Article(id=1322191027469931218, tenantId=1146029695717560320, journalId=1319373162502258839, issueId=1322191025829953744, articleNumber=null, orderNo=null, doi=10.16016/j.2097-0927.202501030, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1736524800000, receivedDateStr=2025-01-11, revisedDate=1741622400000, revisedDateStr=2025-03-11, acceptedDate=null, acceptedDateStr=null, onlineDate=1793164506069, onlineDateStr=2026-10-28, pubDate=1753804800000, pubDateStr=2025-07-30, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793164506069, onlineIssueDateStr=2026-10-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793164506069, creator=13701087609, updateTime=1793164506069, updator=13701087609, issue=Issue{id=1322191025829953744, tenantId=1146029695717560320, journalId=1319373162502258839, year='2025', volume='47', issue='14', pageStart='1557', pageEnd='1684', issueExtLink='null', onlineDate='null', pubDate='1753804800000', pubDateStr='2025-07-30', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1793164505679, creator='13701087609', updateTime=1793164547848, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1322191202804420934, tenantId=1146029695717560320, journalId=1319373162502258839, issueId=1322191025829953744, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1322191202804420935, tenantId=1146029695717560320, journalId=1319373162502258839, issueId=1322191025829953744, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=1577, endPage=1586, ext={EN=ArticleExt(id=1322191028384289491, articleId=1322191027469931218, tenantId=1146029695717560320, journalId=1319373162502258839, language=EN, title=Value of MRI radiomics based on intratumoral and peritumoral heterogeneity in predicting spatial patterns of locally recurrent high-grade gliomas, columnId=1320442291748192970, journalTitle=Journal of Army Medical University, columnName=Monographic Reports, runingTitle=null, highlight=null, articleAbstract=
Objective‍

To establish and validate a multimodal MRI radiomics model based on intratumoral and peritumoral heterogeneity for prediction of spatial pattern of locally recurrent high-grade gliomas (HGGs).

Methods‍

A retrospective analysis was conducted on the clinical and imaging data of all HGGs patients who underwent maximum safe resection followed by postoperative radiotherapy combined with temozolomide treatment and experienced in local recurrence in Army Medical Center of PLA from 2012 to 2021. Two radiologists independently assessed the spatial patterns of locally recurrence HGGs through continuous follow-up MRI data, and primarily categorized the pattern into intra-resection cavity recurrence and extra-resection cavity recurrence. The subjected patients were randomly divided into a training set and a validation set in a 7∶3 ratio. In the training set, Pearson or Spearman correlation analysis and least absolute shrinkage and selection operator (LASSO) analysis were employed to screen radiomic features within the intratumoral and peritumoral regions, as well as to calculate radiomic scores. A radiomics model was established using logistic regression analysis. The performance of the model was assessed using calibration curves, Hosmer-Lemeshow goodness-of-fit test, and the area under the receiver operating characteristic curve (AUC). Validation of the model was performed in the validation set.

Results‍

A total of 121 patients with locally recurrent HGGs were enrolled in this study, including 54 in intra-resection cavity recurrence group and 67 in extra-resection cavity recurrence group. Among them, 84 were assigned into the training set and 37 into the validation set. In the training set, the radiomics score for the extra-resection cavity recurrence group was 0.424 (0.278, 0.573), which was higher than that for the intra-resection cavity recurrence group [-0.030 (-0.226,0.248), P<0.001]. In the validation set, the radiomics score for the extra-resection cavity recurrence group was 0.369 (0.258, 0.487), which was higher than that for the intra-resection cavity recurrence group [0.277 (0.103, 0.322), P=0.033]. The established radiomics model exhibited good calibration and performed well in predicting spatial recurrence patterns, with an AUC value of 0.844 (95%CI: 0.749~0.914) in the training set and 0.706 (95%CI:0.534~0.844) in the validation set.

Conclusion

Our multimodal radiomics model combined with intratumoral and peritumoral heterogeneity can predict the spatial pattern of locally recurrent HGGs, providing a basis for individualized treatment of HGGs.

, authors=Hanwei WANG1, Linlan ZENG1, Mimi ZHAO1, Xuan LI1, Huan XIE1, Xiaoguang LI1, Liang YI2, Yi WU3, Shunan WANG1, authorsList=Hanwei WANG, Linlan ZENG, Mimi ZHAO, Xuan LI, Huan XIE, Xiaoguang LI, Liang YI, Yi WU, Shunan WANG, authorCompany=null, correspAuthors=Yi WU, Shunan 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=1322191033320985312, articleId=1322191027469931218, tenantId=1146029695717560320, journalId=1319373162502258839, language=CN, title=联合瘤内及瘤周异质性的MRI影像组学预测高级别胶质瘤原位复发空间模式的价值, columnId=1322190423666315871, journalTitle=陆军军医大学学报, columnName=专题报道, runingTitle=null, highlight=null, articleAbstract=
目的

建立并验证基于瘤内及瘤周异质性的多模态MRI影像组学模型,预测高级别胶质瘤(high-grade gliomas,HGGs)原位复发空间模式。

方法

回顾性分析陆军特色医学中心2012-2021年采用最大范围安全切除并术后放疗联合替莫唑胺治疗后原位复发的HGGs患者临床及影像资料。2名放射科医师通过连续性随访MRI资料独立评估HGGs原位复发空间模式,分为切除腔内复发和切除腔外复发。采用完全随机法将患者按7∶3的比例分为训练集与验证集。在训练集中采用Pearson或Spearman相关性分析以及最小绝对收缩与选择算子分析,对瘤内及瘤周影像组学特征进行筛选及影像组学评分计算。采用Logistic回归分析建立影像组学模型,利用校准图、Hosmer-Lemeshow检验和受试者操作特征曲线下面积(AUC)评估模型效能,并在验证集中进行验证。

