工程 2025,47(12): 1367-1375
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陆军军医大学学报 |生物医学工程 2025 , 47 (12) : 1367 -1375
基于增强静脉期CT放射组学的甲状腺乳头状癌中央区淋巴结转移预测模型构建与验证
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贺星云1, 刘晨1, 杜俊泽2, 李倩3, 陈康1, 范锐1, 王健1
作者信息
  • 1陆军军医大学（第三军医大学）第一附属医院：放射科，重庆
  • 2陆军军医大学（第三军医大学）第一附属医院：乳腺甲状腺外科，重庆
  • 3中国人民解放军陆军第九五八医院放射科，重庆
通讯作者:
王健，E-mail：
WANG Jian，E-mail：
Construction and validation of a prediction model for central lymph node metastasis of papillary thyroid carcinoma based on contrast-enhanced venous phase CT radiomics
Xingyun HE1, Chen LIU1, Junze DU2, Qian LI3, Kang CHEN1, Rui FAN1, Jian WANG1
Affiliations
  • 1Department of Radiology, Chongqing，China
  • 2Department of Breast and Thyroid Surgery，First Affiliated Hospital，Army Medical University （Third Military Medical University），Chongqing, Chongqing，China
  • 3Department of Radiology，No. 958 Hospital of PLA Army，Chongqing，China
出版时间: 2025-06-30 doi: 10.16016/j.2097-0927.202503007
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摘要
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目的

开发并验证一种整合静脉期增强CT放射组学特征与临床特征的机器学习模型，用于术前预测甲状腺乳头状癌（papillary thyroid carcinoma，PTC）患者中央区淋巴结转移（central lymph nod metastasis，CLNM）。

方法

采用病例-对照研究方法。共纳入经病理证实为PTC的患者243例：陆军军医大学第一附属医院196例，以7∶3的比例简单随机抽样分为训练集（n=137）和内部测试集（n=59）；中国人民解放军陆军第九五八医院的47例患者作为外部测试集。患者术前均行颈部CT增强检查。通过大数据人工智能能力开放平台从静脉期CT图像中提取放射组学特征。采用极端梯度提升（extreme gradient boosting，XGBoost）、支持向量机（support vector machine，SVM）、随机森林（random forest，RF）、逻辑回归（logistic regression，LR）、K最邻近算法（k-nearest neighbor，KNN）和决策树（decision tree，DT）6种机器学习算法构建临床-影像组学模型。通过受试者工作特征曲线（receiver operating characteristic curve，ROC）、ROC曲线下面积（area under curve，AUC）分析评估模型在训练集、内部测试集和外部测试集中的性能。对表现最佳模型使用沙普利加性解释（Shapley additive explanations，SHAP）进行解释。

结果

XGBoost模型在训练集、内部测试集和外部测试集的AUC分别为0.936（95%CI：0.895~0.976）、0.832（95%CI：0.724~0.941）和0.772（95%CI：0.632~0.912）。SHAP分析显示，年龄是最重要的预测因子，年轻患者CLNM风险更高（OR=0.968，P=0.042）。

结论

基于机器学习算法构建的临床-影像组学预测模型性能良好，通过预测甲状腺乳头状癌中央区淋巴结转移，为医生临床决策提供参考。

甲状腺乳头状癌  /  机器学习  /  影像组学  /  预测模型
Objective‍

To construct and validate an interpretable machine learning model integrating contrast-enhanced venous phase CT radiomics and clinical features for preoperative prediction of central lymph node metastasis (CLNM) of papillary thyroid carcinoma (PTC).

Methods‍

A case-control study was conducted on 243 histologically confirmed PTC patients. Among them, 196 patients from the First Affiliated Hospital of Army Medical University were randomly allocated into a training set (n=137) and an internal validation set (n=59) at a 7:3 ratio, while the left 47 patients from No. 958 Hospital of PLA Army were assigned into an external validation set. All participants completed standardized preoperative contrast-enhanced neck CT scanning. Quantitative radiomic features were systematically extracted from venous phase CT images through an open-source big data AI platform. Six machine learning classifiers, eXtreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Random Forest (RF), Logistic Regression (LR), k-Nearest Neighbors (KNN), and Decision Tree (DT) were implemented to construct clinical-radiomics integration models. The predictive performance was quantitatively assessed through receiver operating characteristic (ROC) curve analysis, with area under the curve (AUC) values calculated for training, internal validation, and external validation sets. Model interpretability was achieved using Shapley additive explanations (SHAP) framework, with particular focus on elucidating feature contributions in the best-performing model.

