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Article(id=1190669168631492856, tenantId=1146029695717560320, journalId=1189873630562394117, issueId=1190669163988398295, articleNumber=null, orderNo=null, doi=10.11855/j.issn.0577-7402.1614.2025.0103, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1729180800000, receivedDateStr=2024-10-18, revisedDate=null, revisedDateStr=null, acceptedDate=1734192000000, acceptedDateStr=2024-12-15, onlineDate=1761807251364, onlineDateStr=2025-10-30, pubDate=1745769600000, pubDateStr=2025-04-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1761807251364, onlineIssueDateStr=2025-10-30, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1761807251364, creator=13701087609, updateTime=1761807251364, updator=13701087609, issue=Issue{id=1190669163988398295, tenantId=1146029695717560320, journalId=1189873630562394117, year='2025', volume='50', issue='4', pageStart='367', pageEnd='503', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1761807250258, creator=13701087609, updateTime=1761807667423, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1190670913772339410, tenantId=1146029695717560320, journalId=1189873630562394117, issueId=1190669163988398295, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1190670913772339411, tenantId=1146029695717560320, journalId=1189873630562394117, issueId=1190669163988398295, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=393, endPage=399, ext={EN=ArticleExt(id=1190669168916705531, articleId=1190669168631492856, tenantId=1146029695717560320, journalId=1189873630562394117, language=EN, title=Analysis of recurrent risk factors associated with high-grade non-functional pituitary neuroendocrine tumors, columnId=1190310109000602400, journalTitle=Medical Journal of Chinese People’s Liberation Army, columnName=Clinical Research, runingTitle=null, highlight=null, articleAbstract=

Objective To investigate the risk factors associated with recurrence of high-grade non-functioning pituitary neuroendocrine tumors (NF-PitNETs) following total resection. Methods A retrospective study was conducted on the clinical data of 252 patients with high-grade NF-PitNETs who underwent surgical treatment at the Department of Neurosurgery, Beijing Tiantan Hospital from January 2012 to December 2023, and met the inclusion criteria. High-grade NF-PitNETs included Knosp 3A, 3B, and 4-grade subtypes. Kaplan-Meier curves and Log-rank tests were employed to compare the progression-free survival (PFS) of Knosp 3A, 3B, and 4-grade patients. Cox regression analysis was applied to identify the risk factors associated with the recurrence of high-grade NF-PitNETs. Receiver operating characteristic (ROC) curve was used to calculate the area under the curve (AUC) of each recurrence-related factor to evaluate the diagnostic efficiency. Results Survival analysis revealed that there were significant differences in PFS among Knosp 3A, 3B, and 4-grade patients (P<0.001). The PFS of Knosp 3A was significantly better than that of grade 3B and 4-grade (P<0.05), while there was no significant difference between Knosp 3B and 4-grade (P=0.118). After integrating the three groups into Knosp 3A group and 3B-4 group, there were significant differences in PFS and some clinical features between the two groups (P<0.05). Cox regression analysis indicated that age <55 years old (HR=2.883, 95%CI 1.253-6.634; P=0.013), T2 heterogeneous signal (HR=1.842, 95%CI 1.061-3.197; P=0.030), Knosp 3B-4 (HR=2.190, 95%CI 1.069-4.488; P=0.032), and Ki-67 ≥3% (HR=2.266, 95%CI 1.265-4.061; P=0.006) were risk factors related to tumor recurrence. ROC curve analysis showed that the AUCs of the above-mentioned risk factors were 0.682, 0.706, 0.709 and 0.750, respectively, and the AUC of the multi-factor combined model (age+T2 signal+Knosp grade+Ki-67) was 0.838, which was significantly larger than that of each single risk factor (P<0.05). Conclusion High-grade NF-PitNETs patients with age <55 years old, T2 heterogeneous signal, Knosp 3B-4 and Ki-67≥3% have a higher recurrence risk. The combined application of multiple risk factors can improve the predictive value of recurrence.

, correspAuthors=Peng Li, authorNote=null, correspAuthorsNote=
E-mail:
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目的 探讨高分级无功能垂体神经内分泌肿瘤(NF-PitNETs)全切术后复发的风险因素。方法 收集2012年1月-2023年12月在首都医科大学附属北京天坛医院神经外科接受手术治疗且符合入组标准的252例高分级NF-PitNETs患者的临床资料进行回顾性分析。高分级NF-PitNETs包括Knosp 3A、3B及4级3个亚型，采用Kaplan-Meier曲线和Log-rank检验比较Knosp 3A、3B及4级的无进展生存率(PFS)，Cox回归分析筛选高分级NF-PitNETs复发的相关风险因素。采用受试者操作特征(ROC)曲线计算各复发相关风险因素的曲线下面积(AUC)以评价其诊断效能。结果 生存分析显示，Knosp 3A、3B及4级的PFS整体比较差异有统计学意义(P<0.001)；其中Knosp 3A级的PFS明显优于Knosp 3B和4级(P<0.05)，Knosp 3B与4级的PFS差异无统计学意义(P=0.118)。将上述3组整合为Knosp 3A组与3B-4组，两组的PFS及部分临床特征比较差异均有统计学意义(P<0.05)。Cox回归分析显示，年龄<55岁(HR=2.883，95%CI 1.253~6.634，P=0.013)、T2信号不均匀(HR=1.842，95%CI 1.061~3.197，P=0.030)、Knosp 3B-4级(HR=2.190，95%CI 1.069~4.488，P=0.032)、Ki-67≥3%(HR=2.266，95%CI 1.265~4.061，P=0.006)是肿瘤复发相关的风险因素。ROC曲线分析显示，上述风险因素对应的AUC分别为0.682、0.706、0.709及0.750，多因素联合模型(年龄+T2信号+Knosp分级+Ki-67)的AUC为0.838，明显大于上述各单一风险因素的AUC(P<0.05)。结论 年龄<55岁、T2信号不均匀、Knosp 3B-4级及Ki-67≥3%的高分级NF-PitNETs复发风险较高，多个风险因素联合应用可提高对肿瘤全切术后复发的预测价值。