结果

本研究共纳入121例原位复发HGGs患者,其中切除腔内复发组54例,切除腔外复发组67例,按7∶3的比例分为训练集(n=84)和验证集(n=37)。在训练集中,切除腔外复发组的影像组学评分为0.424(0.278,0.573),高于切除腔内复发组[-0.030(-0.226,0.248),P<0.001]。同时,验证集中切除腔外复发组的影像组学评分[0.369(0.258,0.487)]高于切除腔内复发组[0.277(0.103,0.322),P=0.033]。通过Logistic回归分析建立的影像组学模型具有良好的校准效果,在预测原位复发空间模式方面表现良好,训练集AUC值为0.844(95%CI:0.749~0.914),验证集AUC值为0.706(95%CI:0.534~0.844)。

结论

联合瘤内及瘤周异质性的多模态影像组学模型可预测HGGs原位复发的空间模式,为HGGs的个体化治疗提供依据。

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吴 毅,E-mail:
王舒楠,E-mail:
WU Yi,E-mail:
WANG Shunan,E-mail:
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J Neurooncol,2013,111(1):19-23., articleTitle=Failure pattern following complete resection plus radiotherapy and temozolomide is at the resection margin in patients with glioblastoma, refAbstract=null), Reference(id=1322191071505929040, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322191027469931218, doi=10.1007/s11060-017-2559-9, pmid=null, pmcid=null, year=2017, volume=135, issue=1, pageStart=129, pageEnd=139, url=null, language=null, rfNumber=19, rfOrder=18, authorNames=PESSINA F, NAVARRIA P, COZZI L, journalName=J Neurooncol, refType=null, unstructuredReference=PESSINA F,NAVARRIA P,COZZI L,et al. Maximize surgical resection beyond contrast-enhancing boundaries in newly diagnosed glioblastoma multiforme:is it useful and safe?A single institution retrospective experience[J]. J Neurooncol,2017,135(1):129-139., articleTitle=Maximize surgical resection beyond contrast-enhancing boundaries in newly diagnosed glioblastoma multiforme:is it useful and safe?A single institution retrospective experience, refAbstract=null), Reference(id=1322191071858250577, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322191027469931218, doi=10.1016/j.wneu.2019.12.130, pmid=null, pmcid=null, year=2020, volume=135, issue=null, pageStart=e738, pageEnd=e747, url=null, language=null, rfNumber=20, rfOrder=19, authorNames=LU M, FU Z H, HE X J, journalName=World Neurosurg, refType=null, unstructuredReference=LU M,FU Z H,HE X J,et al. T2 fluid-attenuated inversion recovery resection for glioblastoma involving eloquent brain areas facilitated through awake craniotomy and clinical outcome[J]. World Neurosurg,2020,135:e738-e747., articleTitle=T2 fluid-attenuated inversion recovery resection for glioblastoma involving eloquent brain areas facilitated through awake craniotomy and clinical outcome, refAbstract=null), Reference(id=1322191071975691090, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322191027469931218, doi=10.1016/j.canlet.2020.02.025, pmid=null, pmcid=null, year=2020, volume=477, issue=null, pageStart=97, pageEnd=106, url=null, language=null, rfNumber=21, rfOrder=20, authorNames=HU L S, HAWKINS-DAARUD A, WANG L J, journalName=Cancer Lett, refType=null, unstructuredReference=HU L S,HAWKINS-DAARUD A,WANG L J,et al. Imaging of intratumoral heterogeneity in high-grade glioma[J]. 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Eur J Cancer,2012,48(4):441-446., articleTitle=Radiomics:extracting more information from medical images using advanced feature analysis, refAbstract=null)], funds=null, companyList=[AuthorCompany(id=1322191035866927842, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322191027469931218, xref=1, ext=[AuthorCompanyExt(id=1322191035875316452, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322191027469931218, companyId=1322191035866927842, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1Department of Radiology,Chongqing Clinical Research Centre of Imaging and Nuclear Medicine, Faculty of Biomedical Engineering and Imaging Medicine,Army Medical University (Third Military Medical University),Chongqing,China), AuthorCompanyExt(id=1322191035883705060, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322191027469931218, companyId=1322191035866927842, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1陆军特色医学中心(第三军医大学大坪医院):放射科,重庆市影像医学与核医学临床研究中心,重庆)]), AuthorCompany(id=1322191036261192421, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322191027469931218, xref=2, ext=[AuthorCompanyExt(id=1322191036294746854, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322191027469931218, companyId=1322191036261192421, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2Department of Neurosurgery,Army Medical Center of PLA/Daping Hospital of Third Military Medical University,Chongqing, Faculty of Biomedical Engineering and Imaging Medicine,Army Medical University (Third Military Medical University),Chongqing,China), AuthorCompanyExt(id=1322191036307329767, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322191027469931218, companyId=1322191036261192421, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2陆军特色医学中心(第三军医大学大坪医院):神经外科,重庆)]), AuthorCompany(id=1322191036726760168, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322191027469931218, xref=3, ext=[AuthorCompanyExt(id=1322191036739343081, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322191027469931218, companyId=1322191036726760168, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=3Department of Digital Medicine, Faculty of Biomedical Engineering and Imaging Medicine,Army Medical University (Third Military Medical University),Chongqing,China), AuthorCompanyExt(id=1322191036747731690, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322191027469931218, companyId=1322191036726760168, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=3陆军军医大学(第三军医大学)生物医学工程与影像医学系数字医学教研室,重庆)])], figs=[ArticleFig(id=1322191050924479273, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322191027469931218, language=EN, label=null, caption=null, figureFileSmall=g5Bc4qvuMOFAnRdFB0qLvg==, figureFileBig=416xoe1oH9yMB4iznMuSgQ==, tableContent=null), ArticleFig(id=1322191052572840746, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322191027469931218, language=CN, label=图1, caption=影像组学分析流程图, figureFileSmall=g5Bc4qvuMOFAnRdFB0qLvg==, figureFileBig=416xoe1oH9yMB4iznMuSgQ==, tableContent=null), ArticleFig(id=1322191053839520555, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322191027469931218, language=EN, label=null, caption=null, figureFileSmall=ZHaHzEfNkWFDyXEhtOuhzg==, figureFileBig=BMNF4sg//aIDPQTsz3hnBQ==, tableContent=null), ArticleFig(id=1322191054267339564, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322191027469931218, language=CN, label=图2, caption=切除腔内复发与切除腔外复发患者MRI表现

