Discrimination between malignant and benign mass-like lesions from breast dynamic contrast enhanced MRI: semi-automatic vs. manual analysis of the signal time-intensity curves

被引:14
作者
Yin, Jiandong [1 ]
Yang, Jiawen [1 ]
Jiang, Zejun [2 ]
机构
[1] China Med Univ, Shengjing Hosp, Dept Radiol, Shenyang, Liaoning, Peoples R China
[2] Northeastern Univ, Sinodutch Biomed & Informat Engn Sch, Shenyang, Liaoning, Peoples R China
来源
JOURNAL OF CANCER | 2018年 / 9卷 / 05期
关键词
manual method; semi-automatic method; breast lesion; quantitative parameter; time intensity curve; COMPUTER-AIDED DIAGNOSIS; MAGNETIC-RESONANCE; NEOADJUVANT CHEMOTHERAPY; SPATIAL-RESOLUTION; CANCER; VARIABILITY; MAMMOGRAPHY; COMBINATION; PARAMETERS; CRITERIA;
D O I
10.7150/jca.23283
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
Purpose: To investigate the performance of a new semi-automatic method for analyzing the signal time-intensity curve (TIC) obtained by breast dynamic contrast enhancement (DCE)-MRI. Methods: In the conventional method, a circular region of interest was drawn manually onto the map reflecting the maximum slope of increase (MSI) to delineate the suspicious lesions. The mean TIC was determined subjectively as one of three different wash-out patterns. In the new method, the lesion area was identified semi-automatically. The mean TIC was categorized quantitatively. In addition to the MSI, other quantitative parameters were calculated, including the signal intensity slope (SIslope), initial percentage of enhancement (E-initial), percentage of peak enhancement (E-peak), early signal enhancement ratio (ESER), and second enhancement percentage (SEP). The performances were compared with receiver operating characteristic (ROC) analysis and Wilcoxon's test. Results: For TIC categorization results, the diagnostic accuracy rates were 61.54% with the traditional manual method and 76.92% with the new method. For the mean MSI values from the manual method, the accuracy was 63.41%. For the mean TIC derived using the semi-automatic method, the diagnostic accuracy were 82.05% for SIslope, 67.31% for MSI, 61.53% for E-initial, 64.75% for E-peak, 64.74% for ESER, and 52.56% for SEP, respectively. For the lesion regions identified by the semi-automatic method, the diagnostic accuracy for above mentioned parameters were 80.13%, 69.87%, 61.54%, 63.47%, 64.74% and 55.13%, respectively. Conclusion: With respect to the analysis of TIC from breast DCE-MRI, the results demonstrated that the new method increased the diagnostic accuracy, and should be considered as a supplementary tool for distinguishing benign and malignant lesions.
引用
收藏
页码:834 / 840
页数:7
相关论文
共 41 条
  • [11] The past, present and future of breast cancer research in China
    Hong, Wei
    Dong, Erdan
    [J]. CANCER LETTERS, 2014, 351 (01) : 1 - 5
  • [12] Detection of breast malignancy: Diagnostic MR protocol for improved specificity
    Huang, W
    Fisher, PR
    Dulaimy, K
    Tudorica, LA
    O'Hea, B
    Button, TM
    [J]. RADIOLOGY, 2004, 232 (02) : 585 - 591
  • [13] Normal parenchymal enhancement patterns in women undergoing MR screening of the breast
    Jansen, Sanaz A.
    Lin, Vicky C.
    Giger, Maryellen L.
    Li, Hui
    Karczmar, Gregory S.
    Newstead, Gillian M.
    [J]. EUROPEAN RADIOLOGY, 2011, 21 (07) : 1374 - 1382
  • [14] Enhancement parameters on dynamic contrast enhanced breast MRI: do they correlate with prognostic factors and subtypes of breast cancers?
