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利用MRI影像组学模型可有效预测乳腺癌前哨淋巴结转移

李新华 卢振东 丁慧 张娜 伍康伟 陈庞富 罗文暄

李新华, 卢振东, 丁慧, 张娜, 伍康伟, 陈庞富, 罗文暄. 利用MRI影像组学模型可有效预测乳腺癌前哨淋巴结转移[J]. 分子影像学杂志, 2024, 47(1): 57-63. doi: 10.12122/j.issn.1674-4500.2024.01.11
引用本文: 李新华, 卢振东, 丁慧, 张娜, 伍康伟, 陈庞富, 罗文暄. 利用MRI影像组学模型可有效预测乳腺癌前哨淋巴结转移[J]. 分子影像学杂志, 2024, 47(1): 57-63. doi: 10.12122/j.issn.1674-4500.2024.01.11
LI Xinhua, LU Zhendong, DING Hui, ZHANG Na, WU Kangwei, CHEN Pangfu, LUO Wenxuan. Breast MRI-based imaging radiomic model can effectively predict sentinel lymph node metastasis in breast cancer prior to surgery[J]. Journal of Molecular Imaging, 2024, 47(1): 57-63. doi: 10.12122/j.issn.1674-4500.2024.01.11
Citation: LI Xinhua, LU Zhendong, DING Hui, ZHANG Na, WU Kangwei, CHEN Pangfu, LUO Wenxuan. Breast MRI-based imaging radiomic model can effectively predict sentinel lymph node metastasis in breast cancer prior to surgery[J]. Journal of Molecular Imaging, 2024, 47(1): 57-63. doi: 10.12122/j.issn.1674-4500.2024.01.11

利用MRI影像组学模型可有效预测乳腺癌前哨淋巴结转移

doi: 10.12122/j.issn.1674-4500.2024.01.11
基金项目: 

湛江市科技攻关计划项目 2022B01053

详细信息
    作者简介:

    李新华,硕士,主治医师,E-mail: 18718303516@163.com

    通讯作者:

    罗文暄,硕士,主治医师,E-mail: 378590588@qq.com

Breast MRI-based imaging radiomic model can effectively predict sentinel lymph node metastasis in breast cancer prior to surgery

  • 摘要:   目的   探索基于不同乳腺MRI序列联合临床病理因素的MRI影像组学模型预测乳腺癌前哨淋巴结转移的潜在价值。   方法   回顾性分析182例经病理确诊为乳腺癌伴前哨淋巴结转移状态患者,其中前哨淋巴结转移阳性组91例,前哨淋巴结转移阴性组91例,并按7:3的比例分训练组(阳性组64例、阴性组64例)和验证组(阳性组27例、阴性组27例)。对乳腺癌患者的临床、影像及病理资料进行单因素和多因素Logistic回归分析并筛选出与乳腺癌前哨淋巴结转移相关的独立风险因素;基于T2WI、弥散加权成像、动态对比增强提取最佳影像组学特征,分别构建多个单、多序列影像组学标签评分,并结合临床、病理及影像特征独立风险因素建立影像组学联合预测模型。绘制ROC曲线,计算曲线下面积,评价各模型预测乳腺癌前哨淋巴结转移的效能。   结果   瘤周水肿(P < 0.001)、肿瘤长径(P < 0.001)、肿瘤短径(P < 0.001)、病理分级(P < 0.001)、脉管侵犯(P < 0.001)、毛刺征(P= 0.006)、弥散加权成像边缘高信号征(P=0.028)及表观弥散系数值(P < 0.001)为乳腺癌前哨淋巴结转移的独立临床病理因素。在影像组学标签评分中,T2WI+弥散加权成像+动态对比增强联合序列的影像组学标签评分预测效能最佳,其验证组曲线下面积为0.744,进一步联合临床、病理及影像特征独立风险因素建立的影像组学联合预测模型的预测效能得到进一步提高,其验证组曲线下面积为0.834。   结论   基于乳腺MRI的影像组学模型在术前能够有效预测乳腺癌前哨淋巴结的转移。

     

  • 图  1  T2WI影像组学标签评分训练组及验证组ROC曲线图

    Figure  1.  ROC curve of T2WI radiomics label score in training group and verification group respectively. A: Training group; B: Validation group.

    图  2  DWI影像组学标签评分训练组及验证组ROC曲线图

    Figure  2.  ROC curve of DWI radiomics label score in training group and verification group respectively. A: Training group; B: Validation group.

    图  3  DCE影像组学标签评分训练组及验证组ROC曲线图

    Figure  3.  ROC curve of DCE radiomics label score in training group and verification group respectively. A: Training group; B: Validation group.

    图  4  T2WI+DWI+DCE影像组学标签评分训练组及验证组ROC曲线

    Figure  4.  ROC curve of T2WI + DWI + DCE radiomics label score in training group and verification group respectively. A: Training group; B: Validation group.

