切换至 "中华医学电子期刊资源库"

中华医学超声杂志(电子版) ›› 2022, Vol. 19 ›› Issue (08) : 774 -778. doi: 10.3877/cma.j.issn.1672-6448.2022.08.008

浅表器官超声影像学

超声影像组学标签对乳腺癌腋窝淋巴结转移的预测价值
王瑛1, 陈英格1, 叶素敏1, 陈东2, 刘宇3, 刘再毅3, 刘敏4,()   
  1. 1. 510120 广州医科大学附属第一医院超声科
    2. 650118 昆明医科大学第三附属医院(云南省肿瘤医院)超声科
    3. 510080 广州,广东省人民医院放射科
    4. 510060 广州,华南肿瘤学国家重点实验室 肿瘤医学省部共建协同创新中心 中山大学肿瘤防治中心(中山大学附属肿瘤医院)超声科
  • 收稿日期:2021-02-23 出版日期:2022-08-01
  • 通信作者: 刘敏
  • 基金资助:
    国家重点研发计划资助(2017YFC1309100)

Ultrasound-based radiomics to predict axillary lymph node metastasis in breast cancer

Ying Wang1, Yingge Chen1, Sumin Ye1, Dong Chen2, Yu Liu3, Zaiyi Liu3, Min Liu4,()   

  1. 1. Department of Ultrasound, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou 510120, China
    2. Department of Ultrasound, the Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming 650118, China
    3. Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China
    4. Department of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China
  • Received:2021-02-23 Published:2022-08-01
  • Corresponding author: Min Liu
引用本文:

王瑛, 陈英格, 叶素敏, 陈东, 刘宇, 刘再毅, 刘敏. 超声影像组学标签对乳腺癌腋窝淋巴结转移的预测价值[J/OL]. 中华医学超声杂志(电子版), 2022, 19(08): 774-778.

Ying Wang, Yingge Chen, Sumin Ye, Dong Chen, Yu Liu, Zaiyi Liu, Min Liu. Ultrasound-based radiomics to predict axillary lymph node metastasis in breast cancer[J/OL]. Chinese Journal of Medical Ultrasound (Electronic Edition), 2022, 19(08): 774-778.

目的

探讨基于常规超声的影像组学特征预测乳腺癌腋窝淋巴结转移的应用价值。

方法

回顾性收集2020年1月至2020年10月于中山大学肿瘤防治中心就诊经手术病理确诊的265例乳腺癌患者的临床资料和术前超声图像,按超声检查时间顺序,将患者分为训练集(159例)和验证集(106例)。应用ImageJ软件手动勾画病灶区域,使用Pyradiomics从每个病灶区域中提取影像组学特征,采用多种方法逐步筛选特征,应用Logistic回归构建预测乳腺癌腋窝淋巴结转移的超声影像组学标签。在训练集和验证集上采用ROC曲线、校准曲线和决策曲线评估超声影像组学标签预测乳腺癌腋窝淋巴结转移的效能。

结果

最终筛选出8个关键超声影像组学特征用于构建超声影像组学标签。该标签在训练集和验证集中预测乳腺癌腋窝淋巴结转移的ROC曲线下面积分别为0.805(95%CI:0.734~0.876)、0.793(95%CI:0.706~0.880)。在校准曲线中,该标签在训练集和验证集均表现出较好的校准度(P=0.592、0.593),决策曲线分析进一步表明了该标签具有一定的临床实用性。

结论

基于超声的影像组学标签在预测乳腺癌腋窝淋巴结转移方面具有一定价值,可为治疗前乳腺癌的准确分期以及治疗方案的合理选择提供参考依据。

Objective

To explore the value of a radiomics model based on ultrasound imaging in predicting the axillary lymph node status of patients with breast cancer.

Methods

A total of 265 patients with early-stage breast cancer were retrospectively analyzed, all of whom underwent preoperative breast ultrasound examination at Sun Yat-sen University Cancer Center from January 2020 to October 2020. According to the order of examination time, the patients were divided into a training group (n=159) and a validation group (n=106). ImageJ software was used to manually delineate the lesion area in the ultrasound image along the tumor boundary. Pyradiomics was used to extract 1130 features from each lesion area, and three statistical methods were used to screen the features. Finally, a logistic regression model was used to construct ultrasound imaging radiomics model. The receive operating characteristic (ROC) curve, calibration curve, and decision curve were used to evaluate the performance and value of the ultrasound imaging radiomics model in predicting axillary lymph node status.