A~C:切除腔内复发患者(女性,29岁,胶质母细胞瘤,WHO 4级)MRI表现;A:术前轴位T1WI增强显示病变位于右侧额顶叶(红色虚线);B:术后轴位T1WI增强显示肿瘤全切,残留手术腔形成;C:术后7个月随访轴位T1WI增强显示切除腔消失,新增团片状强化影;D~F:切除腔外复发患者(女性,49岁,胶质母细胞瘤,WHO 4级)MRI表现;D:术前轴位T1WI增强显示病变位于双侧额叶(红色虚线);E:术后轴位T1WI增强显示肿瘤全切,残留手术腔形成;F:术后4个月随访轴位T1WI增强显示切除腔外新增强化结节影(白色箭头)

, figureFileSmall=ZHaHzEfNkWFDyXEhtOuhzg==, figureFileBig=BMNF4sg//aIDPQTsz3hnBQ==, tableContent=null), ArticleFig(id=1322191054682575661, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322191027469931218, language=EN, label=null, caption=null, figureFileSmall=zmhwmRvxavOUKmY/F9abbg==, figureFileBig=oD6Naivu/6P27x+tEEAyvA==, tableContent=null), ArticleFig(id=1322191055131366191, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322191027469931218, language=CN, label=图3, caption=影像组学特征及相应系数的选择

A:惩罚参数λ选择;B:系数分布

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A:训练集中2组影像组学评分;B:验证集中2组影像组学评分;C:训练集中不同空间复发模式之间影像组学评分比较;D:验证集中不同空间复发模式之间影像组学评分比较

, figureFileSmall=PLws0wxgN+W59BLUCtrpEA==, figureFileBig=S/2gvf2ekgWmJqa/dszzAA==, tableContent=null), ArticleFig(id=1322191057589228338, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322191027469931218, language=EN, label=null, caption=null, figureFileSmall=lpyZ6qgrAYLkO6SAYH6gtQ==, figureFileBig=tlix40tVXcUfC4s5eX8C+Q==, tableContent=null), ArticleFig(id=1322191057677308723, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322191027469931218, language=CN, label=图5, caption=影像组学模型及其性能评估

A:影像组学模型预测HGGs空间复发模式的列线图;B:训练集中模型的校正曲线;C:验证集中模型的校正曲线;D:训练集中模型的ROC曲线;E:验证集中模型的ROC曲线

, figureFileSmall=lpyZ6qgrAYLkO6SAYH6gtQ==, figureFileBig=tlix40tVXcUfC4s5eX8C+Q==, tableContent=null), ArticleFig(id=1322191058079961908, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322191027469931218, language=EN, label=null, caption=null, figureFileSmall=null, figureFileBig=null, tableContent=
扫描序列重复时间/ms回波时间/ms反转时间/ms层厚/mm层间距/mm矩阵视野/mm翻转角
T1WI2502.67-51320×256230×23090°
T2WI4 900100-51320×320230×230150°
T2WI/FLAIR8 000942 37051256×256230×230150°
CE-T1WI2502.67-51320×256230×23090°
), ArticleFig(id=1322191058440672053, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322191027469931218, language=CN, label=表1, caption=

患者颅脑MRI扫描参数

, figureFileSmall=null, figureFileBig=null, tableContent=
扫描序列重复时间/ms回波时间/ms反转时间/ms层厚/mm层间距/mm矩阵视野/mm翻转角
T1WI2502.67-51320×256230×23090°
T2WI4 900100-51320×320230×230150°
T2WI/FLAIR8 000942 37051256×256230×230150°
CE-T1WI2502.67-51320×256230×23090°
), ArticleFig(id=1322191058910434102, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322191027469931218, language=EN, label=null, caption=null, figureFileSmall=null, figureFileBig=null, tableContent=
临床资料训练集(n=84)验证集(n=37)t/χ2P
年龄50.31±14.5652.05±13.95-0.6150.540
性别0.1560.693
男性49(58.3)23(62.2)
女性35(41.7)14(37.8)
术前KPS评分0.0010.975
≥7057(67.9)25(67.6)
<7027(32.1)12(32.4)
WHO分级1.3060.253
3级21(25.0)13(35.1)
4级63(75.0)24(64.9)
IDH状态4.8340.089
突变型5(6.0)7(18.9)
野生型58(69.0)22(59.5)
数据缺失21(25.0)8(21.6)
Ki-67指数0.7150.398
>10%63(75.0)25(67.6)
≤10%21(25.0)12(32.4)
肿瘤位置0.7810.677
左侧36(42.9)13(35.1)
右侧34(40.5)18(48.6)
双侧14(16.7)6(16.2)
), ArticleFig(id=1322191059287921463, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322191027469931218, language=CN, label=表2, caption=

训练集与验证集中原位复发HGGs患者临床资料

, figureFileSmall=null, figureFileBig=null, tableContent=
临床资料训练集(n=84)验证集(n=37)t/χ2P
年龄50.31±14.5652.05±13.95-0.6150.540
性别0.1560.693
男性49(58.3)23(62.2)
女性35(41.7)14(37.8)
术前KPS评分0.0010.975
≥7057(67.9)25(67.6)
<7027(32.1)12(32.4)
WHO分级1.3060.253
3级21(25.0)13(35.1)
4级63(75.0)24(64.9)
IDH状态4.8340.089
突变型5(6.0)7(18.9)
野生型58(69.0)22(59.5)
数据缺失21(25.0)8(21.6)
Ki-67指数0.7150.398
>10%63(75.0)25(67.6)
≤10%21(25.0)12(32.4)
肿瘤位置0.7810.677
左侧36(42.9)13(35.1)
右侧34(40.5)18(48.6)
双侧14(16.7)6(16.2)
), ArticleFig(id=1322191059698963256, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322191027469931218, language=EN, label=null, caption=null, figureFileSmall=null, figureFileBig=null, tableContent=
临床特征训练集验证集
切除腔内复发组
(n = 37)
切除腔外复发组
(n = 47)
t/χ2P切除腔内复发组
(n = 17)
切除腔外复发组
(n = 20)
t/χ2P
年龄50.46±15.2350.19±14.180.0830.93453.00±13.4651.25±14.660.3760.709
性别0.0350.853-0.745
男性22(59.5)27(57.4)10(58.8)13(65.0)
女性15(40.5)20(42.6)7(41.2)7(35.0)
术前KPS评分0.0030.960-0.157
≥7025(67.6)32(68.1)9(52.9)16(80.0)
<7012(32.4)15(31.9)8(47.1)4(20.0)
WHO分级1.9480.163-1.000
3级12(32.4)9(19.1)6(35.3)7(35.0)
4级25(67.6)38(80.9)11(64.7)13(65.0)
IDH状态0.1630.922-0.531
突变型2(5.4)3(6.4)2(11.8)5(25.0)
野生型25(67.6)33(70.2)10(58.8)12(60.0)
数据缺失10(27.0)11(23.4)5(29.4)3(15.0)
Ki-671.3040.253-1.000
>10%30(81.1)33(70.2)11(64.7)14(70.0)
≤10%7(18.9)14(29.8)6(35.3)6(30.0)
肿瘤位置1.8780.391-0.609
左侧18(48.6)18(38.3)5(29.4)8(40.0)
右侧15(40.5)19(40.4)10(58.8)8(40.0)
双侧4(10.8)10(21.3)2(11.8)4(20.0)
), ArticleFig(id=1322191061812892474, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322191027469931218, language=CN, label=表3, caption=

