DOI QR코드

DOI QR Code

Intratumoral Heterogeneity of Breast Cancer Xenograft Models: Texture Analysis of Diffusion-Weighted MR Imaging

  • Yun, Bo La (Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine) ;
  • Cho, Nariya (Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine) ;
  • Li, Mulan (Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine) ;
  • Jang, Min Hye (Department of Pathology, Seoul National University Bundang Hospital) ;
  • Park, So Yeon (Department of Pathology, Seoul National University Bundang Hospital) ;
  • Kang, Ho Chul (Department of Computer Science and Engineering, Seoul National University) ;
  • Kim, Bohyoung (Department of Radiology, Seoul National University Bundang Hospital) ;
  • Song, In Chan (Department of Radiology, Seoul National University Bundang Hospital) ;
  • Moon, Woo Kyung (Department of Radiology, Seoul National University Bundang Hospital)
  • 투고 : 2014.03.20
  • 심사 : 2014.06.07
  • 발행 : 2014.09.01

초록

Objective: To investigate whether there is a relationship between texture analysis parameters of apparent diffusion coefficient (ADC) maps and histopathologic features of MCF-7 and MDA-MB-231 xenograft models. Materials and Methods: MCF-7 estradiol (+), MCF-7 estradiol (-), and MDA-MB-231 xenograft models were made with approval of the animal care committee. Twelve tumors of MCF-7 estradiol (+), 9 tumors of MCF-7 estradiol (-), and 6 tumors in MDA-MB-231 were included. Diffusion-weighted MR images were obtained on a 9.4-T system. An analysis of the first and second order texture analysis of ADC maps was performed. The texture analysis parameters and histopathologic features were compared among these groups by the analysis of variance test. Correlations between texture parameters and histopathologic features were analyzed. We also evaluated the intraobserver agreement in assessing the texture parameters. Results: MCF-7 estradiol (+) showed a higher standard deviation, maximum, skewness, and kurtosis of ADC values than MCF-7 estradiol (-) and MDA-MB-231 (p < 0.01 for all). The contrast of the MCF-7 groups was higher than that of the MDA-MB-231 (p = 0.004). The correlation (COR) of the texture analysis of MCF-7 groups was lower than that of MDA-MB-231 (p < 0.001). The histopathologic analysis showed that $Ki-67_{mean}$ and $Ki-67_{diff}$ of MCF-7 estradiol (+) were higher than that of MCF-7 estradiol (-) or MDA-MB-231 (p < 0.05). The microvessel density $(MVD)_{mean}$ and $MVD_{diff}$ of MDA-MB-231 were higher than those of MCF-7 groups (p < 0.001). A diffuse-multifocal necrosis was more frequently found in MDA-MB-231 (p < 0.001). The proportion of necrosis moderately correlated with the contrast (r = -0.438, p = 0.022) and strongly with COR (r = 0.540, p = 0.004). Standard deviation (r = 0.622, r = 0.437), skewness (r = 0.404, r = 0.484), and kurtosis (r = 0.408, r = 0.452) correlated with $Ki-67_{mean}$ and $Ki-67_{diff}$ (p < 0.05 for all). COR moderately correlated with $Ki-67_{diff}$ (r = -0.388, p = 0.045). Skewness (r = -0.643, r = -0.464), kurtosis (r = -0.581, r = -0.389), contrast (r = -0.473, r = -0.549) and COR (r = 0.588, r = 0.580) correlated with $MVD_{mean}$ and $MVD_{diff}$ (p < 0.05 for all). Conclusion: The texture analysis of ADC maps may help to determine the intratumoral spatial heterogeneity of necrosis patterns, amount of cellular proliferation and the vascularity in MCF-7 and MDA-MB-231 xenograft breast cancer models.