    Kim, Ji Youn
    Kim, Sung Hun
    Kim, Yun Ju
    Kang, Bong Joo
    An, Yeong Yi
    Lee, A. Won
    Song, Byung Joo
    Park, Yong Soo
    Lee, Han Bi
    [J]. MAGNETIC RESONANCE IMAGING, 2015, 33 (01) : 72 - 80
  • [15] Dynamic high-spatial-resolution MR imaging of suspicious breast lesions: Diagnostic criteria and interobserver variability
    Kinkel, K
    Helbich, TH
    Esserman, LJ
    Barclay, J
    Schwerin, EH
    Sickles, EA
    Hylton, NM
    [J]. AMERICAN JOURNAL OF ROENTGENOLOGY, 2000, 175 (01) : 35 - 43
  • [16] Kuhl Christiane K, 2007, Magn Reson Imaging Clin N Am, V15, P315, DOI 10.1016/j.mric.2007.08.003
  • [17] Dynamic breast MR imaging: Are signal intensity time course data useful for differential diagnosis of enhancing lesions?
    Kuhl, CK
    Mielcareck, P
    Klaschik, S
    Leutner, C
    Wardelmann, E
    Gieseke, J
    Schild, HH
    [J]. RADIOLOGY, 1999, 211 (01) : 101 - 110
  • [18] MRI evaluation of the contralateral breast in women with recently diagnosed breast cancer
    Lehman, Constance D.
    Gatsonis, Constantine
    Kuhl, Christiane K.
    Hendrick, R. Edward
    Pisano, Etta D.
    Hanna, Lucy
    Peacock, Sue
    Smazal, Stanley F.
    Maki, Daniel D.
    Julian, Thomas B.
    DePeri, Elizabeth R.
    Bluemke, David A.
    Schnall, Mitchell D.
    Julian, T.
    Poller, W.
    Schilling, K.
    Neal, C.
    Wichterman, L.
    Seifert, P.
    O'Loughlin, M.
    Bluemke, D.
    Kawamoto, S.
    DePeri, E.
    Hendrick, E.
    Wolfman, J.
    Smazal, S.
    Thickman, D.
    Korn, R.
    Maki, D.
    Whitfill, C.
    Cook, A.
    Causer, P.
    Rao, V.
    Piccoli, C.
    Ferris, E.
    Harms, S.
    Kuhl, C.
    DeBruhl, N.
    Hylton, N.
    Mahoney, M.
    Pisano, E.
    Schnall, M.
    Weinstein, S.
    Keesara, S.
    Weatherall, P.
    DeAngelis, G.
    Lehman, C.
    Li, T.
    Soulen, R.
    [J]. NEW ENGLAND JOURNAL OF MEDICINE, 2007, 356 (13) : 1295 - 1303
  • [19] Semi-Automatic Region-of-Interest Segmentation Based Computer-Aided Diagnosis of Mass Lesions from Dynamic Contrast-Enhanced Magnetic Resonance Imaging Based Breast Cancer Screening
    Levman, Jacob
    Warner, Ellen
    Causer, Petrina
    Martel, Anne
    [J]. JOURNAL OF DIGITAL IMAGING, 2014, 27 (05) : 670 - 678
  • [20] DCE-MRI Analysis Methods for Predicting the Response of Breast Cancer to Neoadjuvant Chemotherapy: Pilot Study Findings
    Li, Xia
    Arlinghaus, Lori R.
    Ayers, Gregory D.
    Chakravarthy, A. Bapsi
    Abramson, Richard G.
    Abramson, Vandana G.
    Atuegwu, Nkiruka
    Farley, Jaime
    Mayer, Ingrid A.
    Kelley, Mark C.
    Meszoely, Ingrid M.
    Means-Powell, Julie
    Grau, Ana M.
    Sanders, Melinda
    Bhave, Sandeep R.
    Yankeelov, Thomas E.
    [J]. MAGNETIC RESONANCE IN MEDICINE, 2014, 71 (04) : 1592 - 1602