, correspAuthors=李朋, authorNote=null, correspAuthorsNote=
李朋，E-mail：
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张哲，博士研究生，主要从事颅脑肿瘤综合治疗方面的研究

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张哲，博士研究生，主要从事颅脑肿瘤综合治疗方面的研究

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张哲，博士研究生，主要从事颅脑肿瘤综合治疗方面的研究

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PFS. 无进展生存率；NF-PitNETs. 无功能垂体神经内分泌肿瘤；A. Knosp 3A、3B和4级3组PFS的总体比较；B. Knosp 3A组与Knosp 3B-4组的PFS比较

, figureFileSmall=CL/I6qmnPBe1ajNgj1Q9xg==, figureFileBig=jSydBOTZR01WMZuWBLcXfA==, tableContent=null), ArticleFig(id=1190682872504791669, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1190669168631492856, language=EN, label=Fig.2, caption=Receiver operating characteristic (ROC) curves of risk factors associated with the recurrence of high-grade non-functioning pituitary neuroendocrine tumors (NF-PitNETs), figureFileSmall=5RZQSvmwd8VLjbgv8sTBWA==, figureFileBig=Ze0mlUSwkbZ3Y3l60mBpvQ==, tableContent=null), ArticleFig(id=1190682872597066358, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1190669168631492856, language=CN, label=图2, caption=各高分级NF-PitNETs复发风险因素的ROC曲线

NF-PitNETs. 无功能垂体神经内分泌肿瘤；ROC曲线. 受试者操作特征曲线

, figureFileSmall=5RZQSvmwd8VLjbgv8sTBWA==, figureFileBig=Ze0mlUSwkbZ3Y3l60mBpvQ==, tableContent=null), ArticleFig(id=1190682872714506871, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1190669168631492856, language=EN, label=Tab.1, caption=

Baseline characteristics of patients with non-functioning pituitary neuroendocrine tumors (NF-PitNETs)(n=252)

, figureFileSmall=null, figureFileBig=null, tableContent=
指标 数值
年龄(岁, x±s) 49.6±11.7
年龄(岁, 范围) 23~84
年龄[例(%)]
≥55岁 100(39.7)
<55岁 152(60.3)
性别[例(%)]
男 85(33.7)
女 167(66.3)
肿瘤直径[例(%)]
≥4 cm 74(29.4)
<4 cm 178(70.6)
肿瘤体积[例(%)]
≥10 cm3 137(54.4)
<10 cm3 115(45.6)
T2信号不均匀[例(%)]
是 88(34.9)
否 164(65.1)
Knosp分级[例(%)]
3A 118(46.8)
3B 50(19.8)
4 84(33.4)
囊变/出血[例(%)]
是 29(11.5)
否 223(88.5)
病理谱系[例(%)]
TPIT 151(59.9)
SF1 101(40.1)
Ki-67[例(%)]
<3% 161(63.9)
≥3% 91(36.1)
随访时间(月, x±s) 42.6±20.0
随访时间(月, 范围) 11.2~120.4
), ArticleFig(id=1190682872865501816, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1190669168631492856, language=CN, label=表1, caption=

高分级NF-PitNETs患者的基线特征(n=252)

, figureFileSmall=null, figureFileBig=null, tableContent=
指标 数值
年龄(岁, x±s) 49.6±11.7
年龄(岁, 范围) 23~84
年龄[例(%)]
≥55岁 100(39.7)
<55岁 152(60.3)
性别[例(%)]
男 85(33.7)
女 167(66.3)
肿瘤直径[例(%)]
≥4 cm 74(29.4)
<4 cm 178(70.6)
肿瘤体积[例(%)]
≥10 cm3 137(54.4)
<10 cm3 115(45.6)
T2信号不均匀[例(%)]
是 88(34.9)
否 164(65.1)
Knosp分级[例(%)]
3A 118(46.8)
3B 50(19.8)
4 84(33.4)
囊变/出血[例(%)]
是 29(11.5)
否 223(88.5)
病理谱系[例(%)]
TPIT 151(59.9)
SF1 101(40.1)
Ki-67[例(%)]
<3% 161(63.9)
≥3% 91(36.1)
随访时间(月, x±s) 42.6±20.0
随访时间(月, 范围) 11.2~120.4
), ArticleFig(id=1190682872974553721, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1190669168631492856, language=EN, label=Tab.2, caption=

Preoperative predominant clinical manifestations of high-grade non-functioning pituitary neuroendocrine tumors (NF-PitNETs) (n=252)

, figureFileSmall=null, figureFileBig=null, tableContent=
临床表现 例(%)
头痛程度
<中度 235(93.3)
≥中度 17(6.7)
视野受损程度
<中度 143(56.7)
≥中度 109(43.3)
动眼神经受累
是 8(3.2)
否 244(96.8)
内分泌功能受累
是 21(8.3)
否 231(91.7)
尿崩症
是 3(1.2)
否 249(98.8)
), ArticleFig(id=1190682873079411322, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1190669168631492856, language=CN, label=表2, caption=