Results‍

The XGBoost model had an AUC value of 0.936 (95%CI: 0.895~0.976), 0.832 (95%CI: 0.724~0.941), and 0.772 (95%CI: 0.632~0.912) in training, internal and external validation sets, respectively. SHAP analysis revealed age as the most influential clinical predictor, with younger patients showing higher CLNM risk (OR=0.968, P=0.042).

Conclusion‍

Our machine learning-based clinic-radiomic prediction model demonstrates satisfactory performance in predicting CLNM of PTC, providing valuable references for clinical decision-making.

‍papillary thyroid carcinoma  /  machine learning  /  radiomics  /  prediction model
贺星云, 刘晨, 杜俊泽, 李倩, 陈康, 范锐, 王健. 基于增强静脉期CT放射组学的甲状腺乳头状癌中央区淋巴结转移预测模型构建与验证. 陆军军医大学学报, 2025 , 47 (12) : 1367 -1375 . DOI: 10.16016/j.2097-0927.202503007
Xingyun HE, Chen LIU, Junze DU, Qian LI, Kang CHEN, Rui FAN, Jian WANG. Construction and validation of a prediction model for central lymph node metastasis of papillary thyroid carcinoma based on contrast-enhanced venous phase CT radiomics[J]. Journal of Army Medical University, 2025 , 47 (12) : 1367 -1375 . DOI: 10.16016/j.2097-0927.202503007
正文
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甲状腺乳头状癌（papillary thyroid carcinoma，PTC）作为甲状腺癌的主要亚型（占比85%），其发病率增速居恶性肿瘤首位［1］。颈部中央区淋巴结转移（central lymph nod metastasis，CLNM）在甲状腺癌患者中很常见［2］，且直接影响临床分期、手术范围以及患者的预后。对于中央区淋巴结（central lymph node，CLN）的治疗，美国甲状腺协会指南建议可对临床淋巴结阴性的PTC患者进行预防性中央区淋巴结清扫（prophylactic central lymph node dissection，pCLND），尤其是甲状腺包膜受侵或肿瘤大小>4 cm的患者［3］。然而，由于仅使用两种病理特征的建议可能不足以预测亚临床CLNM，导致pCLND以常规或根据临床医生的个人偏好进行。此外，pCLND可能会增加手术并发症的风险，如喉返神经损伤和甲状旁腺功能减退。尽管大多数PTC患者预后良好，但约13.3%的患者在初次手术后会出现复发，且大多数PTC复发发生在颈部淋巴结［4-5］。CLNM的患者即使接受了淋巴结清扫术和大剂量放射性碘消融治疗，其局部复发率仍较高，这会增加并发症的发病率并降低患者生存质量［6-7］。因此，准确的术前识别有CLNM风险的PTC患者对于患者的长期管理和预后至关重要［8-9］。由于CLN位置较深且体积较小，术前超声对CLNM的检出敏感度仅为20%~31%［10］，且肿瘤和内脏实质的密度相似，淋巴结病变较小，平扫CT准确区分CLNM比较困难［11］。增强CT可通过增强各淋巴结的正常实质而不增强或仅轻微增强转移性颈部淋巴结，进一步增加病灶与邻近组织的密度差异。因此，增强CT可以提高转移淋巴结的检出率［12］。然而，目前增强CT的诊断功效仍然受到不同放射科医师主观判断的限制，不能满足临床需求。
随着人工智能（artificial intelligence，AI）技术的加速进步，机器学习（machine learning，ML）在图像处理和特征挖掘方面取得了显著进展，特别是在基准图像的分类任务中［13-14］。然而，它的功效在很大程度上依赖于大样本量，这使得它在需要从单个患者图像中提取大量数据的医疗应用中具有挑战性［15］。为了应对这一挑战，放射组学成为了一种解决方案，该领域涉及从数字图像中高通量提取和分析大量定量特征，超越了人类视觉感知的限制［16］。通过识别这些成像特征和潜在组织信息之间的相关性，放射组学可以提高评估肿瘤生物学特征和预后的性能，从而有助于优化复杂的临床决策过程。鉴于此，本研究基于CT放射组学的潜力，旨在建立可解释的ML模型来预测PTC患者CLNM，从而提供有助于个体化的治疗策略。