키워드

과제정보

연구 과제 주관 기관 : National Research Foundation of Korea (NRF), Seoul National University Hospital

참고문헌

  1. Perou CM, Sorlie T, Eisen MB, van de Rijn M, Jeffrey SS, Rees CA, et al. Molecular portraits of human breast tumours. Nature 2000;406:747-752 https://doi.org/10.1038/35021093
  2. Bedard PL, Hansen AR, Ratain MJ, Siu LL. Tumour heterogeneity in the clinic. Nature 2013;501:355-364 https://doi.org/10.1038/nature12627
  3. Koh DM, Collins DJ. Diffusion-weighted MRI in the body: applications and challenges in oncology. AJR Am J Roentgenol 2007;188:1622-1635 https://doi.org/10.2214/AJR.06.1403
  4. Choi BB, Kim SH, Kang BJ, Lee JH, Song BJ, Jeong SH, et al. Diffusion-weighted imaging and FDG PET/CT: predicting the prognoses with apparent diffusion coefficient values and maximum standardized uptake values in patients with invasive ductal carcinoma. World J Surg Oncol 2012;10:126 https://doi.org/10.1186/1477-7819-10-126
  5. Costantini M, Belli P, Distefano D, Bufi E, Matteo MD, Rinaldi P, et al. Magnetic resonance imaging features in triple-negative breast cancer: comparison with luminal and HER2-overexpressing tumors. Clin Breast Cancer 2012;12:331-339 https://doi.org/10.1016/j.clbc.2012.07.002
  6. Guo Y, Cai YQ, Cai ZL, Gao YG, An NY, Ma L, et al. Differentiation of clinically benign and malignant breast lesions using diffusion-weighted imaging. J Magn Reson Imaging 2002;16:172-178 https://doi.org/10.1002/jmri.10140
  7. Jeh SK, Kim SH, Kim HS, Kang BJ, Jeong SH, Yim HW, et al. Correlation of the apparent diffusion coefficient value and dynamic magnetic resonance imaging findings with prognostic factors in invasive ductal carcinoma. J Magn Reson Imaging 2011;33:102-109 https://doi.org/10.1002/jmri.22400
  8. Kamitani T, Matsuo Y, Yabuuchi H, Fujita N, Nagao M, Jinnouchi M, et al. Correlations between apparent diffusion coefficient values and prognostic factors of breast cancer. Magn Reson Med Sci 2013;12:193-199 https://doi.org/10.2463/mrms.2012-0095
  9. Park SH, Moon WK, Cho N, Song IC, Chang JM, Park IA, et al. Diffusion-weighted MR imaging: pretreatment prediction of response to neoadjuvant chemotherapy in patients with breast cancer. Radiology 2010;257:56-63 https://doi.org/10.1148/radiol.10092021
  10. Jung DC, Lee HJ, Seo JW, Park SY, Lee SJ, Lee JH, et al. Diffusion-weighted imaging of a prostate cancer xenograft model seen on a 7 Tesla animal MR scanner: comparison of ADC values and pathologic findings. Korean J Radiol 2012;13:82-89 https://doi.org/10.3348/kjr.2012.13.1.82
  11. Stephen RM, Pagel MD, Brown K, Baker AF, Meuillet EJ, Gillies RJ. Monitoring the development of xenograft triple-negative breast cancer models using diffusion-weighted magnetic resonance imaging. Exp Biol Med (Maywood) 2012;237:1273-1280 https://doi.org/10.1258/ebm.2012.011326
  12. Davnall F, Yip CS, Ljungqvist G, Selmi M, Ng F, Sanghera B, et al. Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice? Insights Imaging 2012;3:573-589 https://doi.org/10.1007/s13244-012-0196-6
  13. Karahaliou A, Vassiou K, Arikidis NS, Skiadopoulos S, Kanavou T, Costaridou L. Assessing heterogeneity of lesion enhancement kinetics in dynamic contrast-enhanced MRI for breast cancer diagnosis. Br J Radiol 2010;83:296-309 https://doi.org/10.1259/bjr/50743919
  14. Haralick RM, Shanmugam K, Dinstein IH. Textural features for image classification. IEEE Transactions on Systems, Man and Cybernetics 1973;SMC-3:610-621
  15. Chen W, Giger ML, Li H, Bick U, Newstead GM. Volumetric texture analysis of breast lesions on contrast-enhanced magnetic resonance images. Magn Reson Med 2007;58:562-571 https://doi.org/10.1002/mrm.21347
