Relationship between DCE-MRI morphological and functional features and histopathological characteristics of breast cancer

被引:59
|
作者
Montemurro, Filippo
Martincich, Laura
Sarotto, Ivana
Bertotto, Ilaria
Ponzone, Riccardo
Cellini, Lisa
Redana, Stefania
Sismondi, Piero
Aglietta, Massimo
Regge, Daniele
机构
[1] Inst Canc Res & Treatment, Unit Diagnost Imaging, I-10060 Turin, Italy
[2] Inst Canc Res & Treatment, Med Oncol Unit, Turin, Italy
[3] Interchange Fdn, Inst Sci, Turin, Italy
[4] Inst Canc Res & Treatment, Unit Surg Pathol, Turin, Italy
[5] Inst Canc Res & Treatment, Unit Gynaecol Oncol, Turin, Italy
关键词
magnetic resonance imaging; contrast media; breast neoplasms; pathology; HER2;
D O I
10.1007/s00330-006-0505-x
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
We studied whether dynamic contrast-enhanced MRI (DCE-MRI) could identify histopathological characteristics of breast cancer. Seventy-five patients with breast cancer underwent DCE-MRI followed by core biopsy. DCE-MRI findings were evaluated following the scoring system published by Fischer in 1999. In this scoring system, five DCE-MRI features, three morphological (shape, margins, enhancement kinetic) and two functional (initial peak of signal intensity (SI) increase and behavior of signal intensity curve), are defined by 14 parameters. Each parameter is assigned points ranging from 0 to 1 or 0 to 2, with higher points for those that are more likely to be associated with malignancy. The sum of all the points defines the degree of suspicion of malignancy, with a score 0 representing the lowest and 8 the highest degree of suspicion. Associations between DCE-MRI features and tumor histopathological characteristics assessed on core biopsies (histological type, grading, estrogen and progesterone receptor status, Ki67 and HER2 status) were studied by contingency tables and logistic regression analysis. We found a significant inverse association between the Fischer's score and HER2-overexpression (odds ratio-OR 0.608, p = 0.02). Based on our results, we suggest that lesions with intermediate-low suspicious DCE-MRI parameters may represent a subset of tumor with poor histopathological characteristics.
引用
收藏
页码:1490 / 1497
页数:8
相关论文
共 50 条
  • [1] Relationship between DCE-MRI morphological and functional features and histopathological characteristics of breast cancer
    Filippo Montemurro
    Laura Martincich
    Ivana Sarotto
    Ilaria Bertotto
    Riccardo Ponzone
    Lisa Cellini
    Stefania Redana
    Piero Sismondi
    Massimo Aglietta
    Daniele Regge
    [J]. European Radiology, 2007, 17 : 1490 - 1497
  • [2] Spatiotemporal features of DCE-MRI for breast cancer diagnosis
    Banaie, Masood
    Soltanian-Zadeh, Hamid
    Saligheh-Rad, Hamid-Reza
    Gity, Masoumeh
    [J]. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, 2018, 155 : 153 - 164
  • [3] Evaluating the Relationship Between DynamicContrast-Enhanced MRI(DCE-MRI) Parameters and Pathological Characteristics in Breast Cancer
    Kang, Se Ri
    Kim, Hye Won
    Kim, Hun Soo
    [J]. JOURNAL OF MAGNETIC RESONANCE IMAGING, 2020, 52 (05) : 1360 - 1373
  • [4] Association of DW/DCE-MRI features with prognostic factors in breast cancer
    Shao, Guoliang
    Fan, Linyin
    Zhang, Juan
    Dai, Gang
    Xie, Tieming
    [J]. INTERNATIONAL JOURNAL OF BIOLOGICAL MARKERS, 2017, 32 (01): : E118 - E125
  • [5] Analysis of DCE-MRI Features in Tumor for Prediction of the Prognosis in Breast Cancer
    Liu, Bin
    Fan, Ming
    Zheng, Shuo
    Li, Lihua
    [J]. MEDICAL IMAGING 2019: IMAGING INFORMATICS FOR HEALTHCARE, RESEARCH, AND APPLICATIONS, 2019, 10954
  • [6] Relationship between histogram metrics of pharmacokinetic parameters of DCE-MRI and histological phenotype in breast cancer
    Yang, Guocai
    Yang, Jing
    Xu, Hui
    Zhang, Qingxin
    Qi, Yonghong
    Zhang, Aiju
    [J]. TRANSLATIONAL CANCER RESEARCH, 2020, 9 (01) : 30 - 41
  • [7] Breast cancer classification with mammography and DCE-MRI
    Yuan, Yading
    Giger, Maryellen L.
    Li, Hui
    Sennett, Charlene
    [J]. MEDICAL IMAGING 2009: COMPUTER-AIDED DIAGNOSIS, 2009, 7260
  • [8] Automated localization of breast cancer in DCE-MRI
    Gubern-Merida, Albert
    Marti, Robert
    Melendez, Jaime
    Hauth, Jakob L.
    Mann, Ritse M.
    Karssemeijer, Nico
    Platel, Bram
    [J]. MEDICAL IMAGE ANALYSIS, 2015, 20 (01) : 265 - 274
  • [9] Kinetic heterogeneity features on breast DCE-MRI as prognostic markers of breast cancer recurrence
    Mahrooghy, M.
    Ashraf, A. B.
    Gavenonis, S. C.
    Daye, D.
    Mies, C.
    Feldman, M.
    Rosen, M.
    Kontos, D.
    [J]. CANCER RESEARCH, 2013, 73
  • [10] Molecular subtypes classification of breast cancer in DCE-MRI using deep features
    Hasan, Ali M.
    Al-Waely, Noor K. N.
    Aljobouri, Hadeel K.
    Jalab, Hamid A.
    Ibrahim, Rabha W.
    Meziane, Farid
    [J]. EXPERT SYSTEMS WITH APPLICATIONS, 2024, 236