1　资料与方法
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1.1　数据收集
本研究采用病例-对照研究方法，经陆军军医大学第一附属医院伦理委员会批准［批准号：（B）KY2023099］，并且免除了知情同意的需要。本研究回顾性收集了2021年1月至2022年11月2个中心共243例经组织病理学验证的PTC患者的数据。其中，中心1（陆军军医大学第一附属医院乳腺甲状腺外科）196例，采用简单随机抽样将患者以7∶3的比例随机分为训练集（n=137）和内部测试集（n=59）。将中心2（中国人民解放军陆军第九五八医院乳腺甲状腺外科）的47例患者作为外部测试集。纳入标准：①接受初始手术治疗并经病理证实的PTC患者；②术前2周进行颈部CT增强扫描；③因细针穿刺活检（fine needle aspiration，FNA）证实CLNM或术前超声结果可疑而行颈中央区淋巴结清扫术（central lymph node dissection，CLND）的患者，临床资料完整；④既往无甲状腺相关疾病治疗。排除标准：①多灶性PTC；②术前抗肿瘤治疗；③影像图像不清或临床资料不全；④PTC长径<5 mm。收集患者的临床数据，包括年龄、性别、体质指数（body mass Index，BMI）、病程、原发肿瘤最大直径、T3、T4、FT3、FT4、是否有结节性甲状腺肿、是否有甲状腺炎、包膜是否受侵，以及促甲状腺激素（thyroid stimulating hormone，TSH）、抗甲状腺过氧化物酶抗体（thyroid peroxidase antibody，TPoab）、抗甲状腺微粒抗体（thyroid microsomal auto-antibodies，TMAb）、抗甲状腺球蛋白抗体（thyroglobulin antibody，TGAb）、甲状腺球蛋白（thyroglobulin，TG）水平。
1.2　样本量计算
本研究根据每个变量事件指标计算样本量，根据前期调研［17］和预实验计算样本量，预期CLNM发生率约为50%，进入LASSO回归分析的因素有10~12个，依据样本量计算公式：样本量=变量数×10÷事件发生率，本研究所需样本量不少于240例（12×10÷0.5=240例）。
1.3　CT扫描方案
采用德国Siemens公司的Somatom Definition AS 64排螺旋CT机或Somatom Definition Flash双源螺旋CT机扫描。扫描参数：管电压140 kV，管电流为自动设置，螺距为1.2~1.5。增强扫描采用非离子型对比剂碘海醇（含碘300 mg/mL），注射剂量80~100 mL，注射速度3.5~4.0 mL/s，于升主动脉内取约1 cm2的圆形感兴趣区（region of interest，ROI），对比剂开始注射10 s后进行连续轴向扫描，在实时重建图像上当其CT值达到触发阈值（120 HU）后进行动脉期扫描，顺延15~20 s进行静脉期扫描。重建参数：软组织窗位30~60 HU，窗宽200~450 HU，层间距1 mm，层厚1 mm。
1.4　CT图像分割
将数据上传至陆军军医大学第一附属医院大数据人工智能能力开放平台（版本：西南医院，https://keyan.deepwise.com，杭州深睿博联科技有限公司，中国浙江），采用手动勾画的方法，在颈部增强CT静脉期图像上，2位拥有超过10年经验的放射科医生勾画ROI，在图像勾画过程中尽量包含肿瘤边界，他们知晓甲状腺癌诊断信息，但对临床和组织病理学数据保持盲态。1个月后，通过简单随机抽样法从243例患者中选择30名患者，并由2位放射科医生重新描绘ROI。计算组内相关系数（interclass correlation coefficient，ICC）评估肿瘤描述一致性。
1.5　影像组学特征提取
本研究使用了大数据人工智能能力开放平台进行了特征提取。使用B样条插值采样技术对不同分辨率的图像进行重新采样，使所有图像被重新采样到相同的分辨率。基于CT图像，本研究进行了预处理和图像变换。使用了9种图像预处理方法，包括小波、高斯拉普拉斯算子、平方、平方根、对数、指数、梯度、二维局部二值模式、三维局部二值模式，并调整了最佳参数。在图像处理后，从每个ROI中获得了3个类别的特征：一阶特征、形状特征和纹理特征。一阶特征使用均值、方差、熵、标准差等基本的一阶统计量来描述ROI内像素的强度和分布。形状特征描述了ROI在2D或3D中的形状和大小信息，例如体积、直径、圆度等。纹理特征主要包括灰度共生矩阵、灰度尺寸区矩阵、灰度游程长度矩阵、灰度依赖矩阵和邻近灰度调子差异矩阵，这些特征描述了某个像素与周围像素之间的灰度关系。
1.6　特征选择和模型建立