高分级NF-PitNETs术前主要临床表现(n=252)

, figureFileSmall=null, figureFileBig=null, tableContent=
临床表现 例(%)
头痛程度
<中度 235(93.3)
≥中度 17(6.7)
视野受损程度
<中度 143(56.7)
≥中度 109(43.3)
动眼神经受累
是 8(3.2)
否 244(96.8)
内分泌功能受累
是 21(8.3)
否 231(91.7)
尿崩症
是 3(1.2)
否 249(98.8)
), ArticleFig(id=1190682873263960699, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1190669168631492856, language=EN, label=Tab.3, caption=

Comparison of clinical characteristics between Knosp grade 3A and grades 3B-4 non-functioning pituitary neuroendocrine tumors (NF-PitNETs)

, figureFileSmall=null, figureFileBig=null, tableContent=
指标

Knosp 3A组

(n=118)

Knosp 3B-4组

(n=134)

 P
年龄(岁, x±s) 52.5±12.1 47.0±10.7 <0.001
年龄[例(%)] <0.001
≥55岁 67(56.8) 33(24.6)
<55岁 51(43.2) 101(75.4)
性别[例(%)] 0.003
男 51(43.2) 34(25.4)
女 67(56.8) 100(74.6)
肿瘤直径[例(%)] 0.001
≥4 cm 16(13.6) 58(43.3)
<4 cm 102(86.4) 76(56.7)
肿瘤体积[例(%)] 0.002
≥10 cm3 52(44.1) 85(63.4)
<10 cm3 66(55.9) 49(36.6)
T2信号不均匀[例(%)] <0.001
否 91(77.1) 73(54.5)
是 27(22.9) 61(45.5)
囊变/出血[例(%)] 0.080
否 100(84.7) 123(91.8)
是 18(15.3) 11(8.2)
病理谱系[例(%)] <0.001
SF1(+) 67(56.8) 34(25.4)
TPIT(+) 51(43.2) 100(74.6)
Ki-67[例(%)] 0.045
<3% 83(70.3) 78(58.2)
≥3% 35(29.7) 56(41.8)
), ArticleFig(id=1190682873352041084, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1190669168631492856, language=CN, label=表3, caption=

Knosp 3A与3B-4级NF-PitNETs患者的临床特征比较

, figureFileSmall=null, figureFileBig=null, tableContent=
指标

Knosp 3A组

(n=118)

Knosp 3B-4组

(n=134)

 P
年龄(岁, x±s) 52.5±12.1 47.0±10.7 <0.001
年龄[例(%)] <0.001
≥55岁 67(56.8) 33(24.6)
<55岁 51(43.2) 101(75.4)
性别[例(%)] 0.003
男 51(43.2) 34(25.4)
女 67(56.8) 100(74.6)
肿瘤直径[例(%)] 0.001
≥4 cm 16(13.6) 58(43.3)
<4 cm 102(86.4) 76(56.7)
肿瘤体积[例(%)] 0.002
≥10 cm3 52(44.1) 85(63.4)
<10 cm3 66(55.9) 49(36.6)
T2信号不均匀[例(%)] <0.001
否 91(77.1) 73(54.5)
是 27(22.9) 61(45.5)
囊变/出血[例(%)] 0.080
否 100(84.7) 123(91.8)
是 18(15.3) 11(8.2)
病理谱系[例(%)] <0.001
SF1(+) 67(56.8) 34(25.4)
TPIT(+) 51(43.2) 100(74.6)
Ki-67[例(%)] 0.045
<3% 83(70.3) 78(58.2)
≥3% 35(29.7) 56(41.8)
), ArticleFig(id=1190682873427538557, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1190669168631492856, language=EN, label=Tab.4, caption=

Univariate and multivariate analyses of recurrence in high-grade non-functioning pituitary neuroendocrine tumors (NF-PitNETs)

, figureFileSmall=null, figureFileBig=null, tableContent=
变量 单因素分析 Cox多因素分析
HR (95% CI) P HR (95% CI) P
头痛(<中度/≥中度) 1.783(0.645~4.929) 0.265 NA NA
视野受损(<中度/≥中度) 1.515(0.921~2.492) 0.101 NA NA
动眼神经受累(是/否) 1.719(0.415~7.116) 0.455 NA NA
内分泌功能受累(是/否) 1.358(0.643~2.870) 0.423 NA NA
尿崩症(是/否) 1.262(0.303~5.257) 0.749 NA NA
年龄<55岁(是/否) 5.558(2.527~12.226) <0.001 2.883(1.253~6.634) 0.013
性别(男/女) 1.220(0.730~2.040) 0.448 NA NA
T2信号不均匀(是/否) 3.263(1.930~5.517) <0.001 1.842(1.061~3.197) 0.030
囊变/出血(是/否) 0.973(0.511~1.850) 0.933 NA NA
肿瘤直径(<4 cm/≥4 cm) 0.927(0.521~1.648) 0.796 NA NA
肿瘤体积(<10 cm3/≥10 cm3) 0.900(0.548~1.477) 0.676 NA NA
病理谱系(SF1/TIPT) 0.989(0.592~1.652) 0.966 NA NA
Knosp 3B-4级(是/否) 3.838(1.947~7.565) <0.001 2.190(1.069~4.488) 0.032
Ki-67≥3%(是/否) 3.315(1.873~5.868) <0.001 2.266(1.265~4.061) 0.006
), ArticleFig(id=1190682873528201854, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1190669168631492856, language=CN, label=表4, caption=

