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张昊东方, 牛小东, 周兴旺, 等. 成人脑室胶质瘤患者预后列线图模型的构建与评价[J]. koko体育app 学报(医学版), 2022, 53(4): 588-596. doi: 10.12182/20220760203
引用本文: 张昊东方, 牛小东, 周兴旺, 等. 成人脑室胶质瘤患者预后列线图模型的构建与评价[J]. koko体育app 学报(医学版), 2022, 53(4): 588-596. doi:
ZHANG Hao-dong-fang, NIU Xiao-dong, ZHOU Xing-wang, et al. Development and Evaluation of Prognostic Nomogram Model for Adult Ventricle Glioma Patients[J]. JOURNAL OF SICHUAN UNIVERSITY (MEDICAL SCIENCE EDITION), 2022, 53(4): 588-596. doi: 10.12182/20220760203
Citation: ZHANG Hao-dong-fang, NIU Xiao-dong, ZHOU Xing-wang, et al. Development and Evaluation of Prognostic Nomogram Model for Adult Ventricle Glioma Patients[J]. JOURNAL OF SICHUAN UNIVERSITY (MEDICAL SCIENCE EDITION), 2022, 53(4): 588-596. doi:

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成人脑室胶质瘤患者预后列线图模型的构建与评价

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Development and Evaluation of Prognostic Nomogram Model for Adult Ventricle Glioma Patients

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  • 摘要:   目的   探究成人脑室胶质瘤(adult ventricle glioma, AVG)患者的预后因素,进一步构建和评价预后列线图模型,为该类患者的临床管理提供一定的参考。   方法   本研究纳入SEER数据库(1973–2016)中经组织学明确诊断的AVG患者,用随机数字表法按2∶1比例分为训练集和验证集进行分析。使用Kaplan-Meier进行生存分析,Cox回归分析确定总生存(OS)和癌症特异性生存(CSS)的独立预后因素,结合患者基本特征,分别构建训练集中针对OS率和CSS率的生存相关列线图预测模型,再依次通过训练集和验证集进行模型的内部交叉验证和外部验证。C指数(C-index)用来评估列线图模型的真实性和可靠性,校准图用来评估训练集和验证集中预测值和观察值之间的一致性。   结果   本研究共纳入369例AVG患者,其中男性218例,女性151例,所有患者中位年龄为53岁。根据WHO分级,66例(17.9%)为Ⅱ级胶质瘤,73例(19.8%)为Ⅲ级胶质瘤,230例(62.3%)为Ⅳ级胶质瘤。根据手术切除程度,59 例(16.0%)为肿瘤全切,145例(39.3%)为次全切或部分切除。所有患者中,167例(45.3%)术后接受了放疗,143例(38.8%)术后接受了化疗。患者随机分为训练集 (n=246) 和验证集(n=123),训练集和验证集之间的基本临床特征的差异均无统计学意义(P>0.05)。训练集中Cox回归分析显示,年龄≥65岁、肿瘤分级III级和Ⅳ级、未接受放疗均是OS和CSS的独立预后因素。在训练集中,使用5个变量(年龄、性别、WHO 分级、手术和放疗)分别构建用于预测术后6个月、1年和2年OS率和CSS率的列线图模型。训练集内部交叉验证结果显示,OS率和CSS率的C指数分别为0.758和0.765;验证集外部验证结果显示,OS率和CSS率的C指数分别为0.733和0.719。训练集中6个月、1年和2年OS率的校准图均表现出良好的一致性,而在验证集中一致性相对较低。6个月、1年和2年CSS率的校准图与OS率校准图具有相似的结果。   结论   OS率和CSS率的列线图预测模型具有中等可靠的预测效能,可为临床医生简易评估AVG患者的生存概率提供参考。
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    图  1  SEER数据库AVG患者筛选流程图

    Figure  1.  The fl🎃ow diagram of how cases were selected fr🤪om the SEER database

    NOS: Not otherwise specified.

    图  2  训练集Kaplan-Meier分析显示不同预后因素对OS的影响

    Figure  2.  Kaplan-Meier analysis🐼 was conducted to determine the impact of variables on OS in the training cohort

    图  3  Kaplan-Meier分析不同治疗方案对AVG患者OS的影响

    Figure  3.  Kaplan-Meier analysis was conducted to determine the impact of treatment regimens 💖on the OS of AVG patien𒁃ts

    A: All patients; B: LGG patients; C: HGG patients. Radio: Radiotherapy.