本研究应用了基于Z分数的归一化方法标准化放射组学特征。首先，通过Pearson相关性分析，设定0.8作为阈值，超过此阈值的冗余特征被排除。然后，利用方差分析（analysis of variance，ANOVA）中的F检验统计量筛选出前1%的特征。最后，通过最小绝对收缩和选择算子（least absolute shrinkage and selection operator，LASSO）回归筛选出最终的放射组学特征。采用单因素和多因素Logistic回归分析筛选临床风险因素。对选定的组学特征及临床风险因素利用决策树（decision tree，DT）、随机森林（random forest，RF）、支持向量机（support vector machine，SVM）、逻辑回归（logistic regression，LR）、K最邻近算法（k-nearest neighbor，KNN）和极端梯度提升（extreme gradient boosting，XGBoost）这6种机器学习算法建立模型。为了量化模型的区分能力，本研究采用了受试者工作特征曲线下面积（area under curve，AUC）作为主要评价指标。
1.7　模型解释性分析
采用沙普利加性解释（Shapley additive explanations，SHAP）方法进行解释性分析，旨在解析单个变量对预测结果的贡献度。SHAP值图可视化特征对模型预测的方向性影响，而变量重要性图则展示特征的全局权重排序。通过分析每个特征的重要性，并根据它们各自的SHAP值降序排列，确定了关键的预测因素，从而增强了对影响受检查患者CLNM的复杂关系的理解。
1.8　统计学分析
采用R语言软件3.6.5对筛选后影像组学特征及临床特征进行统计学分析，符合正态分布的计量资料以x¯±s表示，组间比较采用独立样本t检验；不符合正态分布的计量资料以M（P25，P75）表示；组间比较采用Mann Whitney U检验。计数资料以例（％）表示，组间比较采用χ2检验。通过受试者工作特征曲线（receiver operating characteristic curve，ROC）分析计算AUC、灵敏度、特异度及准确度等指标，评价模型的诊断性能。P<0.05为差异具有统计学意义。
2　结果
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2.1　PTC患者临床资料
训练集患者平均年龄（39±7）岁，106名女性；内部测试集平均年龄（43±7）岁，43名女性；外部测试集平均年龄（47±6）岁，36名女性。变量在所有队列之间差异不具备统计学意义。在所有队列中，分别有70例（51.1%）、29例（49.2%）、25例（53.2%）发生转移，见表1。
2.2　CT静脉期放射组学特征与临床特征选择
从PTC患者的CT图像中，提取了2 107个影像组学特征。删除重复性较差（ICC<0.75）和稳定性较差的特征后，总共选择了446个放射学特征进行方差分析和Pearson相关分析。最终利用LASSO回归，分离出10个与CLNM相关的显著特征（图1）。通过单因素和多因素Logistic回归分析描述了临床特征和CLNM之间的关系，年龄（OR=0.968，P=0.042）和T4（OR=0.980，P=0.040）被确定为独立预测因素（P<0.05，表2、3）。
2.3　机器学习模型的建立和选择
为了确定PTC患者CLNM的最佳预测模型，选择了6种机器学习分类器（SVM、DT、LR、RF、KNN和XGBoost）。XGBoost模型在外部测试集中表现出最高的区分度（AUC=0.772，95%CI：0.632~0.912），其在训练集（95%CI：0.895~0.976）和内部测试集（95%CI：0.724~0.941）中AUC＞0.800（图2）。外部测试集中XGBoost模型ROC曲线的AUC、精确率、特异性优于其他模型（图3）。
2.4　SHAP模型解释
SHAP图描述了外部测试集中每个特征在预测甲状腺乳头状癌颈部淋巴结转移中的作用。当横坐标为负且绝对值较大时，负预测结果的可能性较大。相比之下，当横坐标为正且绝对值较大时，正预测结果的可能性较大。结果显示，年龄越大，患者发生淋巴结转移的可能性越小，然而lbp.3D.k_glrlm_LongRun LowGrayLevel Emphasis越大，患者发生淋巴结转移的可能性越大（图4）。就特征而言，年龄是最重要的特征变量。
3　讨论
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3.1　PTC患者现状
PTC患者颈部淋巴结转移是影响患者预后的关键，同时也影响着临床分期和手术范围。由于CLN位置靠近喉返神经、甲状旁腺等重要结构，清扫淋巴结时需要更加精细地操作，以避免损伤这些结构。然而，即使在经验丰富的外科医生操作下，仍可能出现术后并发症，如喉返神经损伤导致的声音嘶哑，或甲状旁腺功能减退引起的低钙血症［18］。研究表明，CLNM是PTC术后复发的高危因素之一。复发的患者往往需要接受更复杂的二次手术或其他治疗，治疗难度和风险显著增加。此外，CLNM还可能影响患者的长期生存率。虽然PTC总体预后较好，但CLNM患者的10年生存率显著低于无转移患者［19］。因此，开发一种术前非侵入性淋巴结转移评估工具具有重要的临床意义。