高分级NF-PitNETs复发的单因素和多因素分析

, figureFileSmall=null, figureFileBig=null, tableContent=
变量 单因素分析 Cox多因素分析
HR (95% CI) P HR (95% CI) P
头痛(<中度/≥中度) 1.783(0.645~4.929) 0.265 NA NA
视野受损(<中度/≥中度) 1.515(0.921~2.492) 0.101 NA NA
动眼神经受累(是/否) 1.719(0.415~7.116) 0.455 NA NA
内分泌功能受累(是/否) 1.358(0.643~2.870) 0.423 NA NA
尿崩症(是/否) 1.262(0.303~5.257) 0.749 NA NA
年龄<55岁(是/否) 5.558(2.527~12.226) <0.001 2.883(1.253~6.634) 0.013
性别(男/女) 1.220(0.730~2.040) 0.448 NA NA
T2信号不均匀(是/否) 3.263(1.930~5.517) <0.001 1.842(1.061~3.197) 0.030
囊变/出血(是/否) 0.973(0.511~1.850) 0.933 NA NA
肿瘤直径(<4 cm/≥4 cm) 0.927(0.521~1.648) 0.796 NA NA
肿瘤体积(<10 cm3/≥10 cm3) 0.900(0.548~1.477) 0.676 NA NA
病理谱系(SF1/TIPT) 0.989(0.592~1.652) 0.966 NA NA
Knosp 3B-4级(是/否) 3.838(1.947~7.565) <0.001 2.190(1.069~4.488) 0.032
Ki-67≥3%(是/否) 3.315(1.873~5.868) <0.001 2.266(1.265~4.061) 0.006
), ArticleFig(id=1190682873620476543, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1190669168631492856, language=EN, label=Tab.5, caption=

Diagnostic efficacy of risk factors associated with the recurrence of high-grade non-functioning pituitary neuroendocrine tumors (NF-PitNETs)

, figureFileSmall=null, figureFileBig=null, tableContent=
变量 AUC(95%CI) 敏感度(%)(95%CI) 特异度(%)(95%CI) 阳性预测值(%)(95%CI) 阴性预测值(%)(95%CI) 约登指数 P
年龄<55岁 0.682(0.621~0.739) 87.5(76.8~94.4) 48.9(41.6~56.3) 36.8(33.0~40.8) 92.0(85.5~95.7) 0.3644 <0.001*
T2信号不均匀 0.706(0.645~0.761) 65.6(52.7~77.1) 75.5(68.7~81.5) 47.7(40.2~55.4) 86.6(82.0~90.1) 0.4116 0.003*
Knosp 3B-4级 0.709(0.649~0.764) 84.4(73.1~92.2) 57.5(50.0~64.6) 40.3(35.7~45.1) 91.5(85.8~95.1) 0.4182 0.001*
Ki-67≥3% 0.750(0.692~0.802) 73.4(60.9~83.7) 76.6(69.9~82.4) 51.6(44.2~59.0) 89.4(84.8~92.8) 0.5003 0.036*
多因素联合模型 0.838(0.786~0.881) 76.6(64.3~86.2) 81.9(75.7~87.1) 59.0(50.8~66.8) 91.1(86.8~94.1) 0.5848
), ArticleFig(id=1190682873775665792, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1190669168631492856, language=CN, label=表5, caption=

各高分级NF-PitNETs复发风险因素的诊断效能

, figureFileSmall=null, figureFileBig=null, tableContent=
变量 AUC(95%CI) 敏感度(%)(95%CI) 特异度(%)(95%CI) 阳性预测值(%)(95%CI) 阴性预测值(%)(95%CI) 约登指数 P
年龄<55岁 0.682(0.621~0.739) 87.5(76.8~94.4) 48.9(41.6~56.3) 36.8(33.0~40.8) 92.0(85.5~95.7) 0.3644 <0.001*
T2信号不均匀 0.706(0.645~0.761) 65.6(52.7~77.1) 75.5(68.7~81.5) 47.7(40.2~55.4) 86.6(82.0~90.1) 0.4116 0.003*
Knosp 3B-4级 0.709(0.649~0.764) 84.4(73.1~92.2) 57.5(50.0~64.6) 40.3(35.7~45.1) 91.5(85.8~95.1) 0.4182 0.001*
Ki-67≥3% 0.750(0.692~0.802) 73.4(60.9~83.7) 76.6(69.9~82.4) 51.6(44.2~59.0) 89.4(84.8~92.8) 0.5003 0.036*
多因素联合模型 0.838(0.786~0.881) 76.6(64.3~86.2) 81.9(75.7~87.1) 59.0(50.8~66.8) 91.1(86.8~94.1) 0.5848
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高分级无功能垂体神经内分泌肿瘤全切术后复发的相关风险因素分析
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张哲 , 别志旭 , 刘丕楠 , 李朋 *
解放军医学杂志 | 临床研究 2025,50(4): 393-399
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解放军医学杂志 | 临床研究 2025, 50(4): 393-399
高分级无功能垂体神经内分泌肿瘤全切术后复发的相关风险因素分析
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张哲, 别志旭, 刘丕楠, 李朋*
作者信息
  • 首都医科大学附属北京天坛医院神经外科，100070 北京

    • 张哲，博士研究生，主要从事颅脑肿瘤综合治疗方面的研究

通讯作者:

李朋，E-mail：
Analysis of recurrent risk factors associated with high-grade non-functional pituitary neuroendocrine tumors
Zhe Zhang, Zhi-Xu Bie, Pi-Nan Liu, Peng Li*
Affiliations
  • Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China
出版时间: 2025-04-28 doi: 10.11855/j.issn.0577-7402.1614.2025.0103
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摘要
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目的 探讨高分级无功能垂体神经内分泌肿瘤(NF-PitNETs)全切术后复发的风险因素。方法 收集2012年1月-2023年12月在首都医科大学附属北京天坛医院神经外科接受手术治疗且符合入组标准的252例高分级NF-PitNETs患者的临床资料进行回顾性分析。高分级NF-PitNETs包括Knosp 3A、3B及4级3个亚型，采用Kaplan-Meier曲线和Log-rank检验比较Knosp 3A、3B及4级的无进展生存率(PFS)，Cox回归分析筛选高分级NF-PitNETs复发的相关风险因素。采用受试者操作特征(ROC)曲线计算各复发相关风险因素的曲线下面积(AUC)以评价其诊断效能。结果 生存分析显示，Knosp 3A、3B及4级的PFS整体比较差异有统计学意义(P<0.001)；其中Knosp 3A级的PFS明显优于Knosp 3B和4级(P<0.05)，Knosp 3B与4级的PFS差异无统计学意义(P=0.118)。将上述3组整合为Knosp 3A组与3B-4组，两组的PFS及部分临床特征比较差异均有统计学意义(P<0.05)。Cox回归分析显示，年龄<55岁(HR=2.883，95%CI 1.253~6.634，P=0.013)、T2信号不均匀(HR=1.842，95%CI 1.061~3.197，P=0.030)、Knosp 3B-4级(HR=2.190，95%CI 1.069~4.488，P=0.032)、Ki-67≥3%(HR=2.266，95%CI 1.265~4.061，P=0.006)是肿瘤复发相关的风险因素。ROC曲线分析显示，上述风险因素对应的AUC分别为0.682、0.706、0.709及0.750，多因素联合模型(年龄+T2信号+Knosp分级+Ki-67)的AUC为0.838，明显大于上述各单一风险因素的AUC(P<0.05)。结论 年龄<55岁、T2信号不均匀、Knosp 3B-4级及Ki-67≥3%的高分级NF-PitNETs复发风险较高，多个风险因素联合应用可提高对肿瘤全切术后复发的预测价值。

高分级  /  无功能垂体神经内分泌肿瘤  /  复发  /  风险因素

Objective To investigate the risk factors associated with recurrence of high-grade non-functioning pituitary neuroendocrine tumors (NF-PitNETs) following total resection. Methods A retrospective study was conducted on the clinical data of 252 patients with high-grade NF-PitNETs who underwent surgical treatment at the Department of Neurosurgery, Beijing Tiantan Hospital from January 2012 to December 2023, and met the inclusion criteria. High-grade NF-PitNETs included Knosp 3A, 3B, and 4-grade subtypes. Kaplan-Meier curves and Log-rank tests were employed to compare the progression-free survival (PFS) of Knosp 3A, 3B, and 4-grade patients. Cox regression analysis was applied to identify the risk factors associated with the recurrence of high-grade NF-PitNETs. Receiver operating characteristic (ROC) curve was used to calculate the area under the curve (AUC) of each recurrence-related factor to evaluate the diagnostic efficiency. Results Survival analysis revealed that there were significant differences in PFS among Knosp 3A, 3B, and 4-grade patients (P<0.001). The PFS of Knosp 3A was significantly better than that of grade 3B and 4-grade (P<0.05), while there was no significant difference between Knosp 3B and 4-grade (P=0.118). After integrating the three groups into Knosp 3A group and 3B-4 group, there were significant differences in PFS and some clinical features between the two groups (P<0.05). Cox regression analysis indicated that age <55 years old (HR=2.883, 95%CI 1.253-6.634; P=0.013), T2 heterogeneous signal (HR=1.842, 95%CI 1.061-3.197; P=0.030), Knosp 3B-4 (HR=2.190, 95%CI 1.069-4.488; P=0.032), and Ki-67 ≥3% (HR=2.266, 95%CI 1.265-4.061; P=0.006) were risk factors related to tumor recurrence. ROC curve analysis showed that the AUCs of the above-mentioned risk factors were 0.682, 0.706, 0.709 and 0.750, respectively, and the AUC of the multi-factor combined model (age+T2 signal+Knosp grade+Ki-67) was 0.838, which was significantly larger than that of each single risk factor (P<0.05). Conclusion High-grade NF-PitNETs patients with age <55 years old, T2 heterogeneous signal, Knosp 3B-4 and Ki-67≥3% have a higher recurrence risk. The combined application of multiple risk factors can improve the predictive value of recurrence.