切除腔内复发组和切除腔外复发组患者之间临床、病理及影像特征比较 [x¯±s, n(%)]

, figureFileSmall=null, figureFileBig=null, tableContent=
临床特征训练集验证集
切除腔内复发组
(n = 37)
切除腔外复发组
(n = 47)
t/χ2P切除腔内复发组
(n = 17)
切除腔外复发组
(n = 20)
t/χ2P
年龄50.46±15.2350.19±14.180.0830.93453.00±13.4651.25±14.660.3760.709
性别0.0350.853-0.745
男性22(59.5)27(57.4)10(58.8)13(65.0)
女性15(40.5)20(42.6)7(41.2)7(35.0)
术前KPS评分0.0030.960-0.157
≥7025(67.6)32(68.1)9(52.9)16(80.0)
<7012(32.4)15(31.9)8(47.1)4(20.0)
WHO分级1.9480.163-1.000
3级12(32.4)9(19.1)6(35.3)7(35.0)
4级25(67.6)38(80.9)11(64.7)13(65.0)
IDH状态0.1630.922-0.531
突变型2(5.4)3(6.4)2(11.8)5(25.0)
野生型25(67.6)33(70.2)10(58.8)12(60.0)
数据缺失10(27.0)11(23.4)5(29.4)3(15.0)
Ki-671.3040.253-1.000
>10%30(81.1)33(70.2)11(64.7)14(70.0)
≤10%7(18.9)14(29.8)6(35.3)6(30.0)
肿瘤位置1.8780.391-0.609
左侧18(48.6)18(38.3)5(29.4)8(40.0)
右侧15(40.5)19(40.4)10(58.8)8(40.0)
双侧4(10.8)10(21.3)2(11.8)4(20.0)
), ArticleFig(id=1322191062634976059, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322191027469931218, language=EN, label=null, caption=null, figureFileSmall=null, figureFileBig=null, tableContent=
序列影像组学提取的感兴趣容积图像处理类型特征类别影像组学特征权重系数
CE-T1WI瘤内实质区域LoG-sigma-1.0-mm-3DGLCM集群阴影0.257 657 01
CE-T1WI瘤内实质区域小波-LHHGLCM最大概率-21.078 233 16
CE-T1WI瘤内实质区域小波-HHL一阶统计量特征偏度-0.078 974 34
CE-T1WI瘤内实质区域小波-HHH一阶统计量特征偏度0.027 782 69
T2WI/FLAIR高信号瘤周区域Log-sigma-2.0-mm-3DGLSZM小区域低灰度强调4.944 656 63
T2WI/FLAIR高信号瘤周区域Log-sigma-4.0-mm-3DGLCM最大概率1.054 961 69
T2WI/FLAIR高信号瘤周区域小波-HHLGLRLM短行程低灰度强调1.532 398 87
), ArticleFig(id=1322191063041823548, tenantId=1146029695717560320, journalId=1319373162502258839, articleId=1322191027469931218, language=CN, label=表4, caption=

筛选的影像组学特征与权重系数

, figureFileSmall=null, figureFileBig=null, tableContent=
序列影像组学提取的感兴趣容积图像处理类型特征类别影像组学特征权重系数
CE-T1WI瘤内实质区域LoG-sigma-1.0-mm-3DGLCM集群阴影0.257 657 01
CE-T1WI瘤内实质区域小波-LHHGLCM最大概率-21.078 233 16
CE-T1WI瘤内实质区域小波-HHL一阶统计量特征偏度-0.078 974 34
CE-T1WI瘤内实质区域小波-HHH一阶统计量特征偏度0.027 782 69
T2WI/FLAIR高信号瘤周区域Log-sigma-2.0-mm-3DGLSZM小区域低灰度强调4.944 656 63
T2WI/FLAIR高信号瘤周区域Log-sigma-4.0-mm-3DGLCM最大概率1.054 961 69
T2WI/FLAIR高信号瘤周区域小波-HHLGLRLM短行程低灰度强调1.532 398 87
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联合瘤内及瘤周异质性的MRI影像组学预测高级别胶质瘤原位复发空间模式的价值
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王瀚苇 1 , 曾琳岚 1 , 赵咪咪 1 , 李轩 1 , 谢欢 1 , 李晓光 1 , 易良 2 , 吴毅 3 , 王舒楠 1
陆军军医大学学报 | 专题报道 2025,47(14): 1577-1586
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陆军军医大学学报 |专题报道 2025 , 47 (14) : 1577 -1586
联合瘤内及瘤周异质性的MRI影像组学预测高级别胶质瘤原位复发空间模式的价值
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王瀚苇1, 曾琳岚1, 赵咪咪1, 李轩1, 谢欢1, 李晓光1, 易良2, 吴毅3 , 王舒楠1
作者信息
  • 1陆军特色医学中心(第三军医大学大坪医院):放射科,重庆市影像医学与核医学临床研究中心,重庆
  • 2陆军特色医学中心(第三军医大学大坪医院):神经外科,重庆
  • 3陆军军医大学(第三军医大学)生物医学工程与影像医学系数字医学教研室,重庆
通讯作者:
吴 毅,E-mail:
王舒楠,E-mail:
WU Yi,E-mail:
WANG Shunan,E-mail:
Value of MRI radiomics based on intratumoral and peritumoral heterogeneity in predicting spatial patterns of locally recurrent high-grade gliomas
Hanwei WANG1, Linlan ZENG1, Mimi ZHAO1, Xuan LI1, Huan XIE1, Xiaoguang LI1, Liang YI2, Yi WU3 , Shunan WANG1
Affiliations
  • 1Department of Radiology,Chongqing Clinical Research Centre of Imaging and Nuclear Medicine, Faculty of Biomedical Engineering and Imaging Medicine,Army Medical University (Third Military Medical University),Chongqing,China
  • 2Department of Neurosurgery,Army Medical Center of PLA/Daping Hospital of Third Military Medical University,Chongqing, Faculty of Biomedical Engineering and Imaging Medicine,Army Medical University (Third Military Medical University),Chongqing,China
  • 3Department of Digital Medicine, Faculty of Biomedical Engineering and Imaging Medicine,Army Medical University (Third Military Medical University),Chongqing,China
出版时间: 2025-07-30 doi: 10.16016/j.2097-0927.202501030
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目的