  16. Eary JF, O'Sullivan F, O'Sullivan J, Conrad EU. Spatial heterogeneity in sarcoma 18F-FDG uptake as a predictor of patient outcome. J Nucl Med 2008;49:1973-1979 https://doi.org/10.2967/jnumed.108.053397
  17. Gibbs P, Turnbull LW. Textural analysis of contrast-enhanced MR images of the breast. Magn Reson Med 2003;50:92-98 https://doi.org/10.1002/mrm.10496
  18. Henriksson E, Kjellen E, Wahlberg P, Ohlsson T, Wennerberg J, Brun E. 2-Deoxy-2-[18F] fluoro-D-glucose uptake and correlation to intratumoral heterogeneity. Anticancer Res 2007;27:2155-2159
  19. Sinha S, Lucas-Quesada FA, DeBruhl ND, Sayre J, Farria D, Gorczyca DP, et al. Multifeature analysis of Gd-enhanced MR images of breast lesions. J Magn Reson Imaging 1997;7:1016-1026 https://doi.org/10.1002/jmri.1880070613
  20. Tixier F, Le Rest CC, Hatt M, Albarghach N, Pradier O, Metges JP, et al. Intratumor heterogeneity characterized by textural features on baseline 18F-FDG PET images predicts response to concomitant radiochemotherapy in esophageal cancer. J Nucl Med 2011;52:369-378 https://doi.org/10.2967/jnumed.110.082404
  21. van Velden FH, Cheebsumon P, Yaqub M, Smit EF, Hoekstra OS, Lammertsma AA, et al. Evaluation of a cumulative SUV-volume histogram method for parameterizing heterogeneous intratumoural FDG uptake in non-small cell lung cancer PET studies. Eur J Nucl Med Mol Imaging 2011;38:1636-1647 https://doi.org/10.1007/s00259-011-1845-6
  22. Yu H, Caldwell C, Mah K, Poon I, Balogh J, MacKenzie R, et al. Automated radiation targeting in head-and-neck cancer using region-based texture analysis of PET and CT images. Int J Radiat Oncol Biol Phys 2009;75:618-625 https://doi.org/10.1016/j.ijrobp.2009.04.043
  23. Stejskal EO, Tanner JE. Spin diffusion measurements: spin echoes in the presence of a time-dependent field gradient. J Chem Phys 1965;42:288-292 https://doi.org/10.1063/1.1695690
  24. Albregtsen F. Statistical texture measures computed from gray level coocurrence matrices. Norway: Image Processing Laboratory Department of Informatics University of Oslo, 2008
  25. Huuse EM, Moestue SA, Lindholm EM, Bathen TF, Nalwoga H, Kruger K, et al. In vivo MRI and histopathological assessment of tumor microenvironment in luminal-like and basal-like breast cancer xenografts. J Magn Reson Imaging 2012;35:1098-1107 https://doi.org/10.1002/jmri.23507
  26. Zhu Z, Edwards RJ, Boobis AR. Increased expression of histone proteins during estrogen-mediated cell proliferation. Environ Health Perspect 2009;117:928-934 https://doi.org/10.1289/ehp.0800109
  27. Katzenellenbogen BS, Kendra KL, Norman MJ, Berthois Y. Proliferation, hormonal responsiveness, and estrogen receptor content of MCF-7 human breast cancer cells grown in the short-term and long-term absence of estrogens. Cancer Res 1987;47:4355-4360
  28. Downey K, Riches SF, Morgan VA, Giles SL, Attygalle AD, Ind TE, et al. Relationship between imaging biomarkers of stage I cervical cancer and poor-prognosis histologic features: quantitative histogram analysis of diffusion-weighted MR images. AJR Am J Roentgenol 2013;200:314-320 https://doi.org/10.2214/AJR.12.9545
  29. Kyriazi S, Collins DJ, Messiou C, Pennert K, Davidson RL, Giles SL, et al. Metastatic ovarian and primary peritoneal cancer: assessing chemotherapy response with diffusion-weighted MR imaging--value of histogram analysis of apparent diffusion coefficients. Radiology 2011;261:182-192 https://doi.org/10.1148/radiol.11110577
  30. Nelson DA, Tan TT, Rabson AB, Anderson D, Degenhardt K, White E. Hypoxia and defective apoptosis drive genomic instability and tumorigenesis. Genes Dev 2004;18:2095-2107 https://doi.org/10.1101/gad.1204904
