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中华乳腺病杂志(电子版)

中华乳腺病杂志(电子版) ›› 2023, Vol. 17 ›› Issue (04) : 229 -237. doi: 10.3877/cma.j.issn.1674-0807.2023.04.005

论著

基于影像学表现和临床病理特征预测良性与交界性乳腺叶状肿瘤复发的列线图模型
叶艳娜, 叶瑞婷, 陈艳玲, 彭雯, 刘乐, 肖文秋, 黄辉, 李明深, 钟慕仪†(), 叶娴   
  1. 523000　东莞职业技术学院卫生健康学院
    523059　东莞市人民医院/南方医科大学第十附属医院乳腺外科
  • 收稿日期:2023-01-09 出版日期:2023-08-01
  • 通信作者: 钟慕仪

Nomogram based on clinicopathological and imaging features to predict recurrence of benign and borderline breast phyllodes tumor

Yanna Ye, Ruiting Ye, Yanling Chen, Wen Peng, Le Liu, Wenqiu Xiao, Hui Huang, Mingshen Li, Muyi Zhong†(), Xian Ye   

  1. School of Health, Dongguan Vocational and Technical College, Dongguan 523000, China
    Department of Breast Surgery, Dongguan People’s Hospital/Tenth Affiliated Hospital of Southern Medical University, Dongguan 523059, China
  • Received:2023-01-09 Published:2023-08-01
  • Corresponding author: Muyi Zhong
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引用本文：

叶艳娜, 叶瑞婷, 陈艳玲, 彭雯, 刘乐, 肖文秋, 黄辉, 李明深, 钟慕仪, 叶娴. 基于影像学表现和临床病理特征预测良性与交界性乳腺叶状肿瘤复发的列线图模型[J/OL]. 中华乳腺病杂志(电子版), 2023, 17(04): 229-237.

Yanna Ye, Ruiting Ye, Yanling Chen, Wen Peng, Le Liu, Wenqiu Xiao, Hui Huang, Mingshen Li, Muyi Zhong, Xian Ye. Nomogram based on clinicopathological and imaging features to predict recurrence of benign and borderline breast phyllodes tumor[J/OL]. Chinese Journal of Breast Disease(Electronic Edition), 2023, 17(04): 229-237.

目的

本研究探讨良性与交界性乳腺叶状肿瘤(PT)复发的潜在因素，构建列线图以预测PT复发率。

方法

回顾性分析2016年6月至2019年12月在东莞市人民医院就诊的65例良性与交界性PT患者的临床病理及影像学资料，采取单因素及多因素Logistic回归分析其复发的独立危险因素，构建列线图。绘制受试者操作特征(ROC)曲线，计算曲线下面积(AUC)；通过bootstrap建立校准曲线评估校准性能；通过决策曲线分析(DCA)证明该预测模型的临床实用性。

结果

单因素Logistic回归分析显示发现与治疗间隔时间(TIMDT)>6个月、MRI强化方式不均匀、超声表现(肿瘤形态不规则、边缘不光整、分叶征、内回声不均匀、点状强回声、血流信号中等/丰富和囊性结构)与PT复发有关(P均<0.050)。多因素Logistic回归分析显示TIMDT>6个月(OR＝32.230，95%CI：2.343~443.367，P＝0.009)、MRI强化方式不均匀(OR＝16.786，95%CI：1.030~273.431，P＝0.048)及超声肿瘤分叶征(OR＝14.861，95%CI：1.155~191.205，P＝0.038)是PT复发的独立危险因素。根据这3个危险因素构建列线图，ROC曲线的AUC为0.906(95%CI：0.811~1.000)，敏感度为83.3%，特异度为88.7%。校准曲线接近于理想曲线，拟合度较高。DCA曲线表明高风险阈值处于0.04~0.96时，使用该列线图预测PT复发的净获益率高。

结论

TIMDT>6个月、MRI强化方式不均匀、具有超声肿瘤分叶征的良性与交界性乳腺PT患者复发风险较高。基于这3个因素开发的列线图预测PT复发能力较强，临床应用价值高。

关键词: 乳腺肿瘤, 磁共振成像, 超声检查, 复发, 列线图, 叶状肿瘤
Objective

To explore the potential factors related to the recurrence of benign and borderline breast phyllodes tumor (PT) and establish a nomogram to predict the recurrence rate of PT.

Methods

The clinicopathological and imaging data of 65 patients with benign and borderline PT who were treated in Dongguan People’s Hospital from June 2016 to December 2019 were retrospectively analyzed. The univariate and multivariate logistic regression were used to analyze the independent risk factors for recurrence, and a nomogram was constructed accordingly. The receiver operating characteristics (ROC) curve was drawn and the area under the curve (AUC) was calculated. A calibration curve was established by bootstrap method to evaluate calibration performance. The clinical utility of this predictive model was demonstrated by decision curve analysis (DCA).

Results

Univariate logistic regression analysis showed that the time interval between mass discovery and treatment (TIMDT)>6 months, uneven MRI enhancement pattern, ultrasonic findings(irregular shape, uneven edge, tumor lobulation, uneven internal echo, punctate strong echo, moderate/abundant blood flow signal and cystic structure) were related to PT recurrence (all P<0.050). Multivariate logistic regression analysis showed that TIMDT>6 months (OR=32.230, 95%CI: 2.343-443.367, P=0.009), uneven MRI enhancement pattern (OR=16.786, 95%CI: 1.030-273.431, P=0.048) and ultrasonic tumor lobulation (OR=14.861, 95%CI: 1.155-191.205, P=0.038) were independent risk factors for PT recurrence. A nomogram was constructed based on these three independent risk factors. The AUC of ROC was 0.906 (95%CI: 0.811-1.000), the sensitivity was 83.3% and the specificity was 88.7%. The calibration curve of the model showed a good calibration efficiency. The DCA curve displayed high clinical net benefit from predicting PT recurrence at a threshold of 0.04-0.96.

