Research progress of application in neoadjuvant therapy for breast cancer based on AI and radiomics_CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY

Authors：

ZHANG Jingwen ,  ZHANG Yimin ,  SUN Shengrong

Department of Breast and Thyroid Surgery, Renmin Hospital of Wuhan University, Wuhan 430060, P. R. China;

Corresponding author：

ZHANG Yimin, Email: dryiminzhang@163.com; SUN Shengrong, Email: sun137@sina.com

Keywords：

breast cancer; neoadjuvant therapy; pathological complete response; aritifical intelligence; radiomics

DOI：

10.7507/1007-9424.202401040

Video：

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Objective To summarize the current research progress in the prediction of the efficacy of neoadjuvant therapy of breast cancer based on the application of artificial intelligence (AI) and radiomics. Method The researches on the application of AI and radiomics in neoadjuvant therapy of breast cancer in recent 5 years at home and abroad were searched in CNKI, Google Scholar, Wanfang database and PubMed database, and the related research progress was reviewed. Results AI had developed rapidly in the field of medical imaging, and molybdenum target, ultrasound and magnetic resonance imaging combined with AI had been deepened and expanded in different degrees in the application research of breast cancer diagnosis and treatment. In the research of molybdenum target combined with AI, the high sensitivity of molybdenum target to microcalcification was mostly used to improve the accuracy of early detection and diagnosis of breast cancer, so as to achieve the clinical purpose of early detection and diagnosis. However, in terms of prediction of neoadjuvant efficacy research of breast cancer, ultrasound and magnetic resonance imaging combined with AI were more prevalent, and their popularity remained unabated. Conclusion In the monitoring of neoadjuvant therapy for breast cancer, the use of properly designed AI and radiomics models can give full play to its role in the predicting the curative effect of neoadjuvant therapy, and help to guide doctors in clinical diagnosis and treatment and evaluate the prognosis of breast cancer patients.

