收稿日期: 2023-07-17
网络出版日期: 2023-12-11
基金资助
教育部产学合作协同育人项目(22097112073101)
Advance in predicting lymph node metastasis of breast cancer by multimodal MRI
Received date: 2023-07-17
Online published: 2023-12-11
乳腺癌是女性最常见的肿瘤相关死亡原因之一，占女性恶性肿瘤发病率的第1位，其发病率逐年上升、逐渐年轻化。淋巴结（LN）转移是评估乳腺癌患者复发和生存率最重要的预测指标，准确评估腋窝淋巴结是否受累是乳腺癌分期的重要组成部分。前哨淋巴结活检术（SLNB）和腋窝淋巴结清扫术（ALND）均是有创检查，存在并发症风险及穿刺假阴性率较高，因此无创性影像学检查方法预测腋窝淋巴结转移成为近年的研究热点。多模态磁共振较其他成像方式预测乳腺癌淋巴转移更具优势，如无辐射、软组织分辨率更高、可进行多参数及多序列成像，应用于鉴别良恶性乳腺病变、评估乳腺癌患者预后、预测淋巴结转移状态、评估新辅助化疗的疗效等。本文就多模态磁共振中各指数模型及峰度扩散成像预测乳腺癌腋窝淋巴结转移的研究进展进行综述。
石夕冉 , 王恒 , 何力兵 , 邱志强 , 李泓箭 , 徐晓雪 . 多模态磁共振预测乳腺癌淋巴结转移状态的研究进展[J]. 实用医学杂志, 2023 , 39(22) : 2861 -2865 . DOI: 10.3969/j.issn.1006-5725.2023.22.001
Breast cancer is one of the most common tumor-related causes of death in women， accounting for the first place in the incidence of female malignant tumors， with its incidence increasing yearly and the age of onset gradually becoming younger. Lymph node（LN）metastasis is the most important predictor to evaluate the recurrence and survival rate of patients with breast cancer. Accurate assessment of axillary lymph node involvement is an important part for breast cancer staging. Sentinel lymph node biopsy（SLNB）and axillary lymph node dissection（ALND）are both invasive procedures with high risk of complications and false negative rate. Therefore， using non-invasive imaging methods to predict axillary lymph node metastasis have become a research hotspot in recent years. Multimodal magnetic resonance has the advantages of no radiation， high soft tissue resolution， multiple parameters， and multiple sequences compared with other imaging methods in predicting lymph node metastasis of breast cancer. It is widely used in the differentiation of benign and malignant breast lesions， evaluation of prognosis of breast cancer patients， evaluation of lymph node metastasis status， and evaluation of the efficacy of neoadjuvant chemotherapy. This article reviews the research progress of multimodal MRI in predicting axillary lymph node metastasis of breast cancer with various index models and kurtosis diffusion imaging.
