Breast Cancer: Early Prediction of Response to Neoadjuvant Chemotherapy Using Parametric Response Maps for MR Imaging

被引:78
作者
Cho, Nariya [1 ]
Im, Seock-Ah [2 ]
Park, In-Ae [3 ]
Lee, Kyung-Hun [2 ]
Li, Mulan [1 ]
Han, Wonshik [4 ]
Noh, Dong-Young [4 ]
Moon, Woo Kyung [1 ]
机构
[1] Seoul Natl Univ, Coll Med, Dept Radiol, Seoul 110744, South Korea
[2] Seoul Natl Univ, Coll Med, Dept Internal Med, Seoul 110744, South Korea
[3] Seoul Natl Univ, Coll Med, Dept Pathol, Seoul 110744, South Korea
[4] Seoul Natl Univ, Coll Med, Dept Surg, Seoul 110744, South Korea
关键词
SURGICAL ADJUVANT BREAST; CONTRAST-ENHANCED MRI; PREOPERATIVE CHEMOTHERAPY; PATHOLOGICAL RESPONSE; SURVIVAL; RECOMMENDATIONS; THERAPY; TRACER;
D O I
10.1148/radiol.14131332
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
Purpose: To prospectively compare the performance of dynamic contrast material-enhanced (DCE) magnetic resonance (MR) imaging using parametric response map (PRM) analysis with that using pharmacokinetic parameters (transfer constant [K-trans], rate constant [k(ep)], and relative extravascular extracellular space [v(e)]) in the early prediction of pathologic responses to neoadjuvant chemotherapy (NAC) in breast cancer patients. Materials and Methods: The institutional review board approved this study; informed consent was obtained. Between August 2010 and December 2012, 48 women (mean age, 46.4 years; range, 29-65 years) with breast cancer were enrolled and treated with an anthracycline-taxane regimen. DCE MR imaging was performed before and after the first cycle of chemotherapy, and the pathologic response was assessed after surgery. Tumor size and volume, PRM characteristics, and pharmacokinetic parameters (K-trans, k(ep), and v(e)) on MR images were assessed and compared according to the pathologic responses by using the Fisher exact test or the independent-sample t test. Results: Six of 48 (12%) patients showed pathologic complete response (CR) (pCR) and 42 (88%) showed nonpathologic CR (npCR). Thirty-eight (79%) patients showed a good response (Miller-Payne score of 3, 4, or 5), and 10 (21%) showed a minor response (Miller-Payne score of 1 or 2). The mean proportion of voxels with increased signal intensity (PRMSI+) in the pCR or good response group was significantly lower than that in the npCR or minor response group (14.0% +/- 6.5 vs 40.7% +/- 27.2, P < .001; 34.3% +/- 26.4 vs 52.8% +/- 24.9, P =.041). Area under the receiver operating characteristic curve for PRMSI+ in the pCR group was 0.770 (95% confidence interval: 0.626, 0.879), and that for the good response group was 0.716 (95% confidence interval: 0.567, 0.837). No difference in tumor size, tumor volume, or pharmacokinetic parameters was found between groups. Conclusion: PRM analysis of DCE MR images may enable the early identification of the pathologic response to NAC after the first cycle of chemotherapy, whereas pharmacokinetic parameters (K-trans, k(ep), and v(e)) do not. (C) RSNA, 2014
引用
收藏
页码:385 / 396
页数:12
相关论文
共 50 条
  • [41] Early prediction of neoadjuvant chemotherapy response for advanced breast cancer using PET/MRI image deep learning
    Choi, Joon Ho
    Kim, Hyun-Ah