建立并验证基于瘤内及瘤周异质性的多模态MRI影像组学模型,预测高级别胶质瘤(high-grade gliomas,HGGs)原位复发空间模式。

方法

回顾性分析陆军特色医学中心2012-2021年采用最大范围安全切除并术后放疗联合替莫唑胺治疗后原位复发的HGGs患者临床及影像资料。2名放射科医师通过连续性随访MRI资料独立评估HGGs原位复发空间模式,分为切除腔内复发和切除腔外复发。采用完全随机法将患者按7∶3的比例分为训练集与验证集。在训练集中采用Pearson或Spearman相关性分析以及最小绝对收缩与选择算子分析,对瘤内及瘤周影像组学特征进行筛选及影像组学评分计算。采用Logistic回归分析建立影像组学模型,利用校准图、Hosmer-Lemeshow检验和受试者操作特征曲线下面积(AUC)评估模型效能,并在验证集中进行验证。

结果

本研究共纳入121例原位复发HGGs患者,其中切除腔内复发组54例,切除腔外复发组67例,按7∶3的比例分为训练集(n=84)和验证集(n=37)。在训练集中,切除腔外复发组的影像组学评分为0.424(0.278,0.573),高于切除腔内复发组[-0.030(-0.226,0.248),P<0.001]。同时,验证集中切除腔外复发组的影像组学评分[0.369(0.258,0.487)]高于切除腔内复发组[0.277(0.103,0.322),P=0.033]。通过Logistic回归分析建立的影像组学模型具有良好的校准效果,在预测原位复发空间模式方面表现良好,训练集AUC值为0.844(95%CI:0.749~0.914),验证集AUC值为0.706(95%CI:0.534~0.844)。

结论

联合瘤内及瘤周异质性的多模态影像组学模型可预测HGGs原位复发的空间模式,为HGGs的个体化治疗提供依据。

胶质瘤  /  影像组学  /  瘤内  /  瘤周  /  复发
Objective‍

To establish and validate a multimodal MRI radiomics model based on intratumoral and peritumoral heterogeneity for prediction of spatial pattern of locally recurrent high-grade gliomas (HGGs).

Methods‍

A retrospective analysis was conducted on the clinical and imaging data of all HGGs patients who underwent maximum safe resection followed by postoperative radiotherapy combined with temozolomide treatment and experienced in local recurrence in Army Medical Center of PLA from 2012 to 2021. Two radiologists independently assessed the spatial patterns of locally recurrence HGGs through continuous follow-up MRI data, and primarily categorized the pattern into intra-resection cavity recurrence and extra-resection cavity recurrence. The subjected patients were randomly divided into a training set and a validation set in a 7∶3 ratio. In the training set, Pearson or Spearman correlation analysis and least absolute shrinkage and selection operator (LASSO) analysis were employed to screen radiomic features within the intratumoral and peritumoral regions, as well as to calculate radiomic scores. A radiomics model was established using logistic regression analysis. The performance of the model was assessed using calibration curves, Hosmer-Lemeshow goodness-of-fit test, and the area under the receiver operating characteristic curve (AUC). Validation of the model was performed in the validation set.

Results‍

A total of 121 patients with locally recurrent HGGs were enrolled in this study, including 54 in intra-resection cavity recurrence group and 67 in extra-resection cavity recurrence group. Among them, 84 were assigned into the training set and 37 into the validation set. In the training set, the radiomics score for the extra-resection cavity recurrence group was 0.424 (0.278, 0.573), which was higher than that for the intra-resection cavity recurrence group [-0.030 (-0.226,0.248), P<0.001]. In the validation set, the radiomics score for the extra-resection cavity recurrence group was 0.369 (0.258, 0.487), which was higher than that for the intra-resection cavity recurrence group [0.277 (0.103, 0.322), P=0.033]. The established radiomics model exhibited good calibration and performed well in predicting spatial recurrence patterns, with an AUC value of 0.844 (95%CI: 0.749~0.914) in the training set and 0.706 (95%CI:0.534~0.844) in the validation set.

