多模态影像技术评估乳腺癌术后复发的研究进展
Research Progress of Multimodal Imaging in Evaluating Postoperative Recurrence of Breast Cancer
DOI: 10.12677/ACM.2022.1291165, PDF,   
作者: 曹梦琳, 孟 莉*:青海大学附属医院影像中心,青海 西宁
关键词: 乳腺癌多模态影像学术后复发Breast Cancer Multi-Modality Imaging Postoperative Recurrence
摘要: 乳腺癌是全世界女性最常见的恶性肿瘤,目前的治疗主要是以手术为主的综合性治疗。但是,手术后的复发或转移,是造成患者治疗失败以及死亡的主要原因。所以,提前预测乳腺癌术后复发的高危因素,并制定相应的干预措施,对进一步改善患者的预后有着重要意义。随着影像诊断技术的进步,X线、超声、MRI、CT及PET等影像学检查用于乳腺疾病的鉴别及诊断。现就影像检查技术方面评估乳腺癌术后复发的研究进展进行综述。
Abstract: Breast cancer is the most common malignant tumor in women all over the world, and the current treatment is mainly surgical-based comprehensive treatment. However, recurrence or metastasis after surgery is the main cause of treatment failure and death of patients. Therefore, predicting the risk factors of postoperative recurrence of breast cancer in advance and formulating corresponding intervention measures are of great significance to further improve the prognosis of patients. With the progress of imaging diagnosis technology, X-ray, ultrasound, MRI, CT and PET imaging examina-tions are used to identify and diagnose breast diseases. Here is to make a review on the research progress of imaging technology in evaluating postoperative recurrence of breast cancer.
文章引用:曹梦琳, 孟莉. 多模态影像技术评估乳腺癌术后复发的研究进展[J]. 临床医学进展, 2022, 12(9): 8088-8095. https://doi.org/10.12677/ACM.2022.1291165

参考文献

[1] Riggio, A.I., Varley, K.E. and Welm, A.L. (2021) The Lingering Mysteries of Metastatic Recurrence in Breast Cancer. British Journal of Cancer, 124, 13-26.
https://www.nature.com/articles/s41416-020-01161-4
[2] Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R.L., Torre, L.A. and Jemal, A. (2018) Global Cancer Statistics 2018: GLOBOCAN Esti-mates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA: A Cancer Journal for Clinicians, 68, 394-424. [Google Scholar] [CrossRef] [PubMed]
[3] Ghoncheh, M., Pournamdar, Z. and Salehiniya, H. (2016) Inci-dence and Mortality and Epidemiology of Breast Cancer in the World. Asian Pacific Journal of Cancer Prevention, 17, 43-46. [Google Scholar] [CrossRef
[4] Yersal, O. and Barutca, S. (2014) Biological Subtypes of Breast Cancer: Prognostic and Therapeutic Implications. World Journal of Clinical Oncology, 5, 412-424. [Google Scholar] [CrossRef] [PubMed]
[5] Arpino, G., Generali, D., Sapino, A., et al. (2013) Gene Expression Profiling in Breast Cancer: A Clinical Perspective. The Breast, 22, 109-120. [Google Scholar] [CrossRef] [PubMed]
[6] Eliyatkin, N., Yalcin, E., Zengel, B., Aktaş, S. and Vardar, E. (2018) Molecular Classification of Breast Carcinoma: From Traditional, Old-Fashioned Way to a New Age, and a New Way. Journal of Breast Health, 11, 59-66.
[7] Anders, C. and Carey, L.A. (2008) Understanding and Treating Tri-ple-Negative Breast Cancer. Oncology (Williston Park), 22, 1233-1239.