high grade  /  non-functioning pituitary neuroendocrine tumors  /  recurrence  /  risk factors
张哲, 别志旭, 刘丕楠, 李朋. 高分级无功能垂体神经内分泌肿瘤全切术后复发的相关风险因素分析. 解放军医学杂志, 2025 , 50 (4) : 393 -399 . DOI: 10.11855/j.issn.0577-7402.1614.2025.0103
Zhe Zhang, Zhi-Xu Bie, Pi-Nan Liu, Peng Li. Analysis of recurrent risk factors associated with high-grade non-functional pituitary neuroendocrine tumors[J]. Medical Journal of Chinese People’s Liberation Army, 2025 , 50 (4) : 393 -399 . DOI: 10.11855/j.issn.0577-7402.1614.2025.0103
正文
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垂体神经内分泌肿瘤(pituitary neuroendocrine tumors，PitNETs)是常见的颅脑肿瘤之一[1]。其中，无功能PitNETs(non-functioning PitNETs，NF-PitNETs)约占所有PitNETs的1/3，这类肿瘤不表现出激素过度分泌的症状，生长过程较为隐匿，部分可侵入海绵窦[2]。当肿瘤侵袭范围达到Knosp 3A、3B或4级时，该肿瘤被定义为高分级NF-PitNETs，此时肿瘤全切手术难度较大[3]。部分病例即使在全切手术后仍出现早期或多次复发[4-5]，严重影响患者的预后，因此，准确评估高分级NF-PitNETs患者复发风险是临床中亟待解决的重要问题。
目前，已有大量文献报道了与PitNETs复发相关的多种因素，包括患者的年龄、性别、肿瘤大小、侵袭性分级、Ki-67表达程度，以及p53或ATRX基因突变等[6-10]，但仍缺乏针对高分级NF-PitNETs全切除术后复发风险因素的系统性研究。本研究为了更精准地预测肿瘤的复发情况，构建了一个简洁、实用且精准的多因素预测模型，全面系统地探讨高分级NF-PitNETs复发的相关风险因素，旨在通过整合临床、影像及病理特征的多维度分析，及时对高危复发风险的患者进行干预，改善其生活质量及预后。
1 资料与方法
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1.1 研究对象
收集2012年1月-2023年12月在首都医科大学附属北京天坛医院神经外科接受手术治疗且符合入组标准的252例高分级NF-PitNETs患者的临床资料进行回顾性分析。NF-PitNETs的诊断标准：临床症状、实验室检查无垂体激素过量分泌表现，病理诊断证实为PitNETs(初次或复发手术病理报告中包含垂体细胞的分化谱系、激素种类等分子病理结果)。纳入标准：(1)年龄≥18岁；(2)术前头部磁共振成像(MRI)提示鞍区占位病变且Knosp分级≥3，包括增强的T1加权图像(轴、冠及矢状位)和T2加权图像(轴位)；(3)术前未接受过药物治疗、放疗或参加其他临床试验；(4)在本院神经外科行显微或经鼻蝶肿瘤切除术；(5)肿瘤获得完全切除；(6)术后复查增强MRI证实肿瘤复发；(7)依从性良好，配合复查随访。排除标准：(1)临床病历、MRI资料不完整或MRI图像质量不佳存在伪影等无法用于研究；(2)病理诊断不明或无分子病理结果。所有患者的临床相关信息依据其电子病历资料进行录入；MRI数据从影像归档和通信系统(picture archiving and communication system，PACS)获得。所有数据由两名研究人员分别录入后交叉核对。本研究获首都医科大学附属北京天坛医院伦理委员会审批(KY2023-170-02)。
1.2 神经影像学评估
术前MRI包括轴位、冠状位及矢状位增强T1图像，轴位T1和T2平扫图像。MRI层厚为2~6 mm。评估的影像特征包括：肿瘤最大直径、肿瘤体积、Knosp分级、出血/囊变以及T2信号异质性。使用3D slicer软件(https://www.slicer.org/)对T1增强MRI图像进行感兴趣区域(region of interest，ROI)勾画，并测定肿瘤最大直径、肿瘤体积。T1/T2提示肿瘤合并囊变或出血征象时予以记录，并从总体肿瘤体积测量中减去肿瘤内囊变或出血相对应的体积。根据冠状面T1加权增强图像确定Knosp分级(3A、3B和4级)；肿瘤完全切除标准：术后增强MRI显示肿瘤切除程度为100%[3]。所有的MRI变量均由3位具有10年及以上经验的神经放射学专家在不知晓临床信息的情况下独立评估。
1.3 组织病理学诊断
手术中切除的肿瘤标本用4%中性甲醛溶液固定，随后包埋于石蜡中制备病理切片，采用苏木精-伊红(HE)染色进行病理组织学诊断。通过免疫组化方法确定肿瘤组织中的转录因子[垂体特异性转录因子(pituitary-specific transcription factor-1，PIT-1)、T-box垂体转录因子(T-box pituitary transcription factor，TPIT)、类固醇合成因子1(steroidogenic factor 1，SF1)]、激素标志物[生长激素(growth hormone，GH)、催乳素(prolactin，PRL)、促甲状腺激素(thyroid stimulating hormone，TSH)、促黄体生成激素(leuteinizing hormone，LH)、促卵泡成熟激素(follicle stimulating hormone，FSH)、促肾上腺皮质激素(adrenocorticotropic hormone，ACTH)等]及肿瘤增殖标志物蛋白Ki-67的表达情况。所有病理切片由3位经验丰富的神经病理学家进行评估。
1.4 随访
