Ultrasound-based radiomics to predict HER-2 status in breast cancer_Chinese Journal of Evidence-Based Medicine

Authors：

WANG Ying ¹ , CHEN Yingge ¹ , YE Sumin ¹ , CHEN Dong ² , WU Lei ³ , LIU Zaiyi ³ ,  LIU Min ⁴

1. Department of Ultrasound, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou 510120, P.R.China;
2. Department of Ultrasound, the Third Affiliated Hospital of Kunming Medical University/Yunnan Cancer Hospital, Kunming 650118, P.R.China;
3. Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, P.R.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, P.R.China;

Corresponding author：

LIU Min, Email: liumin@sysucc.org.cn

Keywords：

Ultrasound; Radiomics; Breast cancer; HER-2; Decision curve

DOI：

10.7507/1672-2531.202010022

Video：

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Objective To explore the value of a radiomics model based on ultrasound imaging in predicting the HER-2 status of breast cancer prior to surgery.Methods A total of 230 patients with invasive breast cancer were retrospectively analyzed, all the patients underwent preoperative breast ultrasound examination. According to the order of examination time, the patients were categorized into training group (n=115) and validation group (n=115). Image J software was used to manually delineate the lesion area in the ultrasound image along the tumor boundary. Pyradiomics was used to extract 1 820 features from each lesion area, and three statistical methods were used to screen features. A logistic regression model was used to construct ultrasound imaging radiomics model. The receive operating characteristic curve (ROC), calibration curve and decision curve were used to evaluate the performance and value of ultrasound imaging radiomics model in predicting HER-2 status.Results Nine key image features were identified to construct ultrasound imaging radiomics model. The area of under the ROC curve of the model in the training group and the validation group were 0.82 (95%CI 0.74 to 0.90) and 0.81 (95%CI 0.72 to 0.89), respectively. The calibration curve showed that the model had a good calibration in both the training and validation groups.Conclusions Ultrasound-based imaging radiomics model is of significant value in predicting the HER-2 status of breast cancer prior to surgery.

Citation： WANG Ying, CHEN Yingge, YE Sumin, CHEN Dong, WU Lei, LIU Zaiyi, LIU Min. Ultrasound-based radiomics to predict HER-2 status in breast cancer. Chinese Journal of Evidence-Based Medicine, 2021, 21(3): 271-275. doi: 10.7507/1672-2531.202010022 Copy