  31. Bokacheva L, Ackerstaff E, LeKaye HC, Zakian K, Koutcher JA. High-field small animal magnetic resonance oncology studies. Phys Med Biol 2014;59:R65-R127 https://doi.org/10.1088/0031-9155/59/2/R65
  32. Padhani AR, Liu G, Koh DM, Chenevert TL, Thoeny HC, Takahara T, et al. Diffusion-weighted magnetic resonance imaging as a cancer biomarker: consensus and recommendations. Neoplasia 2009;11:102-125 https://doi.org/10.1593/neo.81328
  33. Barrett T, Gill AB, Kataoka MY, Priest AN, Joubert I, McLean MA, et al. DCE and DW MRI in monitoring response to androgen deprivation therapy in patients with prostate cancer: a feasibility study. Magn Reson Med 2012;67:778-785 https://doi.org/10.1002/mrm.23062
  34. Vossen JA, Buijs M, Geschwind JF, Liapi E, Prieto Ventura V, Lee KH, et al. Diffusion-weighted and Gd-EOB-DTPA-contrast-enhanced magnetic resonance imaging for characterization of tumor necrosis in an animal model. J Comput Assist Tomogr 2009;33:626-630 https://doi.org/10.1097/RCT.0b013e3181953df3
  35. Wagner M, Maggiori L, Ronot M, Paradis V, Vilgrain V, Panis Y, et al. Diffusion-weighted and T2-weighted MR imaging for colorectal liver metastases detection in a rat model at 7 T: a comparative study using histological examination as reference. Eur Radiol 2013;23:2156-2164 https://doi.org/10.1007/s00330-013-2789-y
  36. Chen YW, Pan HB, Tseng HH, Chu HC, Hung YT, Yen YC, et al. Differentiated epithelial- and mesenchymal-like phenotypes in subcutaneous mouse xenografts using diffusion weighted-magnetic resonance imaging. Int J Mol Sci 2013;14:21943-21959 https://doi.org/10.3390/ijms141121943
  37. Kim SH, Cha ES, Kim HS, Kang BJ, Choi JJ, Jung JH, et al. Diffusion-weighted imaging of breast cancer: correlation of the apparent diffusion coefficient value with prognostic factors. J Magn Reson Imaging 2009;30:615-620 https://doi.org/10.1002/jmri.21884
  38. Razek AA, Gaballa G, Denewer A, Nada N. Invasive ductal carcinoma: correlation of apparent diffusion coefficient value with pathological prognostic factors. NMR Biomed 2010;23:619-623 https://doi.org/10.1002/nbm.1503
  39. Nowell PC. The clonal evolution of tumor cell populations. Science 1976;194:23-28 https://doi.org/10.1126/science.959840
  40. Le Bihan D, Breton E, Lallemand D, Grenier P, Cabanis E, Laval-Jeantet M. MR imaging of intravoxel incoherent motions: application to diffusion and perfusion in neurologic disorders. Radiology 1986;161:401-407 https://doi.org/10.1148/radiology.161.2.3763909
  41. Liu C, Liang C, Liu Z, Zhang S, Huang B. Intravoxel incoherent motion (IVIM) in evaluation of breast lesions: comparison with conventional DWI. Eur J Radiol 2013;82:e782-e789 https://doi.org/10.1016/j.ejrad.2013.08.006
  42. Doblas S, Wagner M, Leitao HS, Daire JL, Sinkus R, Vilgrain V, et al. Determination of malignancy and characterization of hepatic tumor type with diffusion-weighted magnetic resonance imaging: comparison of apparent diffusion coefficient and intravoxel incoherent motion-derived measurements. Invest Radiol 2013;48:722-728 https://doi.org/10.1097/RLI.0b013e3182915912
  43. Sigmund EE, Cho GY, Kim S, Finn M, Moccaldi M, Jensen JH, et al. Intravoxel incoherent motion imaging of tumor microenvironment in locally advanced breast cancer. Magn Reson Med 2011;65:1437-1447 https://doi.org/10.1002/mrm.22740
  44. Tamura T, Usui S, Murakami S, Arihiro K, Akiyama Y, Naito K, et al. Biexponential Signal Attenuation Analysis of Diffusion-weighted Imaging of Breast. Magn Reson Med Sci 2010;9:195-207 https://doi.org/10.2463/mrms.9.195