Conclusion

The benign and borderline breast PT patients with the following features (TIMDT>6 months, uneven MRI enhancement pattern, and ultrasonic tumor lobulation) are in high risk of recurrence. The nomogram based on these three factors shows a strong ability to predict the recurrence of PT, indicating high potential in clinical application.

Key words: Breast neoplasms, Magnetic resonance imaging, Ultrasonography, Recurrence, Nomogram, Phyllodes tumor
表1 65例乳腺叶状肿瘤患者临床病理特征与肿瘤复发单因素Logistic回归分析结果(例)
临床病理特征 无复发组(n＝53) 复发组(n＝12) OR值 95%CI P值
年龄(岁) 38.7±11.1 43.0±9.3 1.096 0.758~1.585 0.627
绝经情况          
未绝经 46 10      
已绝经 7 2 1.371 0.247~7.604 0.718
纤维腺瘤病史          
无 46 12      
有 7 0     0.999
TIMDT          
≤6个月 37 2      
>6个月 16 10 11.560 2.270~58.870 0.003
肿瘤位置          
右 23 7      
左 30 5 0.548 0.154~1.949 0.353
手术方式          
单纯肿块切除术 5 2      
肿块扩大切除术 48 5 0.260 0.040~1.708 0.161
乳房切除术 0 5     0.999
肿瘤类型          
良性 38 8      
交界性 15 4 1.267 0.331~4.841 0.730
最大直径          
≤3 cm 23 0      
>3 cm 30 12     0.998
合并纤维腺瘤          
无 29 9      
有 24 3 0.403 0.098~1.657 0.208
合并乳腺增生          
无 32 10      
有 21 2 0.305 0.061~1.532 0.149
合并IDP          
无 49 12      
有 4 0     0.999
表2 65例乳腺叶状肿瘤患者MRI特征与肿瘤复发单因素Logistic回归分析结果(例)
MRI特征 无复发组(n＝53) 复发组(n＝12) OR值 95%CI P值
肿瘤边缘          
清晰 48 10      
不清晰 5 2 1.920 0.325~11.337 0.472
分叶征          
无 23 2      
有 30 10 3.833 0.764~19.224 0.102
多结节融合征          
无 49 12      
有 4 0     0.999
T2WI信号          
等/等高 8 1      
稍高 45 11 1.956 0.221~17.315 0.547
T1WI信号          
等/等高 23 5      
低 18 5 1.278 0.320~5.102 0.729
混杂高 12 2 0.767 0.129~4.556 0.770
强化方式          
均匀 34 1      
不均匀 19 11 19.680 2.360~164.440 0.006
TIC          
流入型(Ⅰ型) 39 9      
平台型(Ⅱ型) 14 3 0.929 0.220~3.928 0.920
DWI信号          
等高 5 1      
高 48 11 1.146 0.121~10.815 0.905
ADC信号          
低 20 2      
混杂高 33 10 3.030 0.602~15.260 0.179
表3 65例乳腺叶状肿瘤患者超声成像特征与肿瘤复发单因素Logistic回归分析结果(例)
超声特征 无复发组(n＝53) 复发组(n＝12) OR值 95%CI P值
肿瘤数量 1.51±0.85 1.33±0.65 0.675 －0.345~0.697 0.502
肿瘤形态          
规则 39 4      
不规则 14 8 5.571 1.149~21.419 0.012
肿瘤边缘          
光整 44 6      
不光整 9 6 4.889 1.281~18.664 0.020
分叶征          
无 46 7      
有 7 5 4.694 1.162~18.962 0.030
肿瘤内回声          
均匀 30 2      
不均匀 23 10 6.522 1.300~32.707 0.023
后方回声          
无变化 16 4      
衰减 3 2 2.667 0.327~21.733 0.360
增强 34 6 0.706 0.174~2.856 0.625
点状强回声          
无 45 6      
有 8 6 5.625 1.446~21.887 0.013
血流信号          
无/少量 41 5      
中等/丰富 12 7 4.783 1.283~17.828 0.020
囊性结构          
无 49 8      
有 4 4 6.125 1.268~29.576 0.024
表4 65例乳腺叶状肿瘤患者复发多因素Logistic回归分析结果
特征 B值 标准误 Wald值 P值 OR值 95%CI
发现与治疗间隔时间>6个月 3.473 1.338 6.742 0.009 32.230 2.343~443.367
MRI强化方式不均匀 2.821 1.424 3.925 0.048 16.786 1.030~273.431
超声肿瘤分叶征 2.699 1.303 4.287 0.038 14.861 1.155~191.205
图1 65例乳腺叶状肿瘤患者的复发预测模型列线图
图2 65例乳腺叶状肿瘤患者复发预测模型的受试者操作特征曲线注：曲线下面积为0.906
图3 65例乳腺叶状肿瘤患者复发预测模型的校准曲线
图4 65例乳腺叶状肿瘤患者复发预测模型的决策曲线分析
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