多模影像在乳腺癌新辅助疗效中的应用
Application of Multimodal Radiomics in Assessing Neoadjuvant Therapeutic Efficacy for Breast Cancer
DOI: 10.12677/jcpm.2026.51047, PDF,   
作者: 刘 聪, 贾 薇：吉首大学医学院，湖南 吉首；吴 涛*：中南大学湘雅医学院附属常德医院(常德市第一人民医院)肿瘤科，湖南 常德
关键词: 乳腺癌；新辅助治疗；影像组学；疗效评估；病理学完全缓解率；Breast Cancer； Neoadjuvant Therapy； Radiomics； Therapeutic Effect Evaluation； Pathological Complete Response
摘要: 新辅助治疗(Neoadjuvant therapy, NAT)已成为局晚期乳腺癌的标准治疗方案，能够在术前缩小肿瘤体积，显著提高保乳率和患者生存率。近来年，随着影像组学技术的快速发展，多模态影像评估在新辅助治疗疗效监测和预后预测中发挥着越来越重要的作用，为临床决策提供了新思路与方法。本文就多模态影像组学在预测新辅助治疗后病理学完全缓解(Pathological Complete Response, pCR)中的价值，重点分析了各类影像学技术特征作为疗效预测的潜力，为乳腺癌患者提供更精准的个体化治疗策略提供依据。
Abstract: Neoadjuvant therapy (NAT) has emerged as the standard treatment protocol for locally advanced breast cancer, effectively reducing tumor volume preoperatively and significantly enhancing both breast-conservation rates and patient survival outcomes. In recent years, the rapid advancement of radiomics technology has elevated the role of multimodal imaging evaluation in monitoring therapeutic efficacy and prognostic prediction during NAT, offering novel perspectives and methodologies for clinical decision-making. This article aims to explore the value of multimodal radiomics in predicting pathological complete response (Pathological Complete Response, pCR) following NAT, with a particular focus on analyzing the potential of various imaging technique characteristics as predictors of therapeutic efficacy, thereby facilitating more precise and individualized treatment strategies for breast cancer patients.
文章引用：刘聪, 贾薇, 吴涛. 多模影像在乳腺癌新辅助疗效中的应用[J]. 临床个性化医学, 2026, 5(1): 324-331. https://doi.org/10.12677/jcpm.2026.51047

参考文献

[1] Bray, F., Laversanne, M., Sung, H., Ferlay, J., Siegel, R.L., Soerjomataram, I., et al. (2024) Global Cancer Statistics 2022: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA: A Cancer Journal for Clinicians, 74, 229-263. [Google Scholar] [CrossRef] [PubMed]
[2] Thompson, A.M. and Moulder-Thompson, S.L. (2012) Neoadjuvant Treatment of Breast Cancer. Annals of Oncology, 23, x231-x236. [Google Scholar] [CrossRef] [PubMed]
[3] Yau, C., Osdoit, M., van der Noordaa, M., et al. (2022) Residual Cancer Burden after Neoadjuvant Chemotherapy and Long-Term Survival Outcomes in Breast Cancer: A Multi-Centre Pooled Analysis of 5161 Patients. The Lancet Oncology, 23, 149-160.
[4] Cortazar, P., Zhang, L., Untch, M., Mehta, K., Costantino, J.P., Wolmark, N., et al. (2014) Pathological Complete Response and Long-Term Clinical Benefit in Breast Cancer: The CTNeoBC Pooled Analysis. The Lancet, 384, 164-172. [Google Scholar] [CrossRef] [PubMed]
[5] Omair, A., Alkushi, A., Alamri, G., et al. (2023) Assessing the Correlation of Rate of Pathological Complete Response and Outcome in Post Neoadjuvant Chemotherapy Setting and Molecular Subtypes of Breast Cancer. Cureus, 15, e37449.
