|
|
MRI影像组学在乳腺癌中的应用进展
|
Abstract:
乳腺癌是女性最常见的恶性肿瘤之一。磁共振成像因其良好的软组织分辨率、无辐射、可重复性等特点现已成为乳腺癌常用检查方法。影像组学技术通过高通量地提取人眼无法观察到的影像组学特征来反映肿瘤内异性，具有可重复性、无创性及客观性等特点，现已广泛应用于乳腺癌诊断、淋巴结转移评估及新辅助疗效评估等方面。本文就基于MRI影像组学技术在乳腺癌诊疗中的研究进展予以综述，并探讨当前存在的局限性及挑战，以期为临床个体化精准医疗提供新思路。
Breast cancer is one of the most common malignant tumours in women. Magnetic resonance imaging has now become a commonly used screening method for breast cancer due to its good soft tissue resolution, no radiation and reproducibility. Imaging histology technology reflects the internal heterogeneity of the tumour through high-throughput extraction of imaging histological features that cannot be observed by the human eye, which is reproducible, non-invasive and objective, and is now widely used in the diagnosis of breast cancer, the assessment of lymph node metastasis, and the evaluation of neoadjuvant therapeutic efficacy. In this paper, we review the research progress of MRI-based imaging histology in breast cancer diagnosis and treatment, and discuss the current limitations and challenges, with a view to providing new ideas for clinical individualised precision medicine.
| [1] | Chen, W., Zheng, R., Baade, P.D., Zhang, S., Zeng, H., Bray, F., et al. (2016) Cancer Statistics in China, 2015. CA: A Cancer Journal for Clinicians, 66, 115-132. https://doi.org/10.3322/caac.21338 |
| [2] | Kolarik, D., Pecha, V., Skovajsova, M., Zahumensky, J., Trnkova, M., Petruzelka, L., et al. (2013) Predicting Axillary Sentinel Node Status in Patients with Primary Breast Cancer. Neoplasma, 60, 334-342. https://doi.org/10.4149/neo_2013_045 |
| [3] | Yoen, H., Jang, M.J., Yi, A., et al. (2024) Artificial Intelligence for Breast Cancer Detection on Mammography: Factors Related to Cancer Detection. Academic Radiology. |
| [4] | Ber,g W.A. (2019) MR BI-RADS Lexicon and Usage. In: Berg, W.A. and Leung, J.W.T., Eds., Diagnostic Imaging: Breast (3rd ed), Elsevier, 320. |
| [5] | 杨亦, 姚钰, 刘家伟, 等. 多种影像学手段评估乳腺癌患者腋窝淋巴结状态的对比研究[J]. 南京医科大学学报(自然科学版), 2019, 39(5): 721-726. |
| [6] | Lambin, P., Rios-Velazquez, E., Leijenaar, R., Carvalho, S., van Stiphout, R.G.P.M., Granton, P., et al. (2012) Radiomics: Extracting More Information from Medical Images Using Advanced Feature Analysis. European Journal of Cancer, 48, 441-446. https://doi.org/10.1016/j.ejca.2011.11.036 |
| [7] | Zhang, Q., Peng, Y., Liu, W., Bai, J., Zheng, J., Yang, X., et al. (2020) Radiomics Based on Multimodal MRI for the Differential Diagnosis of Benign and Malignant Breast Lesions. Journal of Magnetic Resonance Imaging, 52, 596-607. https://doi.org/10.1002/jmri.27098 |
| [8] | Bai, D., Zhou, N., Liu, X., Liang, Y., Lu, X., Wang, J., et al. (2024) The Diagnostic Value of Multimodal Imaging Based on MR Combined with Ultrasound in Benign and Malignant Breast Diseases. Clinical and Experimental Medicine, 24, Article No. 110. https://doi.org/10.1007/s10238-024-01377-1 |
| [9] | Torous, V.F., Resteghini, N.A., Phillips, J., Dialani, V., Slanetz, P.J., Schnitt, S.J., et al. (2021) Histopathologic Correlates of Nonmass Enhancement Detected by Breast Magnetic Resonance Imaging. Archives of Pathology & Laboratory Medicine, 145, 1264-1269. https://doi.org/10.5858/arpa.2020-0266-oa |