[8] Hubalek, M., Czech, T. and Müller, H. (2017) Biological Subtypes of Triple-Negative Breast Cancer. Breast Care (Basel), 12, 8-14. [Google Scholar] [CrossRef] [PubMed]
[9] Carey, L.A., Perou, C.M., Livasy, C.A., et al. (2006) Race, Breast Cancer Subtypes, and Survival in the Carolina Breast Cancer Study. JAMA, 295, 2492-2502. [Google Scholar] [CrossRef] [PubMed]
[10] Fiorica, J.V. (2016) Breast Cancer Screening, Mammography, and other Modalities. Clinical Obstetrics and Gynecology, 59, 688-709. [Google Scholar] [CrossRef
[11] Jochelson, M. (2012) Advanced Imaging Techniques for the Detection of Breast Cancer. American Society of Clinical Oncology Educational Book, 32, 65-69. [Google Scholar] [CrossRef
[12] Dodelzon, K., Simon, K., Dou, E., Levy, A.D., Michaels, A.Y., Askin, G. and Katzen, J.T. (2020) Performance of 2D Synthetic Mammography versus Digital Mammography in the Detection of Microcalcifications at Screening. AJR. American Journal of Roentgenology, 214, 1436-1444. [Google Scholar] [CrossRef
[13] Clauser, P., Nagl, G., Helbich, T.H., Pinker-Domenig, K., Weber, M., Kapetas, P., Bernathova, M. and Baltzer, P.A.T. (2016) Diagnostic Performance of Digital Breast Tomosynthesis with a Wide Scan Angle Compared to Full-Field Digital Mammography for the Detection and Characterization of Microcalcifi-cations. European Journal of Radiology, 85, 2161-2168. [Google Scholar] [CrossRef] [PubMed]
[14] Hofvind, S., Holen, Å.S., Aase, H.S., Houssami, N., Sebuødegård, S., Moger, T.A., Haldorsen, I.S. and Akslen, L.A. (2019) Two-View Digital Breast Tomosynthesis versus Digital Mammography in a Population-Based Breast Cancer Screening Programme (To-Be): A Randomised, Controlled Trial. The Lancet Oncology, 20, 795-805. [Google Scholar] [CrossRef
[15] Haka, A.S., Shafer-Peltier, K.E., Fitzmaurice, M., Crowe, J., Dasari, R.R. and Feld, M.S. (2002) Identifying Microcalcifications in Benign and Malignant Breast Lesions by Probing Differences in Their Chemical Composition Using Raman Spectroscopy. Cancer Research, 62, 5375-5380.
[16] Tabár, L., Chen, H.-H., Duffy, S.W., Yen, M.F., Chiang, C.F., Dean, P.B., et al. (2000) A Novel Method for Prediction of Long-Term Outcome of Women with T1a, T1b, and 10-14 mm Invasive Breast Cancers: A Prospective Study. The Lan-cet, 355, 429-433. [Google Scholar] [CrossRef
[17] Ling, H., Liu, Z.-B., Xu, L.-H., Xu, X.-L., Liu, G.-Y. and Shao, Z.-M. (2013) Malignant Calcification Is an Important Unfavorable Prognostic Factor in Primary Invasive Breast Cancer. Asia-Pacific Journal of Clinical Oncology, 9, 139-145. [Google Scholar] [CrossRef] [PubMed]
[18] O’Grady, S. and Morgan, M.P. (2018) Microcalcifications in Breast Cancer: From Pathophysiology to Diagnosis and Prognosis. Biochimica et Biophysica Acta—Reviews on Can-cer, 1869, 310-320. [Google Scholar] [CrossRef] [PubMed]
[19] Li, J.-J., Chen, C., Gu, Y., Di, G., Wu, J., Liu, G., et al. (2014) The Role of Mammographic Calcification in the Neoadjuvant Therapy of Breast Cancer Imaging Evaluation. PLOS ONE, 9, e88853. [Google Scholar] [CrossRef] [PubMed]
[20] Murata, A., Sannomiya, N., Miyamoto, N., Ueda, N., Kamida, A., Koyanagi, Y., et al. (2015) Microcalcification of Tumor Is a Predictor of Response to Neoadjuvant Chemotherapy for Invasive Breast Carcinoma. Yonago Acta Medica, 58, 85-88.
[21] Nakashoji, A., Matsui, A., Nagayama, A., Iwata, Y., Sasahara, M. and Murata, Y. (2017) Clinical Predictors of Pathological Complete Response to Neoadjuvant Chemother-apy in Triple-Negative Breast Cancer. Oncology Letters, 14, 4135-4141. [Google Scholar] [CrossRef] [PubMed]
[22] DiCorpo, D., Tiwari, A., Tang, R., Griffin, M., Aftreth, O., Bautista, P., Hughes, K., Gershenfeld, N. and Michaelson, J. (2020) The Role of Micro-CT in Imaging Breast Cancer Specimens. Breast Cancer Research Treatment, 180, 343-357. [Google Scholar] [CrossRef] [PubMed]
[23] Xu, J., Li, F. and Chang, F. (2017) Correlation of the Ultrasound Imaging of Breast Cancer and the Expression of Molecular Biological Indexes. Pakistan Journal of Pharmaceutical Sci-ences, 30, 1425-1430.