[6] De Mattos-Arruda, L., Shen, R., Reis-Filho, J.S. and Cortés, J. (2016) Translating Neoadjuvant Therapy into Survival Benefits: One Size Does Not Fit All. Nature Reviews Clinical Oncology, 13, 566-579. [Google Scholar] [CrossRef] [PubMed]
[7] Rastogi, P., Anderson, S.J., Bear, H.D., Geyer, C.E., Kahlenberg, M.S., Robidoux, A., et al. (2008) Preoperative Chemotherapy: Updates of National Surgical Adjuvant Breast and Bowel Project Protocols B-18 and B-27. Journal of Clinical Oncology, 26, 778-785. [Google Scholar] [CrossRef] [PubMed]
[8] Schmid, P., Cortes, J., Dent, R., Pusztai, L., McArthur, H., Kümmel, S., et al. (2022) Event-Free Survival with Pembrolizumab in Early Triple-Negative Breast Cancer. New England Journal of Medicine, 386, 556-567. [Google Scholar] [CrossRef] [PubMed]
[9] Schmid, P., Cortes, J., Pusztai, L., McArthur, H., Kümmel, S., Bergh, J., et al. (2020) Pembrolizumab for Early Triple-Negative Breast Cancer. New England Journal of Medicine, 382, 810-821. [Google Scholar] [CrossRef] [PubMed]
[10] Cortes, J., Cescon, D.W., Rugo, H.S., Nowecki, Z., Im, S., Yusof, M.M., et al. (2020) Pembrolizumab Plus Chemotherapy versus Placebo Plus Chemotherapy for Previously Untreated Locally Recurrent Inoperable or Metastatic Triple-Negative Breast Cancer (KEYNOTE-355): A Randomised, Placebo-Controlled, Double-Blind, Phase 3 Clinical Trial. The Lancet, 396, 1817-1828. [Google Scholar] [CrossRef] [PubMed]
[11] Park, C.K., Jung, W. and Koo, J.S. (2016) Pathologic Evaluation of Breast Cancer after Neoadjuvant Therapy. Journal of Pathology and Translational Medicine, 50, 173-180. [Google Scholar] [CrossRef] [PubMed]
[12] 邵志敏, 江泽飞, 李俊杰, 等. 中国乳腺癌新辅助治疗专家共识(2019年版) [J]. 中国癌症杂志, 2019, 29(5): 390-400.
[13] 邵志敏, 吴炅, 江泽飞, 等. 中国乳腺癌新辅助治疗专家共识(2022年版) [J]. 中国癌症杂志, 2022, 32(1): 80-89.
[14] Uematsu, T. (2023) Rethinking Screening Mammography in Japan: Next-Generation Breast Cancer Screening through Breast Awareness and Supplemental Ultrasonography. Breast Cancer, 31, 24-30. [Google Scholar] [CrossRef] [PubMed]
[15] Feig, S.A. (1987) Mammography Equipment: Principles, Features, Selection. Radiologic Clinics of North America, 25, 897-911. [Google Scholar] [CrossRef] 
[16] 周桂萍, 李建梅, 马英桥, 等. 彩色多普勒超声、X线钼靶联合CT对乳腺癌的诊断价值分析[J]. 临床误诊误治, 2024, 37(10): 44-48.
[17] Helvie, M.A., Joynt, L.K., Cody, R.L., Pierce, L.J., Adler, D.D. and Merajver, S.D. (1996) Locally Advanced Breast Carcinoma: Accuracy of Mammography versus Clinical Examination in the Prediction of Residual Disease after Chemotherapy. Radiology, 198, 327-332. [Google Scholar] [CrossRef] [PubMed]
[18] Yang, M., Liu, H., Dai, Q., Yao, L., Zhang, S., Wang, Z., et al. (2022) Treatment Response Prediction Using Ultrasound-Based Pre-, Post-Early, and Delta Radiomics in Neoadjuvant Chemotherapy in Breast Cancer. Frontiers in Oncology, 12, Article 748008. [Google Scholar] [CrossRef] [PubMed]
[19] 陈雯, 余进洪, 杨豪, 等. 超声成像技术在乳腺癌诊治中的应用研究进展[J]. 影像研究与医学应用, 2021, 5(16): 8-10.
[20] 陈灵焕. 超声诊断乳腺癌的临床进展[J]. 中国医疗器械信息, 2021, 27(8): 24-25, 78.
[21] 张磊, 戴松. 彩色多普勒血流成像联合高频超声评估乳腺癌新辅助化疗疗效价值分析[J]. 医学影像学杂志, 2024, 34(7): 167-169.