| [10] | Li, Y., Yang, Z.L., Lv, W.Z., Qin, Y.J., Tang, C.L., Yan, X., et al. (2021) Non-Mass Enhancements on DCE-MRI: Development and Validation of a Radiomics-Based Signature for Breast Cancer Diagnoses. Frontiers in Oncology, 11, Article 738330. https://doi.org/10.3389/fonc.2021.738330 |
| [11] | Kayadibi, Y., Saracoglu, M.S., Kurt, S.A., Deger, E., Boy, F.N.S., Ucar, N., et al. (2024) Differentiation of Malignancy and Idiopathic Granulomatous Mastitis Presenting as Non-Mass Lesions on MRI: Radiological, Clinical, Radiomics, and Clinical-Radiomics Models. Academic Radiology. https://doi.org/10.1016/j.acra.2024.03.025 |
| [12] | Harbeck, N., Thomssen, C. and Gnant, M. (2013) St. Gallen 2013: Brief Preliminary Summary of the Consensus Discussion. Breast Care, 8, 102-109. https://doi.org/10.1159/000351193 |
| [13] | Xu, H., Liu, J., Chen, Z., Wang, C., Liu, Y., Wang, M., et al. (2022) Intratumoral and Peritumoral Radiomics Based on Dynamic Contrast-Enhanced MRI for Preoperative Prediction of Intraductal Component in Invasive Breast Cancer. European Radiology, 32, 4845-4856. https://doi.org/10.1007/s00330-022-08539-3 |
| [14] | 黄晓妮. DCE-MRI影像特征联合影像组学标签预测浸润性乳腺癌分子亚型的价值研究[D]: [硕士学位论文]. 广州: 南方医科大学, 2023. |
| [15] | Kova？evi？, L., ？tajduhar, A., Stemberger, K., Kor？a, L., Maru？i？, Z. and Prutki, M. (2023) Breast Cancer Surrogate Subtype Classification Using Pretreatment Multi-Phase Dynamic Contrast-Enhanced Magnetic Resonance Imaging Radiomics: A Retrospective Single-Center Study. Journal of Personalized Medicine, 13, Article 1150. https://doi.org/10.3390/jpm13071150 |
| [16] | 张前勇, 王斌. 磁共振成像影像组学特征与乳腺癌分子分型的相关性研究[J]. 新疆医科大学学报, 2023, 46(10): 1307-1312. |
| [17] | Kim, M.Y. (2021) Breast Cancer Metastasis. In: Noh, D.Y., Han, W. and Toi, M., Eds., Advances in Experimental Medicine and Biology, Springer Singapore, 183-204. https://doi.org/10.1007/978-981-32-9620-6_9 |
| [18] | Chang, J.M., Leung, J.W.T., Moy, L., Ha, S.M. and Moon, W.K. (2020) Axillary Nodal Evaluation in Breast Cancer: State of the Art. Radiology, 295, 500-515. https://doi.org/10.1148/radiol.2020192534 |
| [19] | Shan, Y., Xu, W., Wang, R., Wang, W., Pang, P. and Shen, Q. (2020) A Nomogram Combined Radiomics and Kinetic Curve Pattern as Imaging Biomarker for Detecting Metastatic Axillary Lymph Node in Invasive Breast Cancer. Frontiers in Oncology, 10, Article 1463. https://doi.org/10.3389/fonc.2020.01463 |
| [20] | Cheng, Y., Xu, S., Wang, H., Wang, X., Niu, S., Luo, Y., et al. (2022) Intra-and Peri-Tumoral Radiomics for Predicting the Sentinel Lymph Node Metastasis in Breast Cancer Based on Preoperative Mammography and MRI. Frontiers in Oncology, 12, Article 1047572. https://doi.org/10.3389/fonc.2022.1047572 |
| [21] | Yu, Y., He, Z., Ouyang, J., Tan, Y., Chen, Y., Gu, Y., et al. (2021) Magnetic Resonance Imaging Radiomics Predicts Preoperative Axillary Lymph Node Metastasis to Support Surgical Decisions and Is Associated with Tumor Microenvironment in Invasive Breast Cancer: A Machine Learning, Multicenter Study. eBioMedicine, 69, Article ID: 103460. https://doi.org/10.1016/j.ebiom.2021.103460 |