患者术后3个月、6个月、1年复查头颅MRI平扫+增强序列，此后每年规律复查上述MRI检查。随访形式为门诊及电话随访，肿瘤是否复发亦由3位具有10年以上经验丰富的神经放射学专家在不知晓临床信息的情况下进行评价，随访截止日期为2024年8月。
1.5 指标定义
肿瘤复发定义为术后头部增强MRI复查时发现有新发病灶；无进展生存期定义为患者首次接受手术后病理确诊的时间至术后发现肿瘤复发的时间间隔。头痛程度依据数字评定量表，0-10数字逐级递增的方式描述[11]。疼痛程度的分级标准如下：无痛(0)、轻度疼痛(1~3)、中度疼痛(4~6)以及重度及以上疼痛(≥7)。每个视野以中心分为8个区域：鼻上内/外、鼻下内/外、颞上内/外、颞下内/外。每个区域有视野缺损记作1分，无缺损记作0分。视野损伤等级分为无损伤(0分)、轻度(1或2分)、中度(3或4分)、重度(5或6分)和极重度(7或8分)5个等级[12]。
1.6 指标分析
根据Knosp分级将NF-PitNETs分为3A、3B及4级3个组，绘制Kaplan-Meier曲线并比较各组患者的无进展生存率(progression-free survival，PFS)，以确定Knosp预后亚型。依据肿瘤最大直径(≥4 cm与<4 cm)和体积(≥10 cm³与<10 cm³)[13]对患者进行分组，以分析肿瘤大小与预后的关系。Ki-67阳性细胞核的百分比是反映肿瘤增殖能力的重要指标，其中Ki-67≥3%提示肿瘤侵袭性高、增殖活性强且预后不良[5]，因此将患者分为Ki-67≥3%和<3%两组。采用受试者操作特征(receiver operating characteristic，ROC)曲线计算肿瘤复发的最佳年龄截断值[14]。根据截断值=55岁，将患者分为≥55岁与<55岁两组。同时将其他临床、影像、病理特性进行单因素和多因素分析以筛选肿瘤复发相关的不良因素，构建多因素联合预测模型，并评价其预测效能。
1.7 统计学处理
采用SPSS 26.0软件(IBM Corporation，Armonk，NY，USA)进行统计分析。计量资料以x±s表示，两组间比较采用t检验；计数资料以例(%)表示，组间比较采用χ2检验；采用Kaplan-Meier曲线和Log-rank检验比较Knosp3A、3B及4级患者的PFS差异。单因素分析中P<0.05的因素纳入Cox多因素回归分析，计算各复发相关因素的风险比(hazard ratio，HR)及95%置信区间(confidence interval，95%CI)，以排除影响预后的混杂因素。采用ROC曲线计算复发相关风险因素的曲线下面积(area under the curve，AUC)、敏感度、特异度、阳性预测值和阴性预测值，以评估其诊断效能。采用双侧检验，P<0.05为差异有统计学意义。
2 结果
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2.1 患者临床基线特征
252例高分级NF-PitNETs患者的年龄为23~84(49.6±11.7)岁，男性占33.7%，女性占66.3%，其他临床基线特征详见表1。
多数患者在术前表现为中度以下头痛(93.3%)和视野受损(56.8%)，少部分患者表现为动眼神经受累(3.2%)、内分泌功能受累(8.3%)和尿崩症(1.2%)(表2)。
2.2 高分级NF-PitNETs患者PFS及临床特征比较
Kaplan-Meier曲线分析显示，Knosp 3A、3B和4级组间的PFS总体比较差异有统计学意义(P<0.001)(图1A)；其中，Knosp 3A组的PFS明显优于Knosp 3B组(P=0.027)和Knosp 4组(P<0.001)；而Knosp 3B组与Knosp 4组的PFS差异无统计学意义(P=0.118)。根据上述结果将Knosp 3B和4两组患者合并为Knosp 3B-4组，而Knosp 3A组的PFS明显优于Knosp 3B-4组(P<0.001)(图1B)。此外，对Knosp 3A组与Knosp 3B-4组不同临床基线特征的PFS进行比较，结果显示，Knosp 3B-4组患者在年龄<55岁、女性、肿瘤直径≥4 cm、肿瘤体积≥10 cm3、T2信号异质性、TPIT及Ki-67≥3%等方面所占的比例均明显高于Knosp 3A组(P<0.05，表3)。
2.3 高分级NF-PitNETs复发的单因素及多因素分析
单因素分析结果显示，年龄<55岁、T2信号不均匀、Knosp 3B-4级及Ki-67≥3%为高分级NF-PitNETs潜在的复发相关因素(P<0.05)，而症状、体征、性别、囊变/出血、肿瘤最大直径、肿瘤体积及病理谱系与肿瘤复发无明显相关性。将单因素分析中P<0.05的因素纳入Cox多因素回归分析，结果显示，年龄<55岁(HR=2.883，95%CI 1.253~6.634，P=0.013)、T2信号不均匀(HR=1.842，95%CI 1.061~3.197，P=0.030)、Knosp 3B-4级(HR=2.190，95%CI 1.069~4.488，P=0.032)、Ki-67≥3%(HR=2.266，95%CI 1.265~4.061，P=0.006)是肿瘤复发的风险因素(表4)。
根据上述结果，得到Cox多因素的危险率函数模型：h(t, Xi)=h0(t)×exp (b1x1+b2x2+b3x3+b4x4)=h0(t)×exp(1.059x1+0.784x2+0.611x3+0.818x4)。其中，h0t是4个变量都取值为0时的基线风险；x1-4分别为年龄<55岁、 T2信号不均匀、Knosp 3B-4级及Ki-67≥3%。多因素联合模型的累积生存函数：S(t, Xi)=S0(t)×exp(b1x1+b2x2+b3x3+b4x4) =S0(t)×exp(1.059x1+0.784x2+0.611x3+0.818x4)，其中，S0t为基线生存函数。
2.4 应用ROC曲线评价各高分级NF-PitNETs复发相关因素的诊断效能
