乳腺MRI Kaiser评分应用与优化研究进展
Research Advances in the Application and Optimization of the Kaiser Score in Breast MRI
摘要: 针对乳腺癌早期诊断需求，Kaiser评分(KS)通过结构化决策树量化乳腺MRI五大形态特征(毛刺征、强化模式、边缘、内部强化、水肿)，将病灶恶性风险转化为1~11分并映射至BI-RADS分类体系，显著提升诊断的标准化。其核心价值在于：精准鉴别BI-RADS 4类病灶(KS ≤ 4分时可使非肿块病变避免14.2%非必要活检且无漏诊)；突破非肿块强化病灶诊断瓶颈；预测侵袭性生物学行为(KS > 6分是脉管侵犯独立预测因子)。KS还可驱动多参数模型优化(如联合临床特征AUC达0.950)，但存在评估主观性、对微钙化敏感性不足等局限，未来需结合机器学习深化精准诊疗。
Abstract: To address the critical need for early breast cancer diagnosis, the Kaiser Score (KS) employs a structured decision tree to quantitatively assess five key morphological features on breast MRI (spiculation, enhancement kinetics, margin, internal enhancement characteristics, and edema) translating lesion malignancy risk into a numerical scale (1~11) directly mapped to the BI-RADS® (Breast Imaging Reporting and Data System) classification, thereby significantly enhancing diagnostic standardization. Its core clinical value lies in: enabling precise risk stratification of BI-RADS category 4 lesions (KS ≤ 4 safely averts 14.2% of unnecessary biopsies in non-mass lesions without missing malignancies); overcoming diagnostic limitations, particularly for challenging Non-Mass Enhancement (NME) lesions; and predicting aggressive pathological features (KS > 6 serves as a significant independent predictor of Lymphovascular Invasion (LVI)). Furthermore, the KS demonstrates strong potential for optimizing multiparametric models (achieving an AUC of 0.950 when combined with clinical features). Current limitations include inherent assessment subjectivity and suboptimal sensitivity for microcalcifications, highlighting the need for future integration with machine learning to advance precision diagnostics.
文章引用：郝清清, 胡晓航, 李晖. 乳腺MRI Kaiser评分应用与优化研究进展[J]. 临床医学进展, 2025, 15(8): 1693-1699. https://doi.org/10.12677/acm.2025.1582415

参考文献

[1] Yang, H., Wang, R., Zeng, F., Zhao, J., Peng, S., Ma, Y., et al. (2020) Impact of Molecular Subtypes on Metastatic Behavior and Overall Survival in Patients with Metastatic Breast Cancer: A SingleCenter Study Combined with a Large Cohort Study Based on the Surveillance, Epidemiology and End Results Database. Oncology Letters, 20, Article No. 87. [Google Scholar] [CrossRef] [PubMed]
[2] Johansson, A.L.V. and Stensheim, H. (2020) Epidemiology of Pregnancy-Associated Breast Cancer. In: Advances in Experimental Medicine and Biology, Springer, 75-79. [Google Scholar] [CrossRef] [PubMed]
[3] Nasim, Z., Girtain, C., Gupta, V., Patel, I. and Hossain, M.A. (2020) Breast Cancer Incidence and Behavior in Younger Patients: A Study from the Surveillance, Epidemiology and End Results Database. World Journal of Oncology, 11, 88-97. [Google Scholar] [CrossRef] [PubMed]
[4] Tao, H., O’Neil, A., Choi, Y., Wang, W., Wang, J., Wang, Y., et al. (2020) Pre-and Post-Diagnosis Diabetes as a Risk Factor for All-Cause and Cancer-Specific Mortality in Breast, Prostate, and Colorectal Cancer Survivors: A Prospective Cohort Study. Frontiers in Endocrinology, 11, Article 60. [Google Scholar] [CrossRef] [PubMed]
[5] Zhang, X.M., Dou, Q.L., Zeng, Y., et al. (2020) Sarcopenia as a Predictor of Mortality in Women with Breast Cancer: A Meta-Analysis and Systematic Review. BMC Cancer, 20, Article No. 172. [Google Scholar] [CrossRef] [PubMed]
[6] Runowicz, C.D., Leach, C.R., Henry, N.L., Henry, K.S., Mackey, H.T., Cowens-Alvarado, R.L., et al. (2016) American Cancer Society/American Society of Clinical Oncology Breast Cancer Survivorship Care Guideline. Journal of Clinical Oncology, 34, 611-635. [Google Scholar] [CrossRef] [PubMed]
[7] Barzaman, K., Karami, J., Zarei, Z., Hosseinzadeh, A., Kazemi, M.H., Moradi-Kalbolandi, S., et al. (2020) Breast Cancer: Biology, Biomarkers, and Treatments. International Immunopharmacology, 84, Article ID: 106535. [Google Scholar] [CrossRef] [PubMed]
[8] 李劭晋, 郑世鹏. 术前影像病理特征及肿瘤标志物预测乳腺癌前哨淋巴结转移风险[J]. 实用医学杂志, 2024, 40(17): 2418-2424.