[22] 王雪情, 王良玉. 多模态超声诊断乳腺良恶性肿块的应用进展[J]. 现代医用影像学, 2020, 29(9): 1665-1668.
[23] 王建文, 符德元. 超声成像新技术在乳腺癌诊断中的应用进展[J]. 影像研究与医学应用, 2020, 4(3): 2-3.
[24] Vourtsis, A. (2019) Three-dimensional Automated Breast Ultrasound: Technical Aspects and First Results. Diagnostic and Interventional Imaging, 100, 579-592. [Google Scholar] [CrossRef] [PubMed]
[25] 黄思婧, 徐晓红. 自动乳腺容积超声检查的临床应用进展[J]. 影像研究与医学应用, 2020, 4(8): 1-3.
[26] Wang, X., Huo, L., He, Y., Fan, Z., Wang, T., Xie, Y., et al. (2016) Early Prediction of Pathological Outcomes to Neoadjuvant Chemotherapy in Breast Cancer Patients Using Automated Breast Ultrasound. Chinese Journal of Cancer Research, 28, 478-485. [Google Scholar] [CrossRef] [PubMed]
[27] Chen, Y., Xie, Y., Li, B., Shao, H., Na, Z., Wang, Q., et al. (2023) Automated Breast Ultrasound (ABUS)-Based Radiomics Nomogram: An Individualized Tool for Predicting Axillary Lymph Node Tumor Burden in Patients with Early Breast Cancer. BMC Cancer, 23, Article No. 340. [Google Scholar] [CrossRef] [PubMed]
[28] McAnena, P., Moloney, B.M., Browne, R., O’Halloran, N., Walsh, L., Walsh, S., et al. (2022) A Radiomic Model to Classify Response to Neoadjuvant Chemotherapy in Breast Cancer. BMC Medical Imaging, 22, Article No. 225. [Google Scholar] [CrossRef] [PubMed]
[29] Galbán, C.J., Ma, B., Malyarenko, D., Pickles, M.D., Heist, K., Henry, N.L., et al. (2015) Multi-Site Clinical Evaluation of DW-MRI as a Treatment Response Metric for Breast Cancer Patients Undergoing Neoadjuvant Chemotherapy. PLOS ONE, 10, e0122151. [Google Scholar] [CrossRef] [PubMed]
[30] Partridge, S.C., Zhang, Z., Newitt, D.C., Gibbs, J.E., Chenevert, T.L., Rosen, M.A., et al. (2018) Diffusion-Weighted MRI Findings Predict Pathologic Response in Neoadjuvant Treatment of Breast Cancer: The ACRIN 6698 Multicenter Trial. Radiology, 289, 618-627. [Google Scholar] [CrossRef] [PubMed]
[31] Li, X., Huang, W. and Holmes, J.H. (2024) Dynamic Contrast-Enhanced (DCE) MRI. Magnetic Resonance Imaging Clinics of North America, 32, 47-61. [Google Scholar] [CrossRef] [PubMed]
[32] Hou, J., Yang, F., Gao, Y., Cai, H., Li, X., Lin, C., et al. (2025) MR Imaging of Breast Cancer: Interpretable Radiomics Analysis to Assess Treatment Response and Survival Prognosis after Neoadjuvant Therapy. International Journal of Cancer, 157, 1723-1733. [Google Scholar] [CrossRef] [PubMed]
[33] Meyer, H.J., Martin, M. and Denecke, T. (2022) DWI of the Breast—Possibilities and Limitations. RöFo—Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren, 194, 966-974. [Google Scholar] [CrossRef] [PubMed]
[34] Tozaki, M., Sakamoto, M., Oyama, Y., O'uchi, T., Kawano, N., Suzuki, T., et al. (2008) Monitoring of Early Response to Neoadjuvant Chemotherapy in Breast Cancer with 1h MR Spectroscopy: Comparison to Sequential 2‐[18F]‐Fluorodeoxyglucose Positron Emission Tomography. Journal of Magnetic Resonance Imaging, 28, 420-427. [Google Scholar] [CrossRef] [PubMed]
[35] 胡从英, 胡伟, 赵爽, 等. 乳腺癌新辅助化疗疗效的影像学评估进展[J]. 放射学实践, 2024, 39(11): 1537-1544.