    Kim, Wook
    Lim, Ilhan
    Lee, Inki
    Byun, Byung Hyun
    Noh, Woo Chul
    Seong, Min-Ki
    Lee, Seung-Sook
    Kim, Byung Il
    Choi, Chang Woon
    Lim, Sang Moo
    Woo, Sang-Keun
    SCIENTIFIC REPORTS, 2020, 10 (01)
  • [42] Role of the Intravoxel Incoherent Motion Diffusion Weighted Imaging in the Pre-treatment Prediction and Early Response Monitoring to Neoadjuvant Chemotherapy in Locally Advanced Breast Cancer
    Che, Shunan
    Zhao, Xinming
    Ou, Yanghan
    Li, Jing
    Wang, Meng
    Wu, Bing
    Zhou, Chunwu
    MEDICINE, 2016, 95 (04)
  • [43] Integrated 18F-FDG PET/MRI in breast cancer: early prediction of response to neoadjuvant chemotherapy
    Cho, Nariya
    Im, Seock-Ah
    Cheon, Gi Jeong
    Park, In-Ae
    Lee, Kyung-Hun
    Kim, Tae-Yong
    Kim, Young Seon
    Kwon, Bo Ra
    Lee, Jung Min
    Suh, Hoon Young
    Suh, Koung Jin
    EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING, 2018, 45 (03) : 328 - 339
  • [44] Predictors of Neoadjuvant Chemotherapy Response in Breast Cancer: A Review
    Xu, Weilin
    Chen, Xiu
    Deng, Fei
    Zhang, Jian
    Zhang, Wei
    Tang, Jinhai
    ONCOTARGETS AND THERAPY, 2020, 13 : 5887 - 5899
  • [45] Technetium-99m methoxyisobutylisonitrile scintimammography for monitoring and early prediction of breast cancer response to neoadjuvant chemotherapy
    Novikov, Sergey N.
    Kanaev, Sergey V.
    Petr, Krivorotko V.
    Tatyana, Semiglazova Y.
    Elena, Turkevich A.
    Ludmila, Jukova A.
    Nikolay, Ilin D.
    Zhanna, Bryanzeva V.
    Pavel, Krzhivitskii I.
    NUCLEAR MEDICINE COMMUNICATIONS, 2015, 36 (08) : 795 - 801
  • [46] Prediction of early clinical response to neoadjuvant chemotherapy in Triple-negative breast cancer: Incorporating Radiomics through breast MRI
    Lee, Hyo-jae
    Lee, Jeong Hoon
    Lee, Jong Eun
    Na, Yong Min
    Park, Min Ho
    Lee, Ji Shin
    Lim, Hyo Soon
    SCIENTIFIC REPORTS, 2024, 14 (01):
  • [47] On-treatment biomarkers can improve prediction of response to neoadjuvant chemotherapy in breast cancer
    Bownes, Richard J.
    Turnbull, Arran K.
    Martinez-Perez, Carlos
    Cameron, David A.
    Sims, Andrew H.
    Oikonomidou, Olga
    BREAST CANCER RESEARCH, 2019, 21 (1)
  • [48] Ultrasound-based prediction of pathologic response to neoadjuvant chemotherapy in breast cancer patients
    Baumgartner, Annina
    Tausch, Christoph
    Hosch, Stefanie
    Papassotiropoulos, Barbel
    Varga, Zsuzsanna
    Rageth, Christoph
    Baege, Astrid
    BREAST, 2018, 39 : 19 - 23
  • [49] Accuracy of multi-parametric breast MR imaging for predicting pathological complete response of operable breast cancer prior to neoadjuvant systemic therapy
    Tsukada, Hiroko
    Tsukada, Jitsuro
    Schrading, Simone
    Strobel, Kevin
    Okamoto, Takahiro
    Kuhl, Christiane K.
    MAGNETIC RESONANCE IMAGING, 2019, 62 : 242 - 248
  • [50] Early changes of platelet-lymphocyte ratio correlate with neoadjuvant chemotherapy response and predict pathological complete response in breast cancer
    Dan, Jiaqiang
    Tan, Jingya
    Huang, Junhua
    Yuan, Zhiying
    Guo, Yao
    MOLECULAR AND CLINICAL ONCOLOGY, 2023, 19 (05)