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2.	赵洪猛, 曹旭晨. 乳腺癌肿瘤内异质性的驱动因素及临床研究进展. 中国肿瘤临床, 2022, 49(15): 797-800.
3.	何洋, 赵伟鹏, 佟仲生. 新辅助化疗对乳腺癌ER PR HER-2及Ki-67表达影响的研究进展. 中国肿瘤临床, 2020, 7(22): 1185-1188.
4.	田力文, 王翠艳. 磁共振在乳腺癌新辅助治疗疗效评估与预测中的应用进展. 山东医药, 2023, 63(13): 87-91.
5.	许晓亮, 李新瑜, 李明霞. 乳腺肿块型癌与非肿块型癌的MRI、钼靶及超声差异分析. 实用癌症杂志, 2022, 37(2): 319-322.
6.	Mao N, Yin P, Wang Q, et al. Added value of radiomics on mammography for breast cancer diagnosis: a feasibility study. J Am Coll Radiol, 2019, 16(4 Pt A): 485-491.
7.	McKinney SM, Sieniek M, Godbole V, et al. International evaluation of an AI system for breast cancer screening. Nature, 2020, 577(7788): 89-94.
8.	Zhang N, Li XT, Ma L, et al. Application of deep learning to establish a diagnostic model of breast lesions using two-dimensional grayscale ultrasound imaging. Clin Imaging, 2021, 79: 56-63.
9.	Murtaza G, Shuib L, Wahab AWA, et al. Deep learning-based breast cancer classification through medical imaging modalities: state of the art and research challenges. Artificial Intelligence Review, 2019, 53: 1655-1720.
10.	彭卫军, 顾雅佳, 龚敬. 人工智能在乳腺肿瘤影像中的应用现状及展望. 中华放射学杂志, 2023, 57(2): 121-124.
11.	Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer, 2012, 48(4): 441-446.
12.	Choi RY, Coyner AS, Kalpathy-Cramer J, et al. Introduction to machine learning, neural networks, and deep learning. Transl Vis Sci Technol, 2020, 9(2): 14. doi: 10.1167/tvst.9.2.14.
13.	Basha SS, Dubey SR, Pulabaigari V, et al. Impact of fully connected layers on performance of convolutional neural networks for image classification. Neurocomputing, 2020, 378: 112-119.
14.	Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. Commun ACM, 2017, 60(6): 84-90.
15.	Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. ICLR, 2015, arXiv: 1409.1556.
16.	Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015: 1-9.
17.	李凤玲, 卫亚妮, 步宏. 人工智能在新辅助治疗后乳腺癌疗效及预后预测中的研究现状. 临床与实验病理学杂志, 2023, 39(7): 833-837.
18.	Wang H, Mao X. Evaluation of the efficacy of neoadjuvant chemotherapy for breast cancer. Drug Des Devel Ther, 2020, 14: 2423-2433.
19.	Byra M, Dobruch-Sobczak K, Klimonda Z, et al. Early prediction of response to neoadjuvant chemotherapy in breast cancer sonography using siamese convolutional neural networks. IEEE J Biomed Health Inform, 2021, 25(3): 797-805.
20.	Liu Y, Wang Y, Wang Y, et al. Early prediction of treatment response to neoadjuvant chemotherapy based on longitudinal ultrasound images of HER2-positive breast cancer patients by Siamese multi-task network: A multicentre, retrospective cohort study. EClinicalMedicine, 2022, 52: 101562. doi: 10.1016/j.eclinm.2022.101562.
21.	Jiang M, Li CL, Luo XM, et al. Ultrasound-based deep learning radiomics in the assessment of pathological complete response to neoadjuvant chemotherapy in locally advanced breast cancer. Eur J Cancer, 2021, 147: 95-105.
22.	Mann RM, Cho N, Moy L. Breast MRI: state of the art. Radiology, 2019, 292(3): 520-536.
23.	Rahmat K, Mumin NA, Hamid MTR, et al. MRI breast: current imaging trends, clinical applications, and future research directions. Curr Med Imaging, 2022, 18(13): 1347-1361.
24.	Scheel JR, Kim E, Partridge SC, et al. MRI, clinical examination, and mammography for preoperative assessment of residual disease and pathologic complete response after neoadjuvant chemotherapy for breast cancer: ACRIN 6657 trial. AJR Am J Roentgenol, 2018, 210(6): 1376-1385.
25.	Gu YL, Pan SM, Ren J, et al. Role of magnetic resonance imaging in detection of pathologic complete remission in breast cancer patients treated with neoadjuvant chemotherapy: A meta-analysis. Clin Breast Cancer, 2017, 17(4): 245-255.
26.	Bóuzon A, Acea B, Soler R, et al. Diagnostic accuracy of MRI to evaluate tumour response and residual tumour size after neoadjuvant chemotherapy in breast cancer patients. Radiol Oncol, 2016, 50(1): 73-79.
27.	Cain EH, Saha A, Harowicz MR, et al. Multivariate machine learning models for prediction of pathologic response to neoadjuvant therapy in breast cancer using MRI features: a study using an independent validation set. Breast Cancer Res Treat, 2019, 173(2): 455-463.
28.	Sutton EJ, Onishi N, Fehr DA, et al. A machine learning model that classifies breast cancer pathologic complete response on MRI post-neoadjuvant chemotherapy. Breast Cancer Res, 2020, 22(1): 57. doi: 10.1186/s13058-020-01291-w.
29.	El Adoui M, Drisis S, Benjelloun M. Multi-input deep learning architecture for predicting breast tumor response to chemotherapy using quantitative MR images. Int J Comput Assist Radiol Surg, 2020, 15(9): 1491-1500.
30.	Joo S, Ko ES, Kwon S, et al. Multimodal deep learning models for the prediction of pathologic response to neoadjuvant chemotherapy in breast cancer. Sci Rep, 2021, 11(1): 18800. doi: 10.1038/s41598-021-98408-8.
31.	Litjens G, Kooi T, Bejnordi BE, et al. A survey on deep learning in medical image analysis. Med Image Anal, 2017, 42: 60-88.
32.	Fujioka T, Mori M, Kubota K, et al. The utility of deep learning in breast ultrasonic imaging: a review. Diagnostics (Basel), 2020, 10(12): 1055. doi: 10.3390/diagnostics10121055.
33.	Lambert B, Forbes F, Doyle S, et al. Trustworthy clinical AI solutions: A unified review of uncertainty quantification in deep learning models for medical image analysis. Artif Intell Med, 024, 150: 102830. doi: 10.1016/j.artmed.2024.102830.
34.	Shen YT, Chen L, Yue WW, et al. Artificial intelligence in ultrasound. Eur J Radiol, 2021, 139: 109717. doi: 10.1016/j.ejrad.2021.109717.
35.	Hussain L, Huang P, Nguyen T, et al. Machine learning classification of texture features of MRI breast tumor and peri-tumor of combined pre- and early treatment predicts pathologic complete response. Biomed Eng Online, 2021, 20(1): 63. doi: 10.1186/s12938-021-00899-z.
36.	Khan N, Adam R, Huang P, et al. Deep learning prediction of pathologic complete response in breast cancer using MRI and other clinical data: a systematic review. Tomography, 2022, 8(6): 2784-2795.
37.	Lo Gullo R, Eskreis-Winkler S, Morris EA, et al. Machine learning with multiparametric magnetic resonance imaging of the breast for early prediction of response to neoadjuvant chemotherapy. Breast, 2020, 49: 115122.
38.	张靖, 宋君, 徐卫云, 等. MRI检查预测乳腺癌新辅助化疗后病理完全缓解的准确性分析. 中国普外基础与临床杂志, 2020, 27(8): 975-979.
39.	Dalmis MU, Gubern-Merida A, Vreemann S, et al. Artificial intelligence-based classification of breast lesions imaged with a multiparametric breast MRI protocol with ultrafast DCE-MRI, T2, and DWI. Invest Radiol, 2019, 54(6): 325-332.