Results

A total of eight key image features were selected to construct the ultrasound imaging radiomics model. The area under the ROC curve values of the model in the training group and the validation group were 0.805 (95% confidence interval [CI]: 0.734-0.876) and 0.793 (95% CI: 0.706-0.880), respectively. The calibration curve showed that the model had a good calibration in both the training and validation groups (P=0.592、0.593); besides, the decision curve analysis confirmed that the model had some clinical practicability.

Conclusion

Ultrasound-based imaging radiomics model is of great value in predicting the axillary lymph node status of patients with breast cancer before surgery, which could guide clinicians in the accurate staging of breast cancer and selection of appropriate therapeutic regimen.

图1 乳腺癌腋窝淋巴结转移患者病灶分割图像。图a为常规超声图像示乳腺原发病灶;图b为应用ImageJ软件描记肿瘤边界进行病灶分割
表1 筛选出的8个关键影像组学特征
图2 超声影像组学模型预测乳腺癌腋窝淋巴结转移的ROC曲线 注:AUC为曲线下面积
表2 超声影像组学模型的预测效能
图3 超声影像组学模型对乳腺癌腋窝淋巴结转移预测概率的校准曲线
图4 超声影像组学模型预测乳腺癌腋窝淋巴结转移的决策分析曲线(在训练集和验证集中,在极大的阈值区间范围内,净获益均较2条极端曲线高) 注:图中2条极端曲线表示不干预任何患者(粗虚线),净获益为0;干预所有患者(细虚线),净获益为斜率为负值的反斜线
1
Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries [J]. CA Cancer J Clin, 2021, 71(3): 209-249.
2
Ehteshami Bejnordi B, Veta M, Johannes van Diest P, et al. Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer [J]. JAMA, 2017, 318(22): 2199-2210.
3
Zheng X, Yao Z, Huang Y, et al. Deep learning radiomics can predict axillary lymph node status in early-stage breast cancer [J]. Nat Commun, 2020, 11(1): 1236.
4
Huang YQ, Liang CH, He L, et al. Development and validation of a radiomics nomogram for preoperative prediction of lymph node metastasis in colorectal cancer [J]. J Clin Oncol, 2016, 34(18): 2157-2164.
5
Dong D, Fang MJ, Tang L, et al. Deep learning radiomic nomogram can predict the number of lymph node metastasis in locally advanced gastric cancer: an international multicenter study [J]. Ann Oncol, 2020, 31(7): 912-920.
6
Fujioka T, Mori M, Kubota K, et al. The utility of deep learning in breast ultrasonic imaging: a review [J]. Diagnostics (Basel), 2020, 10(12): 1055.
7
Quiaoit K, DiCenzo D, Fatima K, et al. Quantitative ultrasound radiomics for therapy response monitoring in patients with locally advanced breast cancer: Multi-institutional study results [J]. PLoS One, 2020, 15(7): e0236182.