[24] Shin, H.J., Kim, H.H., Huh, M.O., et al. (2011) Correlation between Mammographic and Sonographic Findings and Prognostic Factors in Patients with Node-Negative Invasive Breast Cancer. The British Jour-nal of Radiology, 84, 19-30. [Google Scholar] [CrossRef] [PubMed]
[25] Choi, W.J., Sim, H., Kim, H.J., Cha, J.H., Shin, H.J., Chae, E.Y. and Kim, H.H. (2021) Association of Mammography and Ultrasound Features with MammaPrint in Patients with Estrogen Receptor-Positive, HER2-Negative, Node-Positive Invasive Breast Cancer. Acta Radiologica, 62, 1592-1600. [Google Scholar] [CrossRef] [PubMed]
[26] Lehman, C.D., Isaacs, C., Schnall, M.D., et al. (2007) Cancer Yield of Mammography, MR, and US in High-Risk Women: Prospective Multi-Institution Breast Cancer Screening Study. Radiology, 244, 381-388. [Google Scholar] [CrossRef] [PubMed]
[27] Saslow, D., Boetes, C., Burke, W., et al. (2007) American Cancer Society Guidelines for Breast Screening with MRI as an Adjunct to Mammography. CA: A Cancer Journal for Clinicians, 57, 75-89. [Google Scholar] [CrossRef] [PubMed]
[28] Choi, E.J., Choi, H., Choi, S.A. and Youk, J.H. (2016) Dynamic Contrast-Enhanced Breast Magnetic Resonance Imaging for the Prediction of Early and Late Recurrences in Breast Cancer. Medicine (Baltimore), 95, e5330. [Google Scholar] [CrossRef
[29] Cheon, H., Kim, H.J., Kim, T.H., Ryeom, H.K., Lee, J., Kim, G., Yuk, J.S. and Kim, W.H. (2018) Invasive Breast Cancer: Prognostic Value of Peritumoral Edema Identified at Pre-operative MR Imaging. Radiology, 287, 68-75. [Google Scholar] [CrossRef] [PubMed]
[30] Costantini, M., Belli, P., Distefano, D., et al. (2012) Magnetic Resonance Imaging Features in Triple-Negative Breast Cancer: Comparison with Luminal and HER2-Overexpressing Tumors. Clinical Breast Cancer, 12, 331-339. [Google Scholar] [CrossRef] [PubMed]
[31] Navarro, V.L., Alandete, G.S.P., Medina García, R., Blanc García, E., Camarasa Lillo, N. and Vilar Samper, J. (2017) MR Imaging Findings in Molecular Subtypes of Breast Cancer Ac-cording to BIRADS System. The Breast Journal, 23, 421-428. [Google Scholar] [CrossRef] [PubMed]
[32] Kawashima, H., Miyati, T., Ohno, N., Ohno, M., Inokuchi, M., lkeda, H. and Gabata, T. (2017) Differentiation between Luminal-A and Luminal-B Breast Cancer Using Intravoxel Incoherent Motion and Dynamic Contrast-Enhanced Magnetic Resonance Imaging. Academic Radiology, 24, 1575-1581. [Google Scholar] [CrossRef] [PubMed]
[33] Shin, J.K. and Kim, J.Y. (2017) Dynamic Contrast-Enhanced and Diffusion-Weighted MRI of Estrogen Receptor-Positive Invasive Breast Cancers: Associations between Quantitative MR Parameters and Ki-67 Proliferation Status. Journal of Magnetic Reso-nance Imaging, 45, 94-102. [Google Scholar] [CrossRef] [PubMed]
[34] Kim, E.J., Kim, S.H., Park, G.E., Kang, B.J., Song, B.J., Kim, Y.J., Lee, D., Ahn, H., Kim, I., Son, Y.H. and Grimm, R. (2015) Histogram Analysis of Apparent Diffusion Coefficient at 3.0 t: Correlation with Prognostic Factors and Subtypes of Invasive Ductal Carcinoma. Journal of Magnetic Resonance Imaging, 42, 1666-1678. [Google Scholar] [CrossRef] [PubMed]