1. Derks MGM, van de Velde CJH. Neoadjuvant chemotherapy in breast cancer: more than just downsizing. Lancet Oncol, 2018, 19(1): 2-3.
2. 赵洪猛, 曹旭晨. 乳腺癌肿瘤内异质性的驱动因素及临床研究进展. 中国肿瘤临床, 2022, 49(15): 797-800.
3. 何洋, 赵伟鹏, 佟仲生. 新辅助化疗对乳腺癌ER PR HER-2及Ki-67表达影响的研究进展. 中国肿瘤临床, 2020, 7(22): 1185-1188.
4. 田力文, 王翠艳. 磁共振在乳腺癌新辅助治疗疗效评估与预测中的应用进展. 山东医药, 2023, 63(13): 87-91.
5. 许晓亮, 李新瑜, 李明霞. 乳腺肿块型癌与非肿块型癌的MRI、钼靶及超声差异分析. 实用癌症杂志, 2022, 37(2): 319-322.
6. Mao N, Yin P, Wang Q, et al. Added value of radiomics on mammography for breast cancer diagnosis: a feasibility study. J Am Coll Radiol, 2019, 16(4 Pt A): 485-491.
7. McKinney SM, Sieniek M, Godbole V, et al. International evaluation of an AI system for breast cancer screening. Nature, 2020, 577(7788): 89-94.
8. Zhang N, Li XT, Ma L, et al. Application of deep learning to establish a diagnostic model of breast lesions using two-dimensional grayscale ultrasound imaging. Clin Imaging, 2021, 79: 56-63.
9. Murtaza G, Shuib L, Wahab AWA, et al. Deep learning-based breast cancer classification through medical imaging modalities: state of the art and research challenges. Artificial Intelligence Review, 2019, 53: 1655-1720.
10. 彭卫军, 顾雅佳, 龚敬. 人工智能在乳腺肿瘤影像中的应用现状及展望. 中华放射学杂志, 2023, 57(2): 121-124.
11. Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer, 2012, 48(4): 441-446.
12. Choi RY, Coyner AS, Kalpathy-Cramer J, et al. Introduction to machine learning, neural networks, and deep learning. Transl Vis Sci Technol, 2020, 9(2): 14. doi: 10.1167/tvst.9.2.14.
13. Basha SS, Dubey SR, Pulabaigari V, et al. Impact of fully connected layers on performance of convolutional neural networks for image classification. Neurocomputing, 2020, 378: 112-119.
14. Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. Commun ACM, 2017, 60(6): 84-90.
15. Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. ICLR, 2015, arXiv: 1409.1556.
16. Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015: 1-9.
17. 李凤玲, 卫亚妮, 步宏. 人工智能在新辅助治疗后乳腺癌疗效及预后预测中的研究现状. 临床与实验病理学杂志, 2023, 39(7): 833-837.
18. Wang H, Mao X. Evaluation of the efficacy of neoadjuvant chemotherapy for breast cancer. Drug Des Devel Ther, 2020, 14: 2423-2433.
19. Byra M, Dobruch-Sobczak K, Klimonda Z, et al. Early prediction of response to neoadjuvant chemotherapy in breast cancer sonography using siamese convolutional neural networks. IEEE J Biomed Health Inform, 2021, 25(3): 797-805.
20. Liu Y, Wang Y, Wang Y, et al. Early prediction of treatment response to neoadjuvant chemotherapy based on longitudinal ultrasound images of HER2-positive breast cancer patients by Siamese multi-task network: A multicentre, retrospective cohort study. EClinicalMedicine, 2022, 52: 101562. doi: 10.1016/j.eclinm.2022.101562.
21. Jiang M, Li CL, Luo XM, et al. Ultrasound-based deep learning radiomics in the assessment of pathological complete response to neoadjuvant chemotherapy in locally advanced breast cancer. Eur J Cancer, 2021, 147: 95-105.
22. Mann RM, Cho N, Moy L. Breast MRI: state of the art. Radiology, 2019, 292(3): 520-536.
23. Rahmat K, Mumin NA, Hamid MTR, et al. MRI breast: current imaging trends, clinical applications, and future research directions. Curr Med Imaging, 2022, 18(13): 1347-1361.