8
Mao B, Ma J, Duan S, et al. Preoperative classification of primary and metastatic liver cancer via machine learning-based ultrasound radiomics [J]. Eur Radiol, 2021, 31(7): 4576-4586.
9
Yu J, Deng Y, Liu T, et al. Lymph node metastasis prediction of papillary thyroid carcinoma based on transfer learning radiomics [J]. Nat Commun, 2020, 11(1): 4807.
10
金华, 罗伟权, 纪宗萍, 等. 乳腺癌超声影像组学图像特征Logistic回归方程预测腋窝淋巴结转移风险 [J]. 中国超声医学杂志, 2021, 37(2): 139-142.
11
Zhou LQ, Wu XL, Huang SY, et al. Lymph node metastasis prediction from primary breast cancer US images using deep learning [J]. Radiology, 2020, 294(1): 19-28.
12
谭红娜, 武明辉, 周晶, 等. 乳腺X线影像组学方法预测乳腺癌腋窝淋巴结转移的价值 [J]. 中华放射学杂志, 2020, 9(54): 859-863.
[1] 章建全, 程杰, 陈红琼, 闫磊. 采用ACR-TIRADS评估甲状腺消融区的调查研究[J/OL]. 中华医学超声杂志(电子版), 2024, 21(10): 966-971.
[2] 罗辉, 方晔. 品管圈在提高甲状腺结节细针穿刺检出率中的应用[J/OL]. 中华医学超声杂志(电子版), 2024, 21(10): 972-977.
[3] 杨忠, 时敬业, 邓学东, 姜纬, 殷林亮, 潘琦, 梁泓, 马建芳, 王珍奇, 张俊, 董姗姗. 产前超声在胎儿22q11.2 微缺失综合征中的应用价值[J/OL]. 中华医学超声杂志(电子版), 2024, 21(09): 852-858.
[4] 孙佳丽, 金琳, 沈崔琴, 陈晴晴, 林艳萍, 李朝军, 徐栋. 机器人辅助超声引导下经皮穿刺的体外实验研究[J/OL]. 中华医学超声杂志(电子版), 2024, 21(09): 884-889.
[5] 史学兵, 谢迎东, 谢霓, 徐超丽, 杨斌, 孙帼. 声辐射力弹性成像对不可切除肝细胞癌门静脉癌栓患者放射治疗效果的评价[J/OL]. 中华医学超声杂志(电子版), 2024, 21(08): 778-784.
[6] 奚玲, 仝瀚文, 缪骥, 毛永欢, 沈晓菲, 杜峻峰, 刘晔. 基于肌少症构建的造口旁疝危险因素预测模型[J/OL]. 中华普外科手术学杂志(电子版), 2025, 19(01): 48-51.
[7] 高杰红, 黎平平, 齐婧, 代引海. ETFA和CD34在乳腺癌中的表达及与临床病理参数和预后的关系研究[J/OL]. 中华普外科手术学杂志(电子版), 2025, 19(01): 64-67.
[8] 韩萌萌, 冯雪园, 马宁. 乳腺癌改良根治术后桡神经损伤1例[J/OL]. 中华普外科手术学杂志(电子版), 2025, 19(01): 117-118.
[9] 张志兆, 王睿, 郜苹苹, 王成方, 王成, 齐晓伟. DNMT3B与乳腺癌预后的关系及其生物学机制[J/OL]. 中华普外科手术学杂志(电子版), 2024, 18(06): 624-629.
[10] 王玲艳, 高春晖, 冯雪园, 崔鑫淼, 刘欢, 赵文明, 张金库. 循环肿瘤细胞在乳腺癌新辅助及术后辅助治疗中的应用[J/OL]. 中华普外科手术学杂志(电子版), 2024, 18(06): 630-633.
[11] 赵林娟, 吕婕, 王文胜, 马德茂, 侯涛. 超声引导下染色剂标记切缘的梭柱型和圆柱型保乳区段切除术的效果研究[J/OL]. 中华普外科手术学杂志(电子版), 2024, 18(06): 634-637.
[12] 张琛, 秦鸣, 董娟, 陈玉龙. 超声检查对儿童肠扭转缺血性改变的诊断价值[J/OL]. 中华消化病与影像杂志(电子版), 2024, 14(06): 565-568.
[13] 韦巧玲, 黄妍, 赵昌, 宋庆峰, 陈祖毅, 黄莹, 蒙嫦, 黄靖. 肝癌微波消融术后中重度疼痛风险预测列线图模型构建及验证[J/OL]. 中华临床医师杂志(电子版), 2024, 18(08): 715-721.
[14] 蔡晓雯, 李慧景, 丘婕, 杨翼帆, 吴素贤, 林玉彤, 何秋娜. 肝癌患者肝动脉化疗栓塞术后疼痛风险预测模型的构建及验证[J/OL]. 中华临床医师杂志(电子版), 2024, 18(08): 722-728.
[15] 孙铭远, 褚恒, 徐海滨, 张哲. 人工智能应用于多发性肺结节诊断的研究进展[J/OL]. 中华临床医师杂志(电子版), 2024, 18(08): 785-790.
阅读次数
全文


摘要