[36] Liu, Z., Li, Z., Qu, J., Zhang, R., Zhou, X., Li, L., et al. (2019) Radiomics of Multiparametric MRI for Pretreatment Prediction of Pathologic Complete Response to Neoadjuvant Chemotherapy in Breast Cancer: A Multicenter Study. Clinical Cancer Research, 25, 3538-3547. [Google Scholar] [CrossRef] [PubMed]
[37] Hylton, N.M., Blume, J.D., Bernreuter, W.K., Pisano, E.D., Rosen, M.A., Morris, E.A., et al. (2012) Locally Advanced Breast Cancer: MR Imaging for Prediction of Response to Neoadjuvant Chemotherapy—Results from ACRIN 6657/I-SPY Trial. Radiology, 263, 663-672. [Google Scholar] [CrossRef] [PubMed]
[38] van der Voort, A., Louis, F.M., van Ramshorst, M.S., et al. (2024) MRI-Guided Optimisation of Neoadjuvant Chemotherapy Duration in Stage II-III HER2-Positive Breast Cancer (TRAIN-3): A Multicentre, Single-Arm, Phase 2 Study. The Lancet Oncology, 25, 603-613.
[39] Li, W., Huang, Y., Zhu, T., Zhang, Y., Zheng, X., Zhang, T., et al. (2024) Noninvasive Artificial Intelligence System for Early Predicting Residual Cancer Burden during Neoadjuvant Chemotherapy in Breast Cancer. Annals of Surgery, 281, 645-654. [Google Scholar] [CrossRef] [PubMed]
[40] 李国雄, 杨秀蓉. 正电子药物PET显像原理及其在肿瘤诊断中的作用[J]. 华南国防医学杂志, 2010, 24(3): 237-240.
[41] Som, P., Atkins, H.L., Bandoypadhyay, D., Fowler, J.S., MacGregor, R.R., Matsui, K., et al. (1980) A Fluorinated Glucose Analog, 2-Fluoro-2-Deoxy-D-Glucose (F-18): Non-Toxic Tracer for Rapid Tumor Detection. Journal of Nuclear Medicine: Official Publication, Society of Nuclear Medicine, 21, 670-675.
[42] Cheng, Y., Chen, Z., Huang, J. and Shao, D. (2023) Efficacy Evaluation of Neoadjuvant Immunotherapy Plus Chemotherapy for Non-Small-Cell Lung Cancer: Comparison of PET/CT with Postoperative Pathology. European Radiology, 33, 6625-6635. [Google Scholar] [CrossRef] [PubMed]
[43] Inchingolo, R., Maino, C., Cannella, R., Vernuccio, F., Cortese, F., Dezio, M., et al. (2023) Radiomics in Colorectal Cancer Patients. World Journal of Gastroenterology, 29, 2888-2904. [Google Scholar] [CrossRef] [PubMed]
[44] Pérez-García, J.M., Cortés, J., Ruiz-Borrego, M., Colleoni, M., Stradella, A., Bermejo, B., et al. (2024) 3-Year Invasive Disease-Free Survival with Chemotherapy De-Escalation Using an 18F-FDG-PET-Based, Pathological Complete Response-Adapted Strategy in HER2-Positive Early Breast Cancer (PHERGain): A Randomised, Open-Label, Phase 2 Trial. The Lancet, 403, 1649-1659. [Google Scholar] [CrossRef] [PubMed]
[45] Shao, Q., Zhang, N., Pan, X., Zhou, W., Wang, Y., Chen, X., et al. (2025) A Single-Arm Phase II Clinical Trial of Fulvestrant Combined with Neoadjuvant Chemotherapy of ER+/HER2– Locally Advanced Breast Cancer: Integrated Analysis of 18F-FES PET-CT and Metabolites with Treatment Response. Cancer Research and Treatment, 57, 126-139. [Google Scholar] [CrossRef] [PubMed]
[46] Chen, L., Zheng, S., Chen, L., Xu, S., Wu, K., Kong, L., et al. (2023) 68Ga-Labeled Fibroblast Activation Protein Inhibitor PET/CT for the Early and Late Prediction of Pathologic Response to Neoadjuvant Chemotherapy in Breast Cancer Patients: A Prospective Study. Journal of Nuclear Medicine, 64, 1899-1905. [Google Scholar] [CrossRef] [PubMed]