3.2　PTC患者CLNM预测模型的构建与验证
本研究融合静脉期CT放射组学特征与临床特征，通过多中心数据集构建了PTC患者CLNM预测模型。基于LASSO回归筛选的10个关键放射组学特征与年龄、T4，2个临床特征建立的XGBoost模型，在内部测试集（AUC=0.832）和外部测试集（AUC=0.772）中均表现出最优诊断性能。与既往研究结果相同，在本研究中，年龄是CLNM的一个风险因素［20-21］，年龄较小是CLNM的独立危险因素，提示临床应加强对年轻PTC患者的淋巴结评估。本模型在保持较高特异性（外部测试集0.78）的同时实现了敏感性的突破（外部测试集0.67），较传统增强CT诊断的敏感性（50%）［22］和超声诊断的敏感性（20%~31%）［23］有显著提升。本研究有效克服了单中心研究的偏倚，较CHANG等［24］基于血清标志物的列线图（AUC=0.756）更具临床普适性。本模型通过整合静脉期CT的肿瘤异质性信息，较SONG等［25］单纯依赖临床特征（肿瘤大小、包膜侵犯等）的模型（AUC=0.729）提升了预测精度（ΔAUC=0.043）。较LI等［26］基于机器学习的影像组学模型预测CLNM外部测试集（AUC=0.764）提升了预测性能，证实了放射组学在捕捉微观肿瘤特征中的独特价值。本研究进一步剖析了模型中各个特征对预测结果的贡献。SHAP值图和特征重要性图揭示了关键预测因素对模型的影响［27］。这种解释性分析不仅增强了模型的透明度，还揭示了肿瘤异质性与淋巴结转移状态之间的强关联。近年来，多模态影像组学在肿瘤转移预测中的价值逐渐受到重视。HU等［28］构建的列线图显示出良好的临床适用性，本研究结果与其相似，进一步证实了多模态影像特征融合能够有效突破单一影像模态的检测局限，为PTC患者颈部CLNM的术前无创评估提供了新的参考。
本研究的创新之处在于通过多中心设计构建并外部测试模型，克服了既往单中心研究的局限性，在独立测试集中取得AUC=0.772，显著提升了模型的泛化能力与临床普适性。其次，创新性地融合放射组学特征（经LASSO筛选的10个关键纹理与形态特征）与临床独立危险因素，利用XGBoost算法捕捉复杂非线性关系，较传统逻辑回归模型（如SONG等［25］研究中的AUC=0.729）及同类影像组学研究（如LI等［26］研究中的AUC=0.764）展现出更良好的预测效能。此外，通过SHAP可解释性分析，明确了年龄较小和特定高阶纹理特征为关键预测因子，揭示了肿瘤异质性与转移风险的潜在关联，弥补了传统“黑箱”模型的不足，为临床个体化决策提供了透明化依据。然而，尽管本研究取得了积极的结果，但仍存在一些局限性：①样本量有限，可能影响模型在更广泛人群中的适用性；②模型的性能可能受到图像质量和对比剂使用等因素的影响；③放射组学特征的生物学解释性仍有待进一步提高，未来的研究需要扩大样本量，探索模型在不同人群中的应用效果，并结合其他临床信息和生物标志物，以进一步提高模型的预测准确性。
本研究开发的基于CT放射组学的机器学习模型为PTC患者CLNM的术前预测提供了一种新的方法。该模型在预测准确性、可解释性和临床应用价值方面均显示出显著优势，有望为PTC患者的个体化治疗提供有力支持。

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2025年第47卷第12期
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doi: 10.16016/j.2097-0927.202503007
  • 接收时间：2025-03-03
  • 首发时间：2026-10-28
  • 出版时间：2025-06-30
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  • 收稿日期：2025-03-03
  • 修回日期：2025-04-25
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    1陆军军医大学（第三军医大学）第一附属医院：放射科，重庆
    2陆军军医大学（第三军医大学）第一附属医院：乳腺甲状腺外科，重庆
    3中国人民解放军陆军第九五八医院放射科，重庆

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王健，E-mail：
WANG Jian，E-mail：
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2种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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