24. Scheel JR, Kim E, Partridge SC, et al. MRI, clinical examination, and mammography for preoperative assessment of residual disease and pathologic complete response after neoadjuvant chemotherapy for breast cancer: ACRIN 6657 trial. AJR Am J Roentgenol, 2018, 210(6): 1376-1385.
25. Gu YL, Pan SM, Ren J, et al. Role of magnetic resonance imaging in detection of pathologic complete remission in breast cancer patients treated with neoadjuvant chemotherapy: A meta-analysis. Clin Breast Cancer, 2017, 17(4): 245-255.
26. Bóuzon A, Acea B, Soler R, et al. Diagnostic accuracy of MRI to evaluate tumour response and residual tumour size after neoadjuvant chemotherapy in breast cancer patients. Radiol Oncol, 2016, 50(1): 73-79.
27. Cain EH, Saha A, Harowicz MR, et al. Multivariate machine learning models for prediction of pathologic response to neoadjuvant therapy in breast cancer using MRI features: a study using an independent validation set. Breast Cancer Res Treat, 2019, 173(2): 455-463.
28. Sutton EJ, Onishi N, Fehr DA, et al. A machine learning model that classifies breast cancer pathologic complete response on MRI post-neoadjuvant chemotherapy. Breast Cancer Res, 2020, 22(1): 57. doi: 10.1186/s13058-020-01291-w.
29. El Adoui M, Drisis S, Benjelloun M. Multi-input deep learning architecture for predicting breast tumor response to chemotherapy using quantitative MR images. Int J Comput Assist Radiol Surg, 2020, 15(9): 1491-1500.
30. Joo S, Ko ES, Kwon S, et al. Multimodal deep learning models for the prediction of pathologic response to neoadjuvant chemotherapy in breast cancer. Sci Rep, 2021, 11(1): 18800. doi: 10.1038/s41598-021-98408-8.
31. Litjens G, Kooi T, Bejnordi BE, et al. A survey on deep learning in medical image analysis. Med Image Anal, 2017, 42: 60-88.
32. Fujioka T, Mori M, Kubota K, et al. The utility of deep learning in breast ultrasonic imaging: a review. Diagnostics (Basel), 2020, 10(12): 1055. doi: 10.3390/diagnostics10121055.
33. Lambert B, Forbes F, Doyle S, et al. Trustworthy clinical AI solutions: A unified review of uncertainty quantification in deep learning models for medical image analysis. Artif Intell Med, 024, 150: 102830. doi: 10.1016/j.artmed.2024.102830.
34. Shen YT, Chen L, Yue WW, et al. Artificial intelligence in ultrasound. Eur J Radiol, 2021, 139: 109717. doi: 10.1016/j.ejrad.2021.109717.
35. Hussain L, Huang P, Nguyen T, et al. Machine learning classification of texture features of MRI breast tumor and peri-tumor of combined pre- and early treatment predicts pathologic complete response. Biomed Eng Online, 2021, 20(1): 63. doi: 10.1186/s12938-021-00899-z.
36. Khan N, Adam R, Huang P, et al. Deep learning prediction of pathologic complete response in breast cancer using MRI and other clinical data: a systematic review. Tomography, 2022, 8(6): 2784-2795.
37. Lo Gullo R, Eskreis-Winkler S, Morris EA, et al. Machine learning with multiparametric magnetic resonance imaging of the breast for early prediction of response to neoadjuvant chemotherapy. Breast, 2020, 49: 115122.
38. 张靖, 宋君, 徐卫云, 等. MRI检查预测乳腺癌新辅助化疗后病理完全缓解的准确性分析. 中国普外基础与临床杂志, 2020, 27(8): 975-979.
39. Dalmis MU, Gubern-Merida A, Vreemann S, et al. Artificial intelligence-based classification of breast lesions imaged with a multiparametric breast MRI protocol with ultrafast DCE-MRI, T2, and DWI. Invest Radiol, 2019, 54(6): 325-332.

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