[9] Monticciolo, D.L., Newell, M.S., Moy, L., Lee, C.S. and Destounis, S.V. (2023) Breast Cancer Screening for Women at Higher-Than-Average Risk: Updated Recommendations from the ACR. Journal of the American College of Radiology, 20, 902-914. [Google Scholar] [CrossRef] [PubMed]
[10] El Khoury, M., Lalonde, L., David, J., Labelle, M., Mesurolle, B. and Trop, I. (2015) Breast Imaging Reporting and Data System (BI-RADS) Lexicon for Breast MRI: Interobserver Variability in the Description and Assignment of BI-RADS Category. European Journal of Radiology, 84, 71-76. [Google Scholar] [CrossRef] [PubMed]
[11] Dietzel, M. and Baltzer, P.A.T. (2018) How to Use the Kaiser Score as a Clinical Decision Rule for Diagnosis in Multiparametric Breast MRI: A Pictorial Essay. Insights into Imaging, 9, 325-335. [Google Scholar] [CrossRef] [PubMed]
[12] Baltzer, P.A.T., Dietzel, M., Gröschel, T. and Kaiser, W.A. (2012) A Simple and Robust Classification Tree for Differentiation between Benign and Malignant Lesions in MR-Mammography. European Journal of Radiology, 81, S4-S5. [Google Scholar] [CrossRef] [PubMed]
[13] Dietzel, M., Bernathova, M., Clauser, P., Kapetas, P., Uder, M. and Baltzer, P.A.T. (2023) Added Value of Clinical Decision Rules for the Management of Enhancing Breast MRI Lesions: A Systematic Comparison of the Kaiser Score and the Göttingen Score. European Journal of Radiology, 169, Article ID: 111185. [Google Scholar] [CrossRef] [PubMed]
[14] Chen, Z.W., Zhao, Y.F., Liu, H.R., et al. (2022) Assessment of Breast Lesions by the Kaiser Score for Differential Diagnosis on MRI: The Added Value of ADC and Machine Learning Modeling. European Radiology, 32, 6608-6618. [Google Scholar] [CrossRef] [PubMed]
[15] Dietzel, M., Krug, B., Clauser, P., Burke, C., Hellmich, M., Maintz, D., et al. (2021) A Multicentric Comparison of Apparent Diffusion Coefficient Mapping and the Kaiser Score in the Assessment of Breast Lesions. Investigative Radiology, 56, 274-282. [Google Scholar] [CrossRef] [PubMed]
[16] Iima, M., Honda, M., Sigmund, E.E., Ohno Kishimoto, A., Kataoka, M. and Togashi, K. (2020) Diffusion MRI of the Breast: Current Status and Future Directions. Journal of Magnetic Resonance Imaging, 52, 70-90. [Google Scholar] [CrossRef] [PubMed]
[17] 邢健, 汤洋, 李奇, 等. 3.0T磁共振弥散加权成像表观扩散系数与乳腺癌病理类型分级相关性的临床价值研究[J]. 中国CT和MRI杂志, 2020, 18(10): 54-57+83.
[18] Meyer, H.J., Wienke, A. and Surov, A. (2022) Diffusion-Weighted Imaging of Different Breast Cancer Molecular Subtypes: A Systematic Review and Meta-Analysis. Breast Care, 17, 47-54. [Google Scholar] [CrossRef] [PubMed]
[19] 潘佳玲, 李晓虹, 陈新杰, 等. Kaiser评分与表观扩散系数评价乳腺病灶的对比研究[J]. 磁共振成像, 2022, 13(6): 108-111+116.