    图  4  训练集Kaplan-Meier分析显示不同预后因素对CSS的影响

    Figure  4.  🦄 Kaplan-Meier analysis was conducted to determine the impact of variables on CS🔜S in the training cohort

    图  5  训练集中6个月、1年和2年OS率和CSS率的列线图预测模型

    Figure  5.  Nomogram prediction model🦂 of 6-month,𓃲 1-year, and 2-year OS rates and CSS rates in the training cohort

    A: Nomogram model of 6-month,1-year, and 2-year OS rates in the training cohort; B: Nomogram model of 6-month, 1-year, and 2-year CSS rates in the training cohort.

    图  6  训练集和验证集中6个月、1年和2年OS率的校准图

    Figure  6.  ᩚᩚᩚᩚᩚᩚ⁤⁤⁤⁤ᩚ⁤⁤⁤⁤ᩚ⁤⁤⁤⁤ᩚ𒀱ᩚᩚᩚ Calibration plots of 6-month, 1-year, and 2-year OS rate in the training cohort and validation cohort

    A-C: Calibration plots of 6-month, 1-year, and 2-year OS rates in the training cohort. D-F: Calibration plots of 6-month, 1-year, and 2-year OS rates in the validation cohort. The grey curve is the ideal curve, the blue curve is the actual curve, and the black line indicates the error margin.

    图  7  训练集和验证集中6个月、1年和2年CSS率的校准图

    Figure  7.  Calibration plots 🤡of 6-month, 1- year, and 2-year CSS rates✅ in the training cohort and validation cohort

    A-C: Calibration plots of 6-month, 1-year, and 2-year CSS rates in the training cohort; D-F: Calibration plots of 6-month, 1-year, and 2-year CSS rates in the validation cohort. The grey curve is the ideal curve, the blue curve is the actual curve, and the black line means the error margin.

    表  1  AVG患者的临床病理特征和治疗情况

    Table  1.   Summ🍌ary of clinicopathologic features and treatments of in patients with AVG

    VariableAll (n=369)Training cohort (n=246)Validation cohort (n=123)
    Age at diagnosis
     Mean/yr. 51.44±17.53 51.73±17.55 50.88±17.54
     Median/yr. 53 53 52
     18-<65 yr./case 272 182 90
     ≥65 yr./case 97 64 33
    Sex/case
     Male 218 147 71
     Female 151 99 52
    Race/case
     White 313 206 107
     Black 24 13 11
     Other 32 27 5
    Year at diagnosis
     1973–1999 143 105 38
     2000–2009 124 75 49
     2010–2016 102 66 36
    Marital status/case
     Single or divorced 124 79 45
     Married 233 158 75
     Unknown 12 9 3
    Insurance/case
     Insured 117 76 41
     Medicaid 22 13 9
     No/unknown 230 157 73
    WHO grade/case
     Ⅱ 66 44 22
     Ⅲ 73 51 22
     Ⅳ 230 151 79
    Tumor diameter/mm
     $ \bar x \pm s $ 39.93±16.03 39.90±16.56 40.00±15.10
     Median 40.00 40.00 40
    Surgery/case
     GTR 59 43 16
     PR/STR 145 90 55
     No 88 57 31
     Surgery, NOS 77 56 21
    Radiotherapy/case
     Yes 167 115 52
     No/unknown 202 131 71
    Chemotherapy/case
     Yes 143 96 47
     No/unknown 226 150 76
    OS/month
     Median 8.0 8.0 7.0
     GTR: Gross total resection; STR: Subtotal resection; PR: Partial resection; NOS: Not otherwise specified.
    下载: 导出CSV

    表  2  训练集中OS和CSS的多变量Cox回归分析(n=246)

    Table  2.   Multivariate Cox regression analysis of OS and CSS in the training cohort (n=246)

    VariableOSCSS
    HR95% CIPHR95% CIP
    Age at diagnosis
     18-<65 yr. 1 Ref 1 Ref
     ≥65 yr. 1.529 1.075-2.177 0.018 1.309 0.886-1.934 0.176
    WHO grade
     Ⅱ 1 Ref 1 Ref
     Ⅲ 5.166 2.339-11.412 <0.001 4.667 1.908-11.417 0.001
     Ⅳ 12.208 5.662-26.323 <0.001 13.339 5.725-31.077 <0.001
    Surgery
     No 1 Ref 1 Ref
     PR/STR 1.004 0.628-1.605 0.986 1.255 0.766-2.056 0.367
     GTR 1.021 0.577-1.805 0.944 1.176 0.634-2.181 0.607
    Radiotherapy
     No 1 Ref 1 Ref
     Yes 0.328 0.213-0.506 <0.001 0.295 0.189-0.461 <0.001
     HR: Hadds ratio; CI: Confidence interval; OS: Overall survival; CSS: Cancer-specific survival.
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-08-19
  • 修回日期:  2023-04-27
  • 刊出日期:  2023-07-22

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