ROC曲线分析结果显示，年龄<55岁、T2信号不均匀、Knosp 3B-4级、Ki-67≥3%及(年龄+T2信号+Knosp分级+Ki-67)多因素联合模型生存概率的AUC分别为0.682、0.706、0.709、0.750和0.838，且多因素联合模型的AUC明显大于上述各单一因素(P<0.05)。因此，多因素联合模型在预测肿瘤复发方面的诊断效能更佳(图2、表5)。
3 讨论
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尽管高分级NF-PitNETs可在手术中实现全切，但患者仍面临较高的复发风险。因此，准确预测高危人群对于临床个体化管理具有至关重要的意义。虽然PitNETs多为良性，但部分肿瘤侵袭性强[3]、生长速度快[5]、术后易复发[15-16]，尤其是Knosp 3-4级的PitNETs，上述侵袭性表现更为明显[5,8]。有研究表明，Knosp 3A与3B级PitNETs的复发率不同[3]。然而，关于Knosp 3A、3B和4级NF-PitNETs的预后情况尚不完全明确。本研究比较了Knosp 3A、3B和4级的PFS，并将上述3组整合为预后不同的两个亚型：Knosp 3A与Knosp 3B-4级，结果显示，这两个亚型在年龄、性别、肿瘤大小、T2信号异质性、病理谱系及Ki-67表达水平等临床特征方面存在明显差异。此外，本研究通过Cox回归分析进一步证实Knosp 3B-4级与肿瘤复发相关，且Knosp 3B-4级肿瘤复发风险是Knosp 3A级的2.190倍，提示Knosp 3A与Knosp 3B-4级的预后存在差异。
尽管已有大量研究探讨了年龄对PitNETs复发的影响，但二者之间的确切联系仍存在争议[8,17-22]。其中，有部分研究发现，年轻患者的复发风险明显高于年长者[18,20]，且年轻患者的复发速度较年长者快[17]；但也有研究指出，患者确诊时的年龄与肿瘤复发无明显相关性[21]。此外，年龄分层与肿瘤复发的关系尚不明确。ROC曲线可将连续型变量数据转换为二分类变量，临床中常被用于判定最佳临界值[14,23]。本研究通过ROC曲线分析确定年龄=55岁为影响肿瘤复发的最佳截断值，并经Cox回归分析证实，年龄<55岁是高分级NF-PitNETs复发的风险因素。
T2信号不均匀被认为是肿瘤异质性的可靠影像学指标[24-25]，可用于预测PitNETs病理特征及药物治疗反应性[26-28]。尽管如此，T2信号不均匀与高分级NF-PitNETs复发之间的关系尚待进一步明确。本研究发现，相较于不具备此特征的患者，伴有T2信号不均匀肿瘤的患者面临更高的复发风险。T2加权成像为MRI检查常用的序列，影像特征容易获取，其对PitNETs的个体化诊疗具有指导价值。
此外，Ki-67≥3%也是评价高分级NF-PitNETs复发的重要指标。研究表明，Ki-67≥3%与PitNETs的侵袭性及不良预后有关[29-30]，其影响肿瘤生物学行为的免疫机制可能为：Ki-67≥3%时，肿瘤微环境中的FOXP3+调节性T细胞增多，CD8/CD4、CD8/FOXP3、CD68/FOXP3比值降低[31]，表现为免疫抑制状态，促进肿瘤生长。
值得注意的是，PitNETs具有较高的异质性，单一因素无法准确预测肿瘤复发[8]。因此，本研究基于Cox回归分析中的生存函数构建了多因素联合模型(年龄+T2信号+Knosp分级+Ki-67)，并应用ROC曲线对各单一因素与多因素联合模型进行比较，发现多因素联合模型的AUC明显大于年龄<55岁、T2信号不均匀、Knosp 3B-4级及Ki-67≥3%各单一因素的AUC，该结果进一步证实多因素联合模型能够更加准确地预测肿瘤复发。
综上所述，年龄<55岁、T2信号不均匀、Knosp 3B-4级及Ki67≥3%的高分级NF-PitNETs具有更高的复发风险。多因素联合模型(年龄+T2信号+Knosp分级+Ki-67)预测肿瘤复发的诊断效能高于单一因素，有助于更加准确地评估高分级NF-PitNETs的预后。然而，本研究仍存在一定局限性：(1)由于采用回顾性分析方法，在临床表现、影像特征、病理类型以及随访时间方面存在一定限制；(2)尽管本研究尝试应用多因素联合模型预测肿瘤复发，但由于样本量有限和单中心数据的限制，尚未对预测模型进行内部和外部验证。因此，未来可通过多中心合作开展前瞻性研究，扩大病理类型的涵盖范围，并延长随访时间，以深入探究各因素之间的相关性，验证模型的准确性，从而提升本研究的临床应用价值和普适性。这将有助于优化此类患者的个体化管理策略，对高危复发人群尽早进行干预，改善其预后。
基金
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  • 国家自然科学基金(81974387)
  • 首都临床特色诊疗技术研究及转化应用(Z221100007422041)
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doi: 10.11855/j.issn.0577-7402.1614.2025.0103
  • 接收时间：2024-10-18
  • 首发时间：2025-10-30
  • 出版时间：2025-04-28
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  • 收稿日期：2024-10-18
  • 录用日期：2024-12-15
基金
National Natural Science Foundation of China(81974387)
国家自然科学基金(81974387)
Capital Clinical Characteristic Diagnosis and Treatment Technology Research and Transformation Application(Z221100007422041)
首都临床特色诊疗技术研究及转化应用(Z221100007422041)
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    首都医科大学附属北京天坛医院神经外科，100070 北京

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