[47] Tian, F., Shen, G., Deng, Y., Diao, W. and Jia, Z. (2017) The Accuracy of 18F-FDG PET/CT in Predicting the Pathological Response to Neoadjuvant Chemotherapy in Patients with Breast Cancer: A Meta-Analysis and Systematic Review. European Radiology, 27, 4786-4796. [Google Scholar] [CrossRef] [PubMed]
[48] Mghanga, F.P., Lan, X., Bakari, K.H., Li, C. and Zhang, Y. (2013) Fluorine-18 Fluorodeoxyglucose Positron Emission Tomography-Computed Tomography in Monitoring the Response of Breast Cancer to Neoadjuvant Chemotherapy: A Meta-Analysis. Clinical Breast Cancer, 13, 271-279. [Google Scholar] [CrossRef] [PubMed]
[49] Filho, A.M., Laversanne, M., Ferlay, J., Colombet, M., Piñeros, M., Znaor, A., et al. (2024) The GLOBOCAN 2022 Cancer Estimates: Data Sources, Methods, and a Snapshot of the Cancer Burden Worldwide. International Journal of Cancer, 156, 1336-1346. [Google Scholar] [CrossRef] [PubMed]
[50] Conforti, F., Pala, L., Sala, I., Oriecuia, C., De Pas, T., Specchia, C., et al. (2021) Evaluation of Pathological Complete Response as Surrogate Endpoint in Neoadjuvant Randomised Clinical Trials of Early Stage Breast Cancer: Systematic Review and Meta-Analysis. BMJ, 375, e066381. [Google Scholar] [CrossRef] [PubMed]
[51] Croshaw, R., Shapiro-Wright, H., Svensson, E., Erb, K. and Julian, T. (2011) Accuracy of Clinical Examination, Digital Mammogram, Ultrasound, and MRI in Determining Postneoadjuvant Pathologic Tumor Response in Operable Breast Cancer Patients. Annals of Surgical Oncology, 18, 3160-3163. [Google Scholar] [CrossRef] [PubMed]
[52] 黄莉, 陈亚明, 张学军. 乳腺钼靶X线摄影和MRI对乳腺癌的诊断价值对照研究[J]. 中国CT和MRI杂志, 2021, 19(8): 89-91.
[53] 师红莉, 许秋霞. 多模态核磁共振成像技术对乳腺癌的诊断价值[J]. 中国实用医药, 2016, 11(26): 121-122.
[54] Liu, S., Ren, R., Chen, Z., Wang, Y., Fan, T., Li, C., et al. (2015) Diffusion‐Weighted Imaging in Assessing Pathological Response of Tumor in Breast Cancer Subtype to Neoadjuvant Chemotherapy. Journal of Magnetic Resonance Imaging, 42, 779-787. [Google Scholar] [CrossRef] [PubMed]
[55] Humbert, O., Cochet, A., Coudert, B., Berriolo-Riedinger, A., Kanoun, S., Brunotte, F., et al. (2015) Role of Positron Emission Tomography for the Monitoring of Response to Therapy in Breast Cancer. The Oncologist, 20, 94-104. [Google Scholar] [CrossRef] [PubMed]
[56] Eskian, M., Alavi, A., Khorasanizadeh, M., Viglianti, B.L., Jacobsson, H., Barwick, T.D., et al. (2018) Effect of Blood Glucose Level on Standardized Uptake Value (SUV) in 18F-FDG Pet-Scan: A Systematic Review and Meta-Analysis of 20,807 Individual SUV Measurements. European Journal of Nuclear Medicine and Molecular Imaging, 46, 224-237. [Google Scholar] [CrossRef] [PubMed]
[57] Nederlof, I., Isaeva, O.I., de Graaf, M., Gielen, R.C.A.M., Bakker, N.A.M., Rolfes, A.L., et al. (2024) Neoadjuvant Nivolumab or Nivolumab Plus Ipilimumab in Early-Stage Triple-Negative Breast Cancer: A Phase 2 Adaptive Trial. Nature Medicine, 30, 3223-3235. [Google Scholar] [CrossRef] [PubMed]