[35] Baxter, G.C., Graves, M.J., Gilbert, F.J. and Patterson, A.J. (2019) A Me-ta-Analysis of the Diagnostic Performance of Diffusion MRI for Breast Lesion Characterization. Radiology, 291, 632-641. [Google Scholar] [CrossRef] [PubMed]
[36] Onaygil, C., Kaya, H., Ugurlu, M.U. and Aribal, E. (2017) Diag-nostic Performance of Diffusion Tensor Imaging Parameters in Breast Cancer and Correlation with the Prognostic Fac-tors. Journal of Magnetic Resonance Imaging, 45, 660-672. [Google Scholar] [CrossRef] [PubMed]
[37] Zhang, L., Tang, M., Min, Z., Lu, J., Lei, X. and Zhang, X. (2016) Accuracy of Combined Dynamic Contrast-Enhanced Magnetic Resonance Imaging and Diffusion-Weighted Imaging for Breast Cancer Detection: A Meta-Analysis. Acta Radiologica, 57, 651-660. [Google Scholar] [CrossRef] [PubMed]
[38] Baltzer, P.A., Schafer, A., Dietzel, M., Grassel, D., Gajda, M., Camara, O., et al. (2011) Diffusion Tensor Magnetic Resonance Imaging of the Breast: A Pilot Study. Euro-pean Radiology, 21, 1-10. [Google Scholar] [CrossRef] [PubMed]
[39] Teruel, J.R., Goa, P.E., Sjobakk, T.E., Ostlie, A., Fjosne, H.E. and Bathen, T.F. (2016) Diffusion Weighted Imaging for the Differentiation of Breast Tumors: From Apparent Diffusion Coefficient to High Order Diffusion Tensor Imaging. Journal of Magnetic Resonance Imaging, 43, 1111-1121. [Google Scholar] [CrossRef] [PubMed]
[40] Partridge, S.C., Ziadloo, A., Murthy, R., White, S.W., Peacock, S., Eby, P.R., et al. (2010) Diffusion Tensor MRI: Preliminary Anisotropy Measures and Mapping of Breast Tumors. Journal of Magnetic Resonance Imaging, 31, 339-347. [Google Scholar] [CrossRef] [PubMed]
[41] Cakir, O., Arslan, A., Inan, N., Anik, Y., Sarisoy, T., Gumustas, S., et al. (2013) Comparison of the Diagnostic Performances of Diffusion Parameters in Diffusion Weighted Imaging and Diffu-sion Tensor Imaging of Breast Lesions. European Journal of Radiology, 82, e801-e806. [Google Scholar] [CrossRef] [PubMed]
[42] Eyal, E., Shapiro-Feinberg, M., Furman-Haran, E., Grobgeld, D., Golan, T., Itzchak, Y., et al. (2012) Parametric Diffusion Tensor Imaging of the Breast. Investigative Radiology, 47, 284-291. [Google Scholar] [CrossRef
[43] Yamaguchi, K., Nakazono, T., Egashira, R., Komori, Y., Nakamura, J., Noguchi, T., et al. (2017) Diagnostic Performance of Diffusion Tensor Imaging with Readout-Segmented Echo-Planar Imaging for Invasive Breast Cancer: Correlation of ADC and FA with Pathological Prognostic Markers. Magnetic Resonance in Medical Sciences, 16, 245-252. [Google Scholar] [CrossRef] [PubMed]
[44] Kim, J.Y., Kim, J.J., Kim, S., Choo, K.S., Kim, A., Kang, T., et al. (2018) Diffusion Tensor Magnetic Resonance Imaging of Breast Cancer: Associations between Diffusion Metrics and Histological Prognostic Factors. European Radiology, 28, 3185-3193. [Google Scholar] [CrossRef] [PubMed]
[45] Kvistad, K.A., Bakken, L.J., Gribbestad, I.S., et al. (1999) Char-acterization of Neoplastic and Normal Human Breast Tissues with in Vivo (1) HMR Spectroscopy. Journal of Magnetic Resonance Imaging, 10, 159-164. [Google Scholar] [CrossRef
[46] Cecil, K.M., Schnall, M.D., Siegelman, E.S. and Lenkinski, R.E. (2001) The Evaluation of Human Breast Lesions with Magnetic Resonance Imaging and Proton Magnetic Resonance Spectroscopy. Breast Cancer Research and Treatment, 68, 4554. [Google Scholar] [CrossRef