| 1 | SUNG H， FERLAY J， SIEGEL R L， et al. Global Cancer Statistics 2020： GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries［J］. CA Cancer J Clin， 2021，71（3）：209-249. |
| 2 | HYUN S J， KIM E K， MOON H J， et al. Preoperative axillary lymph node evaluation in breast cancer patients by breast magnetic resonance imaging （MRI）： Can breast MRI exclude advanced nodal disease？［J］. Eur Radiol， 2016，26（11）：3865-3873. |
| 3 | GIULIANO A E， HUNT K K， BALLMAN K V， et al. Axillary dissection vs no axillary dissection in women with invasive breast cancer and sentinel node metastasis： a randomized clinical trial［J］. JAMA， 2011，305（6）：569-575. |
| 4 | LYMAN G H， SOMERFIELD M R， BOSSERMAN L D， et al. Sentinel Lymph Node Biopsy for Patients With Early-Stage Breast Cancer： American Society of Clinical Oncology Clinical Practice Guideline Update［J］. J Clin Oncol， 2017，35（5）：561-564. |
| 5 | KOOTSTRA J， HOEKSTRA-WEEBERS J E， RIETMAN H， et al. Quality of life after sentinel lymph node biopsy or axillary lymph node dissection in stage I/II breast cancer patients： a prospective longitudinal study［J］. Ann Surg Oncol， 2008，15（9）：2533-2541. |
| 6 | FEHM T， MAUL H， GEBAUER S， et al. Prediction of axillary lymph node status of breast cancer patients by tumorbiological factors of the primary tumor［J］. Strahlenther Onkol， 2005，181（9）：580-586. |
| 7 | CHOI E J， YOUK J H， CHOI H， et al. Dynamic contrast-enhanced and diffusion-weighted MRI of invasive breast cancer for the prediction of sentinel lymph node status［J］. J Magn Reson Imaging， 2020，51（2）：615-626. |
| 8 | RAHMAT K， MUMIN N A， HAMID M， et al. MRI Breast： Current Imaging Trends， Clinical Applications， and Future Research Directions［J］. Curr Med Imaging， 2022，18（13）：1347-1361. |
| 9 | 王保茎， 秦全波， 毛怡盛， 等. 动态增强磁共振联合DWI对乳腺癌淋巴结转移的诊断价值［J］. 实用癌症杂志， 2020，35（2）：270-273. |
| 10 | SUROV A， MEYER H J， WIENKE A. Can apparent diffusion coefficient （ADC） distinguish breast cancer from benign breast findings？ A meta-analysis based on 13 847 lesions［J］. BMC Cancer， 2019，19（1）：955. |
| 11 | KOH D M， COLLINS D J. Diffusion-weighted MRI in the body： applications and challenges in oncology［J］. AJR Am J Roentgenol， 2007，188（6）：1622-1635. |
| 12 | CHO P， PARK C S， PARK G E， et al. Diagnostic Usefulness of Diffusion-Weighted MRI for Axillary Lymph Node Evaluation in Patients with Breast Cancer［J］. Diagnostics （Basel）， 2023，13（3）：513. |
| 13 | KURT N， BINBOGA K B， GULSARAN U， et al. Diffusion tensor imaging and diffusion-weighted imaging on axillary lymph node status in breast cancer patients［J］. Diagn Interv Radiol， 2022，28（4）：329-336. |
| 14 | YANG Z L， LI Y， ZHAN C A， et al. Evaluation of suspicious breast lesions with diffusion kurtosis MR imaging and connection with prognostic factors［J］. Eur J Radiol， 2021，145：110014. |
| 15 | SUN J H， JIANG L， GUO F， et al. Diagnostic significance of apparent diffusion coefficient values with diffusion weighted MRI in breast cancer： a meta- analysis［J］. Asian Pac J Cancer Prev， 2014，15（19）：8271-8277. |
| 16 | LE BIHAN D， BRETON E， LALLEMAND D， et al. MR imaging of intravoxel incoherent motions： application to diffusion and perfusion in neurologic disorders［J］. Radiology， 1986，161（2）：401-407. |
| 17 | LE BIHAN D， TURNER R. The capillary network： a link between IVIM and classical perfusion［J］. Magn Reson Med， 1992，27（1）：171-178. |
| 18 | ZHU Y， LI X， WANG F， et al. Intravoxel incoherent motion diffusion-weighted magnetic resonance imaging in characterization of axillary lymph nodes： Preliminary animal experience［J］. Magn Reson Imaging， 2018，52：46-52. |