피인용 문헌

  1. Texture features on T2-weighted magnetic resonance imaging: new potential biomarkers for prostate cancer aggressiveness vol.60, pp.7, 2014, https://doi.org/10.1088/0031-9155/60/7/2685
  2. Progress in the clinical detection of heterogeneity in breast cancer vol.5, pp.12, 2014, https://doi.org/10.1002/cam4.943
  3. Free-breathing 3D diffusion MRI for high-resolution hepatic metastasis characterization in small animals vol.33, pp.2, 2016, https://doi.org/10.1007/s10585-015-9766-6
  4. Hyaluronan-conjugated liposomes encapsulating gemcitabine for breast cancer stem cells vol.11, pp.None, 2014, https://doi.org/10.2147/ijn.s95850
  5. Temporal Changes of Texture Features Extracted From Pulmonary Nodules on Dynamic Contrast-Enhanced Chest Computed Tomography: How Influential Is the Scan Delay? vol.51, pp.9, 2016, https://doi.org/10.1097/rli.0000000000000267
  6. Biomarkers from in vivo molecular imaging of breast cancer: pretreatment 18 F-FDG PET predicts patient prognosis, and pretreatment DWI-MR predicts response to neoadjuvant chemotherapy vol.30, pp.4, 2014, https://doi.org/10.1007/s10334-017-0610-7
  7. Texture Analysis of Torn Rotator Cuff on Preoperative Magnetic Resonance Arthrography as a Predictor of Postoperative Tendon Status vol.18, pp.4, 2014, https://doi.org/10.3348/kjr.2017.18.4.691
  8. Selection and Reporting of Statistical Methods to Assess Reliability of a Diagnostic Test: Conformity to Recommended Methods in a Peer-Reviewed Journal vol.18, pp.6, 2017, https://doi.org/10.3348/kjr.2017.18.6.888
  9. Differentiation of malignant and benign breast lesions: Added value of the qualitative analysis of breast lesions on diffusion-weighted imaging (DWI) using readout-segmented echo-planar imaging at 3.0 vol.12, pp.3, 2014, https://doi.org/10.1371/journal.pone.0174681
  10. MRI texture analysis in differentiating luminal A and luminal B breast cancer molecular subtypes - a feasibility study vol.17, pp.None, 2014, https://doi.org/10.1186/s12880-017-0239-z
  11. Could texture features from preoperative CT image be used for predicting occult peritoneal carcinomatosis in patients with advanced gastric cancer? vol.13, pp.3, 2014, https://doi.org/10.1371/journal.pone.0194755
  12. An MRI-based Radiomics Classifier for Preoperative Prediction of Ki-67 Status in Breast Cancer vol.25, pp.9, 2018, https://doi.org/10.1016/j.acra.2018.01.006
  13. Quantitative imaging of cancer in the postgenomic era: Radio(geno)mics, deep learning, and habitats vol.124, pp.24, 2014, https://doi.org/10.1002/cncr.31630
  14. Evaluation of the Impact of Iterative Reconstruction Algorithms on Computed Tomography Texture Features of the Liver Parenchyma Using the Filtration-Histogram Method vol.20, pp.4, 2014, https://doi.org/10.3348/kjr.2018.0368
  15. Predicting neo‐adjuvant chemotherapy response and progression‐free survival of locally advanced breast cancer using textural features of intratumoral heterogeneity on F‐18 FDG PET/CT vol.25, pp.3, 2014, https://doi.org/10.1111/tbj.13032
  16. Spatial heterogeneity of oxygenation and haemodynamics in breast cancer resolved in vivo by conical multispectral optoacoustic mesoscopy vol.9, pp.1, 2020, https://doi.org/10.1038/s41377-020-0295-y
  17. Is the standard deviation of the apparent diffusion coefficient a potential tool for the preoperative prediction of tumor grade in endometrial cancer? vol.61, pp.12, 2020, https://doi.org/10.1177/0284185120915596