1.	江泽飞, 邵志敏, 徐兵河. 人表皮生长因子受体 2 阳性乳腺癌临床诊疗专家共识 2016. 中华医学杂志, 2016, 96(14): 1091-1096.
2.	薛珂, 李卓琳, 李振辉, 等. 多参数 MRI 影像组学特征识别 HER-2 过表达型乳腺癌. 放射学实践, 2020, 35(2): 186-189.
3.	梁翠珊, 崔运能, 杨伟超, 等. 基于 T2WI 影像组学标签预测乳腺癌人表皮生长因子受体 2 表达状态. 中国医学影像技术, 2019, 35(4): 555-559.
4.	吴佩琪, 赵可, 吴磊, 等. 基于扩散加权成像和动态增强 MRI 的影像组学特征与乳腺癌分子分型的关系初探. 中华放射学杂志, 2018, 52(5): 338-343.
5.	Crowe JP, Patrick RJ, Rybicki LA, et al. A data model to predict HER2 status in breast cancer based on the clinical and pathologic profiles of a large patient population at a single institution. Breast, 2006, 15(6): 728-735.
6.	Grimm LJ, Zhang J, Mazurowski MA. Computational approach to radiogenomics of breast cancer: Luminal A and luminal B molecular subtypes are associated with imaging features on routine breast MRI extracted using computer vision algorithms. J Magn Reson Imaging, 2015, 42(4): 902-907.
7.	Li H, Zhu Y, Burnside ES, et al. Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set. NPJ Breast Cancer, 2016, 2: 16012.
8.	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 Clin Oncol, 2016, 34(18): 2157-2164.
9.	Cui H, Zhang D, Peng F, et al. Identifying ultrasound features of positive expression of Ki67 and P53 in breast cancer using radiomics. Asia Pac J Clin Oncol, 2020, doi:10.1111/ajco.13397.
10.	Tran WT, Jerzak K, Lu FI, et al. Personalized breast cancer treatments using artificial intelligence in radiomics and pathomics. J Med Imaging Radiat Sci, 2019, 50(4 Suppl 2): S32-S41.
11.	Liu Z, Wang S, Dong D, et al. The applications of radiomics in precision diagnosis and treatment of oncology: opportunities and challenges. Theranostics, 2019, 9(5): 1303-1322.
12.	Liu Z, Li Z, Qu J, et al. Radiomics of multiparametric MRI for pretreatment prediction of pathologic complete response to neoadjuvant chemotherapy in breast cancer: a multicenter study. Clin Cancer Res, 2019, 25(12): 3538-3547.
13.	Liang M, Cai Z, Zhang H, et al. Machine learning-based analysis of rectal cancer mri radiomics for prediction of metachronous liver metastasis. Acad Radiol, 2019, 26(11): 1495-1504.
14.	Kim S, Shin J, Kim DY, et al. Radiomics on gadoxetic acid-enhanced magnetic resonance imaging for prediction of postoperative early and late recurrence of single hepatocellular carcinoma. Clin Cancer Res, 2019, 25(13): 3847-3855.
15.	Ji GW, Zhang YD, Zhang H, et al. Biliary tract cancer at CT: a radiomics-based model to predict lymph node metastasis and survival outcomes. Radiology, 2019, 290(1): 90-98.
16.	Zheng X, Yao Z, Huang Y, et al. Deep learning radiomics can predict axillary lymph node status in early-stage breast cancer. Nat Commun, 2020, 11(1): 1236.
17.	Gao Y, Luo Y, Zhao C, et al. Nomogram based on radiomics analysis of primary breast cancer ultrasound images: prediction of axillary lymph node tumor burden in patients. Eur Radiol, 2021, 31(2): 928-937.
18.	DiCenzo D, Quiaoit K, Fatima K, et al. Quantitative ultrasound radiomics in predicting response to neoadjuvant chemotherapy in patients with locally advanced breast cancer: results from multi-institutional study. Cancer Med, 2020, 9(16): 5798-5806.
19.	Moustafa AF, Cary TW, Sultan LR, et al. Color doppler ultrasound improves machine learning diagnosis of breast cancer. Diagnostics (Basel), 2020, 10(9): 631.
20.	Qiu X, Jiang Y, Zhao Q, et al. Could ultrasound-based radiomics noninvasively predict axillary lymph node metastasis in breast cancer? J Ultrasound Med, 2020, 39(10): 1897-1905.
21.	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. PLoS One, 2020, 15(7): e0236182.
22.	Sim Y, Lee SE, Kim EK, et al. A radiomics approach for the classification of fibroepithelial lesions on breast ultrasonography. Ultrasound Med Biol, 2020, 46(5): 1133-1141.
23.	Sun Q, Lin X, Zhao Y, et al. Deep learning vs. radiomics for predicting axillary lymph node metastasis of breast cancer using ultrasound images: don't forget the peritumoral region. Front Oncol, 2020, 10: 53.
24.	邱小英, 蒋天安. 基于超声的影像组学特征能否无创地预测乳腺癌中 HER2 的状态? 北京: 中国超声医学工程学会第五届全国介入超声医学学术交流大会, 2019: 1.
25.	Fan M, Li H, Wang S, et al. Radiomic analysis reveals DCE-MRI features for prediction of molecular subtypes of breast cancer. PLoS One, 2017, 12(2): e0171683.