| [22] | Chen, Y., Wang, L., Dong, X., Luo, R., Ge, Y., Liu, H., et al. (2023) Deep Learning Radiomics of Preoperative Breast MRI for Prediction of Axillary Lymph Node Metastasis in Breast Cancer. Journal of Digital Imaging, 36, 1323-1331. https://doi.org/10.1007/s10278-023-00818-9 |
| [23] | Huober, J. and von Minckwitz, G. (2011) Neoadjuvant Therapy—What Have We Achieved in the Last 20 Years. Breast Care, 6, 419-426. https://doi.org/10.1159/000335347 |
| [24] | Navarro-Cecilia, J., Due？as-Rodríguez, B., Luque-López, C., Ramírez-Expósito, M.J., Martínez-Ferrol, J., Ruíz-Mateas, A., et al. (2013) Intraoperative Sentinel Node Biopsy by One-Step Nucleic Acid Amplification (OSNA) Avoids Axillary Lymphadenectomy in Women with Breast Cancer Treated with Neoadjuvant Chemotherapy. European Journal of Surgical Oncology (EJSO), 39, 873-879. https://doi.org/10.1016/j.ejso.2013.05.002 |
| [25] | 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. https://doi.org/10.1158/1078-0432.ccr-18-3190 |
| [26] | Panthi, B., Mohamed, R.M., Adrada, B.E., Boge, M., Candelaria, R.P., Chen, H., et al. (2023) Longitudinal Dynamic Contrast-Enhanced MRI Radiomic Models for Early Prediction of Response to Neoadjuvant Systemic Therapy in Triple-Negative Breast Cancer. Frontiers in Oncology, 13, Article 1264259. https://doi.org/10.3389/fonc.2023.1264259 |
| [27] | Li, X. and Yan, F. (2024) Predictive Value of Background Parenchymal Enhancement on Breast Magnetic Resonance Imaging for Pathological Tumor Response to Neoadjuvant Chemotherapy in Breast Cancers: A Systematic Review. Cancer Imaging, 24, Article No. 35. https://doi.org/10.1186/s40644-024-00672-0 |
| [28] | Chen, J.H., Yu, H.J., Hsu, C., Mehta, R.S., Carpenter, P.M. and Su, M.Y. (2015) Background Parenchymal Enhancement of the Contralateral Normal Breast: Association with Tumor Response in Breast Cancer Patients Receiving Neoadjuvant Chemotherapy. Translational Oncology, 8, 204-209. https://doi.org/10.1016/j.tranon.2015.04.001 |
| [29] | Arasu, V.A., Kim, P., Li, W., Strand, F., McHargue, C., Harnish, R., et al. (2020) Predictive Value of Breast MRI Background Parenchymal Enhancement for Neoadjuvant Treatment Response among HER2—Patients. Journal of Breast Imaging, 2, 352-360. https://doi.org/10.1093/jbi/wbaa028 |
| [30] | Gatenby, R.A., Grove, O. and Gillies, R.J. (2013) Quantitative Imaging in Cancer Evolution and Ecology. Radiology, 269, 8-14. https://doi.org/10.1148/radiol.13122697 |
| [31] | Natrajan, R., Sailem, H., Mardakheh, F.K., Arias Garcia, M., Tape, C.J., Dowsett, M., et al. (2016) Microenvironmental Heterogeneity Parallels Breast Cancer Progression: A Histology-Genomic Integration Analysis. PLOS Medicine, 13, e1001961. https://doi.org/10.1371/journal.pmed.1001961 |
| [32] | Shi, Z., Huang, X., Cheng, Z., Xu, Z., Lin, H., Liu, C., et al. (2023) MRI-Based Quantification of Intratumoral Heterogeneity for Predicting Treatment Response to Neoadjuvant Chemotherapy in Breast Cancer. Radiology, 308, e222830. https://doi.org/10.1148/radiol.222830 |