    图  5  影像组学联合预测模型训练组及验证组ROC曲线

    Figure  5.  ROC curve of combined radiomics prediction modelin training group and verification group respectively. A: Training group; B: Validation group.

    表  1  阳性与阴性组患者临床、病理及影像资料

    Table  1.   Clinical, pathological and imaging data of patients with positive and negative groups (n=91)

    Index Positive group Negative group t/χ2 P
    Age (years, Mean±SD) 46.3±9.9 48.7±11.2 1.536 0.063
    Gland type [n(%)] 0.895 0.827
        Few adenoidal type 36(39.6) 39(42.9)
        Multiple glandular type 35(38.5) 29(31.9)
        Dense glandular type 12(13.2) 14(15.4)
    TIC type [n(%)] 5.634 0.060
        Type Ⅰ 2(2.2) 3(3.3)
        Type Ⅱ 34(37.4) 49(53.8)
        Type Ⅲ 55(60.4) 39(42.9)
    DWI rim high signal sign [n(%)] 4.848 0.028
        + 30(33.0) 17(18.7)
        - 61(67.0) 74(81.3)
    MRS-Cho peak [n(%)] 0.969 0.325
        + 29(31.9) 15(25.9)
        - 62(68.1) 46(79.3)
    Burr sign [n(%)] 7.702 0.006
        + 42(46.2) 24(26.4)
        - 49(53.8) 67(74.7)
    Peritumoral enhancement sign [n(%)] 2.391 0.122
        + 27(29.7) 18(19.8)
        - 64(70.3) 73(80.2)
    Peritumoural edema sign [n(%)] 19.840 <0.001
        + 58(63.7) 28(30.8)
        - 33(36.3) 63(69.2)
    Edge enhancement sign [n(%)] 3.809 0.051
        + 26(28.6) 15(16.5)
        - 65(71.4) 76(83.5)
    Tumour long diameter[cm, M(P25, P75)] 2.60(2.00,3.50) 2.10(1.50,2.80) -3.551 <0.001
    Tumour short diameter[cm, M(P25, P75)] 1.70(1.30,2.20) 1.40(1.10,1.70) -3.608 <0.001
    Tumour long diameter/tumour short diameter [M(P25, P75)] 1.41(1.22,2.20) 1.41(1.23,1.61) -0.543 0.587
    ADC [×10-3 mm/s2, M(P25, P75)] 0.78(0.69,0.87) 0.83(0.74,0.91) -2.105 0.035
    Pathological grade [n(%)] 21.542 <0.001
        Degree Ⅰ 3(3.3) 17(18.7)
        Degree Ⅱ 42(46.2) 54(59.3)
        Degree Ⅲ 46(50.5) 20(22.0)
    HER-2 [n(%)] 2.768 0.096
        + 49(53.8) 60(65.9)
        - 42(46.2) 31(34.1)
    Ki-67 [M(P25, P75)] 0.30(0.15,0.50) 0.20(0.10,0.40) -2.093 0.036
    EGFR [n(%)] 0.139 0.710
        + 17(18.7) 19(20.9)
        - 74(81.3) 72(79.1)
    ER [M(P25, P75)] 80.0(30.0,90.0) 90.0(50.0,90.0) -1.205 0.228
    PR [M(P25, P75)] 24.0(0.00,60.0) 40.0(0.00,80.0) -1.365 0.172
    Vascular infiltration [n(%)] 69.333 <0.001
        + 70(76.9) 14(15.4)
        - 21(23.1) 77(84.6)
    +: Positive; -: Negative; TIC: Time-intensity curve; DWI:Diffusion weighted imaging; ADC:Apparent diffusion coefficient; MRS: Magnetic resonance spectroscopy; Cho:Choline; HER-2:Human epidermal growth factor receptor-2; Ki-67:Proliferating cell nuclear antigen-67; PR:Progesterone receptor; ER:Estrogen receptor; EGFR:Epidermal growth factor receptor.
    下载: 导出CSV

    表  2  多序列影像组学标签评分和影像组学联合预测模型的诊断效能

    Table  2.   Diagnostic efficiency of multi-sequence radiomics label score and combined radiomics prediction model

    Group Training group(n=127) Verification group(n=55)
    AUC 95% CI Sensitivity(%) Specitivity(%) AUC 95% CI Sensitivity(%) Specitivity(%)
    Multi-sequence radiomics label score 0.752 0.678-0.823 68.3 74.6 0.744 0.619-0.858 78.6 74.3
    Combined radiomics prediction model 0.906 0.861-0.945 85.7 84.1 0.834 0.727-0.925 75.0 85.7
    下载: 导出CSV
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  • 收稿日期:  2023-08-31
  • 网络出版日期:  2024-01-23
  • 刊出日期:  2024-01-20

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