Conclusion

Our multimodal radiomics model combined with intratumoral and peritumoral heterogeneity can predict the spatial pattern of locally recurrent HGGs, providing a basis for individualized treatment of HGGs.

glioma  /  radiomics  /  intratumoral  /  peritumoral  /  recurrence
王瀚苇, 曾琳岚, 赵咪咪, 李轩, 谢欢, 李晓光, 易良, 吴毅, 王舒楠. 联合瘤内及瘤周异质性的MRI影像组学预测高级别胶质瘤原位复发空间模式的价值. 陆军军医大学学报, 2025 , 47 (14) : 1577 -1586 . DOI: 10.16016/j.2097-0927.202501030
Hanwei WANG, Linlan ZENG, Mimi ZHAO, Xuan LI, Huan XIE, Xiaoguang LI, Liang YI, Yi WU, Shunan WANG. Value of MRI radiomics based on intratumoral and peritumoral heterogeneity in predicting spatial patterns of locally recurrent high-grade gliomas[J]. Journal of Army Medical University, 2025 , 47 (14) : 1577 -1586 . DOI: 10.16016/j.2097-0927.202501030
高级别胶质瘤(high-grade gliomas,HGGs)是成人中枢神经系统最常见的侵袭性肿瘤。尽管多模式治疗策略在HGGs患者治疗中取得一定进展,但大多数HGGs患者的预后无显著改善,超过一半的患者在术后6~8个月内复发[1-2]。随着对胶质瘤复发研究的深入,越来越多的学者注意到HGGs复发存在不同的复发模式,手术切除腔边缘2~3 cm内的原位复发是HGGs最常见的复发模式[3-6]。同时,根据术后磁共振成像(MRI)图像上复发肿瘤与手术切除腔的空间距离,可将原位复发分为切除腔内复发与切除腔外复发[3,7]。WANG等[7]分析了69例原位复发患者生存时间与空间复发模式的关系,结果发现切除腔外复发患者的预后比切除腔内复发患者差。因此,早期评估HGGs原位复发空间模式有助于为患者制定个性化治疗方案并改善其预后。
胶质瘤异质性是导致肿瘤复发和预后不良的重要因素之一,其存在于瘤内及瘤周[8-9]。肿瘤异质性导致同一肿瘤不同部位的基因型与表型差异[10],这些差异使HGGs复发时存在复杂性和多样性。肿瘤异质性评估通常依赖于组织病理学,但病理取材范围有限,限制了结果的准确性。由此,采用无创性手段全面描述肿瘤异质性对评估HGGs复发具有重要意义。MRI是管理HGGs患者的首要影像学工具,在HGGs诊断、术后监控及随访中起着至关重要的作用。先进的MRI处理技术,如影像组学分析,可以评估HGGs异质性[11-12]。同时,通过肿瘤亚区域分割手段,影像组学可以更准确地反映HGGs瘤内及瘤周的异质性。ISMAIL等[13]分析了T1WI增强肿瘤实质区域和T2WI/FLAIR高信号瘤周水肿区域的三维形态特征,结果表明这些特征可用于鉴别肿瘤进展与假性进展。既往研究也报道了影像组学模型在预测HGGs复发模式中的应用[14-15]。上述研究表明,基于瘤内及瘤周亚区域分割的影像组学模型在术前预测HGGs原位复发空间模式上具有前景。
本研究拟通过回顾性分析本院原位复发HGGs患者的临床及影像数据,旨在建立并验证基于瘤内及瘤周异质性的多模态MRI影像组学模型,用于术前预测HGGs原位复发空间模式。
本研究回顾性分析了我院2012年1月至2021年10月原位复发HGGs患者临床以及影像资料。本研究遵守《赫尔辛基宣言》,经陆军特色医学中心伦理委员会批准,免除受试者书面知情同意书(2022第325号)。
纳入标准:①根据2021年WHO中枢神经系统肿瘤分类标准,术后病理学首次确诊为WHO 3~4级的HGGs患者;②术前及MRI检查前无治疗史;③在术前1周内行颅脑MRI扫描,扫描序列包括T1WI、T2WI、T2WI/液体衰减反转恢复(FLAIR)以及增强T1加权成像(contrast enhanced T1 weighted imaging,CE-T1WI)序列;④经过最大范围安全切除并术后放疗联合替莫唑胺治疗;⑤年龄>18岁。排除标准:①患者失访;②随访期间肿瘤无复发;③非原位复发;④MRI图像质量差。
本研究按7∶3的比例,采用完全随机法将纳入的原位复发HGGs患者分为训练集和验证集,回顾性分析患者的临床资料以及影像资料。
患者术后24~72 h内行增强MRI和CT检查,此后每3~6个月进行1次MRI检查,直至随访患者死亡或研究截止(2023年10月)。确定肿瘤复发的依据:①再次手术后病理学证实复发的患者;②根据神经肿瘤学标准影像学判定肿瘤复发的患者,或利用多模态功能MRI排除治疗后相关反应的患者。
2位放射科医师(分别具有5年和11年的工作经验)独立评估HGGs空间复发模式。如存在争议,则与另一位有15年工作经验的放射科医师讨论后达成共识。空间复发模式评估流程:首先,利用术后24~72 h内CE-MRI和CT评估手术区域情况;其次,通过连续性随访CE-MRI观察手术切除腔的变化以及复发病灶向切除腔内或腔外生长的趋势;最后,根据复发肿瘤与手术切除腔的距离评估原位复发HGGs的空间模式[3]。原位复发空间模式分为切除腔内复发和切除腔外复发[7]。切除腔内复发定义为肿瘤在切除腔区域内复发,切除腔外复发定义为肿瘤在切除腔边缘或2 cm范围内复发。
患者均采用Siemens Verio 3.0T超导型磁共振扫描仪和8通道头颅线圈。MRI扫描序列包括T1WI、T2WI、T2WI/FLAIR以及CE-T1WI序列。CE-T1WI使用对比剂钆喷替酸葡甲胺,经静脉团注,剂量为0.2 mL/kg,流速为3 mL/s。各序列具体扫描参数见表1。
影像组学分析的工作流程包括图像分割、影像组学特征提取、特征选择以及模型构建和评估(图1)。首先,将所有患者MRI图像的DICOM格式使用MRIcroGL(https://www.nitrc.org/projects/mricrogl/)转换为神经影像信息技术倡议(neuroimaging informatics technology initiative,NIFTI)格式。初始分割使用ONCOhabitats(https://www.oncohabitats.upv.es/)提供的无监督自动分割方法进行[16]。肿瘤自动分割过程如下:①预处理:对图像进行偏磁场不均匀性、噪声或尖峰伪影的校正,通过颅骨剥离、自动配准、脑提取和强度归一化生成一致的多参数高质量脑图像。②分割:采用基于U-Net结构的三维卷积神经网络分类器分割T1WI增强瘤内实质区域和T2WI/FLAIR高信号瘤周区域。然后,1位放射科医师(有5年工作经验)使用ITK-SNAP3.8软件(www.itksnap.org)评估图像分割的准确性,并生成最终的感兴趣容积(volume of interest,VOI)。