| 19 | ZHAO M， WU Q， GUO L， et al. Magnetic resonance imaging features for predicting axillary lymph node metastasis in patients with breast cancer［J］. Eur J Radiol， 2020，129：109093. |
| 20 | ZHOU Z， CHEN Y， ZHAO F， et al. Predictive value of intravoxel incoherent motion combined with diffusion kurtosis imaging for breast cancer axillary lymph node metastasis： a retrospective study［J］. Acta Radiol， 2023，64（3）：951-961. |
| 21 | LIU Y， LUO H， WANG C， et al. Diagnostic performance of T2-weighted imaging and intravoxel incoherent motion diffusion-weighted MRI for predicting metastatic axillary lymph nodes in T1 and T2 stage breast cancer［J］. Acta Radiol， 2022，63（4）：447-457. |
| 22 | BENNETT K M， SCHMAINDA K M， BENNETT R T， et al. Characterization of continuously distributed cortical water diffusion rates with a stretched-exponential model［J］. Magn Reson Med， 2003，50（4）：727-734. |
| 23 | SUO S， CHENG F， CAO M， et al. Multiparametric diffusion-weighted imaging in breast lesions： Association with pathologic diagnosis and prognostic factors［J］. J Magn Reson Imaging， 2017，46（3）：740-750. |
| 24 | MAZAHERI Y， AFAQ A， ROWE D B， et al. Diffusion-weighted magnetic resonance imaging of the prostate： improved robustness with stretched exponential modeling［J］. J Comput Assist Tomogr， 2012，36（6）：695-703. |
| 25 | WINFIELD J M， DESOUZA N M， PRIEST A N， et al. Modelling DW-MRI data from primary and metastatic ovarian tumours［J］. Eur Radiol， 2015，25（7）：2033-2040. |
| 26 | PANEK R， BORRI M， ORTON M， et al. Evaluation of diffusion models in breast cancer［J］. Med Phys， 2015，42（8）：4833-4839. |
| 27 | SUO S， YIN Y， GENG X， et al. Diffusion-weighted MRI for predicting pathologic response to neoadjuvant chemotherapy in breast cancer： evaluation with mono-， bi-， and stretched-exponential models［J］. J Transl Med， 2021，19（1）：236. |
| 28 | KIM Y， KIM S H， LEE H W， et al. Intravoxel incoherent motion diffusion-weighted MRI for predicting response to neoadjuvant chemotherapy in breast cancer［J］. Magn Reson Imaging， 2018，48：27-33. |
| 29 | ALMUTLAQ Z M， WILSON D J， BACON S E， et al. Evaluation of Monoexponential， Stretched-Exponential and Intravoxel Incoherent Motion MRI Diffusion Models in Early Response Monitoring to Neoadjuvant Chemotherapy in Patients With Breast Cancer-A Preliminary Study［J］. J Magn Reson Imaging， 2022，56（4）：1079-1088. |
| 30 | BEDAIR R， PRIEST A N， PATTERSON A J， et al. Assessment of early treatment response to neoadjuvant chemotherapy in breast cancer using non-mono-exponential diffusion models： a feasibility study comparing the baseline and mid-treatment MRI examinations［J］. Eur Radiol， 2017，27（7）：2726-2736. |
| 31 | JENSEN J H， HELPERN J A， RAMANI A， et al. Diffusional kurtosis imaging： the quantification of non-gaussian water diffusion by means of magnetic resonance imaging［J］. Magn Reson Med， 2005，53（6）：1432-1440. |
| 32 | JENSEN J H， HELPERN J A. MRI quantification of non-Gaussian water diffusion by kurtosis analysis［J］. NMR Biomed， 2010，23（7）：698-710. |
| 33 | HONDA M， IIMA M， KATAOKA M， et al. Biomarkers Predictive of Distant Disease-free Survival Derived from Diffusion-weighted Imaging of Breast Cancer［J］. Magn Reson Med Sci， 2023，22（4）：469-476. |
| 34 | TANG W， ZHOU H， QUAN T， et al. XGboost Prediction Model Based on 3.0T Diffusion Kurtosis Imaging Improves the Diagnostic Accuracy of MRI BiRADS 4 Masses［J］. Front Oncol， 2022，12：833680. |
| 35 | HUANG Y， LIN Y， HU W， et al. Diffusion Kurtosis at 3.0T as an in vivo Imaging Marker for Breast Cancer Characterization： Correlation With Prognostic Factors［J］. J Magn Reson Imaging， 2019，49（3）：845-856. |
/
| 〈 |
|
〉 |