1. 江泽飞, 邵志敏, 徐兵河. 人表皮生长因子受体 2 阳性乳腺癌临床诊疗专家共识 2016. 中华医学杂志, 2016, 96(14): 1091-1096.
2. 薛珂, 李卓琳, 李振辉, 等. 多参数 MRI 影像组学特征识别 HER-2 过表达型乳腺癌. 放射学实践, 2020, 35(2): 186-189.
3. 梁翠珊, 崔运能, 杨伟超, 等. 基于 T2WI 影像组学标签预测乳腺癌人表皮生长因子受体 2 表达状态. 中国医学影像技术, 2019, 35(4): 555-559.
4. 吴佩琪, 赵可, 吴磊, 等. 基于扩散加权成像和动态增强 MRI 的影像组学特征与乳腺癌分子分型的关系初探. 中华放射学杂志, 2018, 52(5): 338-343.
5. Crowe JP, Patrick RJ, Rybicki LA, et al. A data model to predict HER2 status in breast cancer based on the clinical and pathologic profiles of a large patient population at a single institution. Breast, 2006, 15(6): 728-735.
6. Grimm LJ, Zhang J, Mazurowski MA. Computational approach to radiogenomics of breast cancer: Luminal A and luminal B molecular subtypes are associated with imaging features on routine breast MRI extracted using computer vision algorithms. J Magn Reson Imaging, 2015, 42(4): 902-907.
7. Li H, Zhu Y, Burnside ES, et al. Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set. NPJ Breast Cancer, 2016, 2: 16012.
8. 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 Clin Oncol, 2016, 34(18): 2157-2164.
9. Cui H, Zhang D, Peng F, et al. Identifying ultrasound features of positive expression of Ki67 and P53 in breast cancer using radiomics. Asia Pac J Clin Oncol, 2020, doi:10.1111/ajco.13397.
10. Tran WT, Jerzak K, Lu FI, et al. Personalized breast cancer treatments using artificial intelligence in radiomics and pathomics. J Med Imaging Radiat Sci, 2019, 50(4 Suppl 2): S32-S41.
11. Liu Z, Wang S, Dong D, et al. The applications of radiomics in precision diagnosis and treatment of oncology: opportunities and challenges. Theranostics, 2019, 9(5): 1303-1322.
12. Liu Z, Li Z, Qu J, et al. Radiomics of multiparametric MRI for pretreatment prediction of pathologic complete response to neoadjuvant chemotherapy in breast cancer: a multicenter study. Clin Cancer Res, 2019, 25(12): 3538-3547.
13. Liang M, Cai Z, Zhang H, et al. Machine learning-based analysis of rectal cancer mri radiomics for prediction of metachronous liver metastasis. Acad Radiol, 2019, 26(11): 1495-1504.
14. Kim S, Shin J, Kim DY, et al. Radiomics on gadoxetic acid-enhanced magnetic resonance imaging for prediction of postoperative early and late recurrence of single hepatocellular carcinoma. Clin Cancer Res, 2019, 25(13): 3847-3855.
15. Ji GW, Zhang YD, Zhang H, et al. Biliary tract cancer at CT: a radiomics-based model to predict lymph node metastasis and survival outcomes. Radiology, 2019, 290(1): 90-98.
16. Zheng X, Yao Z, Huang Y, et al. Deep learning radiomics can predict axillary lymph node status in early-stage breast cancer. Nat Commun, 2020, 11(1): 1236.
17. Gao Y, Luo Y, Zhao C, et al. Nomogram based on radiomics analysis of primary breast cancer ultrasound images: prediction of axillary lymph node tumor burden in patients. Eur Radiol, 2021, 31(2): 928-937.
18. DiCenzo D, Quiaoit K, Fatima K, et al. Quantitative ultrasound radiomics in predicting response to neoadjuvant chemotherapy in patients with locally advanced breast cancer: results from multi-institutional study. Cancer Med, 2020, 9(16): 5798-5806.
19. Moustafa AF, Cary TW, Sultan LR, et al. Color doppler ultrasound improves machine learning diagnosis of breast cancer. Diagnostics (Basel), 2020, 10(9): 631.
20. Qiu X, Jiang Y, Zhao Q, et al. Could ultrasound-based radiomics noninvasively predict axillary lymph node metastasis in breast cancer? J Ultrasound Med, 2020, 39(10): 1897-1905.
21. 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. PLoS One, 2020, 15(7): e0236182.
22. Sim Y, Lee SE, Kim EK, et al. A radiomics approach for the classification of fibroepithelial lesions on breast ultrasonography. Ultrasound Med Biol, 2020, 46(5): 1133-1141.
23. Sun Q, Lin X, Zhao Y, et al. Deep learning vs. radiomics for predicting axillary lymph node metastasis of breast cancer using ultrasound images: don't forget the peritumoral region. Front Oncol, 2020, 10: 53.
24. 邱小英, 蒋天安. 基于超声的影像组学特征能否无创地预测乳腺癌中 HER2 的状态? 北京: 中国超声医学工程学会第五届全国介入超声医学学术交流大会, 2019: 1.
25. Fan M, Li H, Wang S, et al. Radiomic analysis reveals DCE-MRI features for prediction of molecular subtypes of breast cancer. PLoS One, 2017, 12(2): e0171683.

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