在特征提取之前,将所有图像重采样为1.0 mm×1.0 mm×1.0 mm的体素大小,灰度直方图bin宽度固定为25进行图像离散化,以避免不同MR重建参数的影响。使用Python3.7.0(https://www.python.org/)中的pyradiomics工具包(https://pyradiomics.readthedocs.io/)进行特征提取[17]。进行影像组学特征提取的图像类型包括原始图像,以及通过拉普拉斯高斯(laplacian of Gaussian,LoG)滤波和小波滤波变换生成的派生图像。LoG-sigma-1.0~LoG-sigma-4.0表示不同程度的LoG滤波,低sigma强调精细纹理,高sigma则强调粗糙的纹理。小波滤波在3个维度中分别使用高通及低通滤波器对图像产生8个分解,包括低通-低通-高通滤波器(low-pass,low-pass,and high-pass filters,LLH)、LHL、LHH、HLL、HLH、HHL、HHH、LLL。提取的影像组学特征包括以下7类:一阶统计量特征、三维和二维形态学特征、灰度共生矩阵(gray-level co-occurrence matrix,GLCM)、灰度游程长度矩阵(gray level run-length matrix,GLRLM)、灰度大小区域矩阵(gray-level size zone matrix,GLSZM)、相邻灰度差矩阵(neighbourhood gray-tone difference matrix,NGTDM)和灰度依赖矩阵(gray level dependence matrix,GLDM)。从T1WI增强瘤内实质区域和T2WI/FLAIR高信号瘤周区域共提取2 446个影像组学特征。这些特征符合成像生物标志物标准化倡议(image biomarker standardization initiative,IBSI)指南。
提取的影像组学特征均采用Z-score标准化,并使用Pearson或Spearman相关分析和最小绝对收缩选择算子(least absolute shrinkage and selection operator,LASSO)从训练集中选择与空间复发模式相关的非零系数特征。然后利用这些非零系数特征的LASSO权重系数进行线性组合,计算患者影像组学评分。
采用SPSS 26.0、R语言4.2.1以及MedCalc 20.116对数据进行统计分析。首先对连续变量进行正态检验和方差齐性检验,如果两者均满足,则以x¯±s表示并采用独立样本t检验;如果不满足,则以M(P25,P75)表示,并使用Mann-Whitney U检验。分类变量以例数(%)表示,采用卡方检验或Fisher精确检验。LASSO分析中的惩罚参数调整采用最小准则和10倍交叉验证进行选择。以切除腔外复发为结局变量,在训练集中采用Logistic回归分析构建预测原位复发空间模式的影像组学模型,并绘制列线图展示模型,利用验证集对模型进行验证。模型效能采用校准曲线来评估模型的校准情况,Hosmer-Lemeshow检验评估模型的拟合优度,受试者操作特征(ROC)曲线下面积(AUC)来评估模型的预测效能。所有假设检验均为双侧检验,P<0.05表示差异具有统计学意义。
本研究共纳入121例原位复发HGGs患者,其中男性72例,女性49例,年龄(50.84±14.35)岁。基于术后随访的CE-MRI图像评估结果,将患者分为切除腔内复发组(n=54,图2A~C)和切除腔外复发组(n=67,图2D~F)。121例患者采用完全随机法按7∶3的比例分为训练集(n=84)和验证集(n=37)。患者年龄、性别、术前卡式评分(karnofsky performance status,KPS)、WHO分级、异柠檬酸脱氢酶(isocitrate dehydrogenase,IDH)状态、Ki-67指数及肿瘤位置,在训练集和验证集之间差异均无统计学意义(表2)。切除腔外复发组患者分别占训练集和验证集的55.95%(47/84)和54.05%(20/37),2组间差异无统计学意义,即数据集之间数据分布均衡具有可比性。而在训练集和验证集中,切除腔内复发组和切除腔外复发组之间患者临床、病理及影像特征差异均无统计学意义(表3)。
在训练集中采用Pearson或Spearman相关分析和LASSO回归模型,通过最小惩罚λ对影像组学标签进行构建,最小λ值为0.089,logλ为-2.409(图3)。
最终筛选出与空间复发模式相关的7个非零系数特征(表4)。基于所筛选的组学特征及对应的LASSO回归权重系数,计算每位患者影像组学评分,计算公式如下:Radiomics score=5.553 053 63+0.257 657 01×T1W1增强瘤内实质区域-LoG-sigma-1.0-mm-3D_ GlCM_Cluster Shade-21.078 233 16×T1WI增强瘤内实质区域-Wavelet-LHH_GLCM_Maximum Probability-0.078 974 34× T1WI增强瘤内实质区域-Wavelet-HHL_First Order_Skewness+0.027 782 69×T1WI增强瘤内实质区域-Wavelet-HHH_First Order_Skewness+4.944 656 63×T2WI/FLAIR高信号瘤周区域-LoG-sigma-2.0-mm-3D_GLSZM_Small Area low Gray Level Emphasis+1.054 961 69×T2WI/FLAIR高信号瘤周区域-LoG-sigma-4.0-mm-3D_GlCM_Maximum Probability+1.532 398 87×T2WI/FLAIR高信号瘤周区域-Wavelet-HHL_GLRLM_Short Run Low Gray Level Emphasis。
通过计算公式得到训练集与验证集中每位患者的影像组学评分(图4A、B)。在训练集中,切除腔内复发组和切除腔外复发组之间的影像组学评分存在统计学差异(P<0.001),随后在验证集中得到证实。切除腔外复发组患者的放射组学评分高于切除腔内复发组患者(图4C、D)。在训练集中,切除腔内复发组和切除腔外复发组的放射组学评分分别为-0.030(-0.226,0.248)和0.424(0.278,0.573),差异有统计学意义(Z=-5.393,P<0.001)。在验证集中切除腔内复发组和切除腔外复发组的放射组学评分分别为0.277(0.103,0.322)和0.369(0.258,0.487),差异有统计学意义(Z=-2.133,P=0.033)。