[47] Bolan, P.J. (2013) Magnetic Resonance Spectroscopy of the Breast: Current Status. Magnetic Resonance Imaging Clinics of North America, 21, 625-639. [Google Scholar] [CrossRef] [PubMed]
[48] Tozaki, M., Sakamoto, M., Oyama, Y., Maruyama, K. and Fukuma, E. (2010) Predicting Pathological Response to Neoadjuvant Chemotherapy in Breast Cancer with Quantitative 1H MR Spectroscopy Using the External Standard Method. Journal of Magnetic Resonance Imaging, 31, 895-902. [Google Scholar] [CrossRef] [PubMed]
[49] Jacobs, M.A., Stearns, V., Wolff, A.C., et al. (2010) Multiparametric Magnetic Resonance Imaging, Spectroscopy and Multinuclear ((2)(3)Na) Imaging Monitoring of Preoperative Chemo-therapy for Locally Advanced Breast Cancer. Academic Radiology, 17, 1477-1485. [Google Scholar] [CrossRef] [PubMed]
[50] Shin, H.J., Baek, H.M., Ahn, J.H., et al. (2012) Prediction of Path-ologic Response to Neoadjuvant Chemotherapy in Patients with Breast Cancer Using Diffusion-Weighted Imaging and MRS. NMR in Biomedicine, 25, 1349-1359. [Google Scholar] [CrossRef] [PubMed]
[51] Kajáry, K., Tőkés, T., Dank, M., Kulka, J., Szakáll, S. and Lengyel, Z. (2015) Correlation of the Value of 18F-FDG Uptake, Described by SUVmax, SUVavg, Metabolic Tumour Volume and Total Lesion Glycolysis, to Clinicopathological Prognostic Factors and Biological Subtypes in Breast Cancer. Nuclear Medicine Communications, 36, 28-37. [Google Scholar] [CrossRef
[52] Kaida, H., Toh, U., Hayakawa, M., Hattori, S., Fujii, T., Kurata, S., Kawa-hara, A., Hirose, Y., Kage, M. and Ishibashi, M. (2013) The Relationship between 18F-FDG Metabolic Volumetric Parameters and Clinicopathological Factors of Breast Cancer. Nuclear Medicine Communications, 34, 562-570. [Google Scholar] [CrossRef
[53] Tchou, J., Sonnad, S.S., Bergey, M.R., Basu, S., Tomaszewski, J., Alavi, A. and Schnall, M. (2010) Degree of Tumor FDG Uptake Correlates with Proliferation Index in Triple Negative Breast Cancer. Molecular Imaging and Biology, 12, 657-662. [Google Scholar] [CrossRef] [PubMed]
[54] Tőkés, T., Somlai, K., Székely, B., Kulka, J., Szentmártoni, G., Torgyík, L., Galgóczy, H., Lengyel, Z., Györke, T. and Dank, M. (2012) The Role of FDG-PET-CT in the Evaluation of Primary Systemic Therapy in Breast Cancer: Links between Metabolic and Pathological Remission. Orvosi Hetilap, 153, 1958-1964. [Google Scholar] [CrossRef
[55] Jiménez-Ballvé, A., García García-Esquinas, M., Salsidua-Arroyo, O., Serrano-Palacio, A., García-Sáenz, J.A., Ortega Candil, A., Fuentes Ferrer, M.E., Rodríguez Rey, C., Román-Santamaría, J.M., Moreno, F. and Carreras-Delgado, J.L. (2016) Prognostic Value of Metabolic Tumour Volume and Total Lesion Glycolysis in 18F-FDG PET/CT Scans in Locally Advanced Breast Cancer Staging. Revista Española de Medicina Nu-clear e Imagen Molecular, 35, 365-372. [Google Scholar] [CrossRef
[56] Groheux, D., Martineau, A., Teixeira, L., Espié, M., de Cremoux, P., Bertheau, P., Merlet, P. and Lemarignier, C. (2017) 18FDG-PET/CT for Predicting the Outcome in ER+/HER2-Breast Cancer Patients: Comparison of Clinicopathological Parameters and PET Image-Derived Indices In-cluding Tumor Texture Analysis. Breast Cancer Research, 19, Article No. 3. [Google Scholar] [CrossRef] [PubMed]