[20] 任晓梦, 刘晓春, 代天姿, 等. Kaiser评分与ADC值对乳腺BI-RADS 4类病变的诊断效能评价[J]. 磁共振成像, 2022, 13(9): 25-29+34.
[21] Woitek, R., Spick, C., Schernthaner, M., Rudas, M., Kapetas, P., Bernathova, M., et al. (2017) A Simple Classification System (the Tree Flowchart) for Breast MRI Can Reduce the Number of Unnecessary Biopsies in MRI-Only Lesions. European Radiology, 27, 3799-3809. [Google Scholar] [CrossRef] [PubMed]
[22] 易熙, 王月爱, 刘芳, 等. 基于Kaiser评分的乳腺MRI肿块诊断预测模型的构建与验证[J]. 磁共振成像, 2023, 14(5): 96-103.
[23] Leithner, D., Wengert, G., Helbich, T., Morris, E. and Pinker, K. (2017) MRI in the Assessment of BI-RADS 4 Lesions. Topics in Magnetic Resonance Imaging, 26, 191-199. [Google Scholar] [CrossRef] [PubMed]
[24] 安永玉, 刘畅, 张宏霞, 敖炜群, 杨光钊, 茅国群. Kaiser评分对MRI乳腺影像报告与数据系统4类病灶的诊断价值[J]. 浙江医学, 2021, 43(5): 511-515.
[25] 郭转转, 张冰, 雷喆, 等. Kaiser评分对乳腺超声BI-RADS 4A病变的诊断价值和避免不必要活检的潜力[J]. 西安交通大学学报(医学版), 2025, 46(2): 304-309.
[26] 王珊, 李建玉, 郑慧, 等. Kaiser评分对乳腺BI-RADS 4类非肿块样强化病灶的诊断价值分析[J]. 临床放射学杂志, 2021, 40(12): 2282-2286.
[27] 徐茂林, 李芳, 苏丹柯, 等. 动态增强磁共振成像对乳腺非肿块样强化良恶性病变的鉴别诊断价值[J]. 广西医科大学学报, 2015, 32(4): 575-578.
[28] Zhou, J., Liu, H., Miao, H., Ye, S., He, Y., Zhao, Y., et al. (2024) Breast Lesions on MRI in Mass and Non-Mass Enhancement: Kaiser Score and Modified Kaiser Score+ for Readers of Variable Experience. European Radiology, 35, 140-150. [Google Scholar] [CrossRef] [PubMed]
[29] 罗显廷, 冯倚敏, 周静, 等. 基于Kaiser评分的MRI影像特征列线图模型术前预测肿块型乳腺癌脉管侵犯的价值[J]. 中国癌症防治杂志, 2024, 16(3): 332-338.
[30] 高文霞, 盛美红, 肖建云, 等. 基于Kaiser评分临床-多参数MRI诊断模型在乳腺良恶性病变鉴别诊断中的价值[J]. 磁共振成像, 2024, 15(8): 117-123.
[31] 贾敏, 王龙胜, 吴宗山, 等. Kaiser评分与邻近血管征对乳腺病变的诊断效能评价[J]. 安徽医学, 2024, 45(11): 1385-1389.
[32] Quinn, L., Tryposkiadis, K., Deeks, J., De Vet, H.C.W., Mallett, S., Mokkink, L.B., et al. (2023) Interobserver Variability Studies in Diagnostic Imaging: A Methodological Systematic Review. The British Journal of Radiology, 96, Article ID: 20220972. [Google Scholar] [CrossRef] [PubMed]
[33] Kim, J.H., Ko, E.S., Kim, D.Y., Han, H., Sohn, J. and Choe, D.H. (2009) Noncalcified Ductal Carcinoma in Situ. Journal of Ultrasound in Medicine, 28, 903-910. [Google Scholar] [CrossRef] [PubMed]
[34] Grimm, L.J., Ghate, S.V., Hwang, E.S. and Soo, M.S. (2017) Imaging Features of Patients Undergoing Active Surveillance for Ductal Carcinoma in Situ. Academic Radiology, 24, 1364-1371. [Google Scholar] [CrossRef] [PubMed]

为你推荐