Logistic回归分析结果显示,影像组学评分是预测空间复发模式的独立因素(OR:345.734,95%CI:26.133~4 574.043,P<0.001)。因此,基于该评分构建了预测HGGs原位复发空间模式的影像组学模型,并绘制了列线图(图5A)。校准曲线和Hosmer-Lemeshow拟合优度检验(Hosmer-Lemeshow χ2=7.798,P=0.453)结果表明,在训练集中模型具有良好的校准能力(图5B)。此外,影像组学模型具有良好的预测效能,在训练集中模型的AUC值为0.844(95%CI:0.749~0.914,准确率:82.14%,敏感性:100.00%;特异性:59.46%,PPV:75.81%,NPV:100.00%)(图5D)。同时,在验证集中(图5C)验证了影像组学模型的良好校准能力(Hosmer-Lemeshow χ2=6.951,P=0.434)。验证集中模型的AUC值为0.706(95%CI:0.534~0.844,准确率:72.97%,敏感性:70.00%;特异性:76.47%,PPV:77.78%,NPV:68.42%)(图5E)。
本研究提取了T1WI增强瘤内实质区域和T2WI/FLAIR高信号瘤周区域的影像组学特征,无创性反映胶质瘤异质性,进一步预测HGGs原位复发空间模式。同时,本研究建立并验证了一个用于预测HGGs原位复发空间模式的影像组学模型,研究结果显示该模型具有良好的校准效果及预测效能。
原位复发是HGGs最常见的复发模式,约占所有复发的80%。目前,大量临床实践及文献发现,尽管在多模态融合神经导航、术中荧光引导等先进技术辅助下能最大范围安全切除肿瘤,但研究者观察到大多数肿瘤原位复发位于手术切除区域的边缘[8,18]。原位复发仍存在不同的空间复发模式,由此,本研究根据复发肿瘤与手术切除腔的距离关系,将原位复发分为切除腔内复发与切除腔外复发。HGGs原位复发的空间模式与患者预后相关,早期明确原位复发HGGs的空间复发模式,制定个体化治疗策略有助于改善HGGs患者的预后。已有研究报道,在胶质母细胞患者中,将手术切除边界扩展至增强区域以外或T2WI/FLAIR异常区域从而提高手术切除程度,能改善患者的预后[19-20]。因此,本研究推测对于切除腔外复发的高危患者,扩大切除范围有利于延长患者生存时间,而对于切除腔内复发的高危患者,增加局部放疗剂量可能改善其预后,但需要更大规模的前瞻性研究来进一步验证这一推论。
PETRECCA等[18]认为胶质瘤的异质性与肿瘤复发在手术切除区域的边缘有关。在影像学上,肿瘤异质性主要表现在T1WI增强瘤内实质区域和T2WI/FLAIR高信号瘤周区域[21-22]。如何无创性获取瘤内及瘤周异质性信息是近年来的热点问题,然而,传统的影像处理方法难以准确评估肿瘤的异质性。影像组学是一个新兴的研究领域,它将医学图像转化为大量的定量数据来揭示疾病信息。与传统的处理方法相比,影像组学可以提供客观、定量的病灶信息,无创性地反映肿瘤的异质性[12,23]。以往的研究表明,影像组学在预测胶质母细胞瘤复发模式方面具有较高的诊断效能[14-15]。其中,SHIM等[15]基于神经网络构建了动态磁敏感性对比增强MRI影像组学模型,模型预测原位复发的AUC值达到0.969。然而,多模态MRI影像组学模型预测原位复发空间模式的研究尚少见报道。本研究基于T1WI增强以及T2WI/FLAIR序列提取影像组学特征,进一步计算患者的影像组学评分,探讨影像组学评分在预测原位复发空间复发模式中的价值。影像组学评分由7个影像组学特征组成,包括2个一阶统计量特征和5个纹理特征。在这些特征中,权重系数最高的是GLCM,其次是GLSZM,两者都是纹理特征。纹理特征通过反映胶质瘤的异质性来检测潜在的肿瘤浸润[21]。研究结果表明,无论在训练集还是验证集,切除腔外复发组的影像组学评分均高于切除腔内复发组。这提示术前切除腔外复发组肿瘤异质性高于切除腔内复发组,即切除腔外复发组肿瘤在术前就具有较高的向外侵袭潜能。最后,本研究采用Logistic回归分析建立预测HGGs原位复发空间模式的影像组学模型,所构建的影像组学模型在训练集和验证集中均表现出良好的预测效能。综合T1WI增强瘤内实质区域和T2WI/FLAIR高信号瘤周区域提取的影像组学特征,能更全面地反映肿瘤的异质性特征[13],预测空间复发模式。
本研究具有一定的局限性。首先,本研究仅利用影像组学评分进行建模,未考虑临床人口学及其他特征指标,同时影像组学评分主要基于常规MRI序列进行计算,未来的研究可联合临床特征以及功能MR序列来改善模型性能。其次,本研究为单中心研究,样本量较小,模型只经过内部验证,缺乏外部验证。为了促进该模型的临床应用,有必要开展多中心、大样本的研究以验证其有效性。最后,本研究使用Logistic回归来构建预测模型,而其他机器学习方法,如支持向量机、K近邻分类器和高斯朴素贝叶斯等也可用于筛选最佳模型。未来需要基于机器学习方法来建立最优影像组学模型。
综上所述,本研究通过T1WI增强瘤内实质区域和T2WI/FLAIR高信号瘤周区域提取影像组学特征,形成影像组学评分,从而建立的影像组学模型能够在术前预测HGGs原位复发空间模式,为HGGs的个性化治疗策略提供参考依据。

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2025年第47卷第14期
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doi: 10.16016/j.2097-0927.202501030
  • 接收时间:2025-01-11
  • 首发时间:2026-10-28
  • 出版时间:2025-07-30
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  • 收稿日期:2025-01-11
  • 修回日期:2025-03-11
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    1陆军特色医学中心(第三军医大学大坪医院):放射科,重庆市影像医学与核医学临床研究中心,重庆
    2陆军特色医学中心(第三军医大学大坪医院):神经外科,重庆
    3陆军军医大学(第三军医大学)生物医学工程与影像医学系数字医学教研室,重庆

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