Correlation of diffusion MRI with the Ki-67 index in non-small cell lung cancer

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Abstract

Background. The primary objective of the study was to evaluate the association between the minimum apparent diffusion coefficient (ADCmin) and Ki-67, an index for cellular proliferation, in non-small cell lung cancers. Also, we aimed to assess whether ADCmin values differ between tumour subtypes and tissue sampling method.

Methods. The patients who had diffusion weighted magnetic resonance imaging (DW-MRI) were enrolled retrospectively. The correlation between ADCmin and the Ki-67 index was evaluated.

Results. Ninety three patients, with a mean age 65 ± 11 years, with histopathologically proven adenocarcinoma and squamous cell carcinoma of the lungs and had technically successful DW-MRI were included in the study. The numbers of tumour subtypes were 47 for adenocarcinoma and 46 for squamous cell carcinoma. There was a good negative correlation between ADCmin values and the Ki-67 proliferation index (r = −0.837, p < 0.001). The mean ADCmin value was higher and the mean Ki-67 index was lower in adenocarcinomas compared to squamous cell carcinoma (p < 0.0001). There was no statistical difference between tissue sampling methods.

Conclusions. Because ADCmin shows a good but negative correlation with Ki-67 index, it provides an opportunity to evaluate tumours and their aggressiveness and may be helpful in the differentiation of subtypes non-invasively.

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  • 1. Yabuuchi H Hatakenaka M Takayama K Matsuo Y Sunami S Kamitani T et al. Non-small cell lung cancer: detection of early response to chemotherapy by using contrast-enhanced dynamic and diffusion-weighted MR imaging. Radiology 2011; 26: 598-604.

  • 2. Wang Y Chen ZE Yaghmai V Nikolaidis P McCarthy RJ Merrick L et al. Diffusion-weighted MR imaging in pancreatic endocrine tumors correlated with histopathologic characteristics. J Magn Reson Imaging 2011; 33: 1071-9.

  • 3. Zhang J Cui LB Tang X Ren XL Shi JR Yang HN et al. DW MRI at 3.0 T versus FDG PET/CT for detection of malignant pulmonary tumors. Int J Cancer 2014; 134: 606-11.

  • 4. Li F Yu T Li W Zhang C Cao Y Su D et al. Correlation of apparent diffusion coefficient with histologic type and grade of lung cancer. Zhongguo Fei Ai Za Zhi 2012; 15: 646-51.

  • 5. Xu L Tian J Liu Y Li C. Accuracy of diffusion-weighted (DW) MRI with background signal suppression (MR-DWIBS) in diagnosis of mediastinal lymph node metastasis of nonsmall-cell lung cancer (NSCLC). J Magn Reson Imaging 2014; 40: 200-5.

  • 6. Usuda K Sagawa M Motono N Ueno M Tanaka M Machida Y et al. Diagnostic performance of diffusion weighted imaging of malignant and benign pulmonary nodules and masses: comparison with positron emission tomography. Asian Pac J Cancer Prev 2014; 15: 4629-35.

  • 7. Türkbey B Aras Ö Karabulut N Turgut AT Akpinar E Alibek S et al. Diffusion-weighted MRI for detecting and monitoring cancer: a review of current applications in body imaging. Diagn Interv Radiol 2012; 18: 46-59.

  • 8. Koh DM Collins DJ. Diffusion-weighted MRI in the body: applications and challenges in oncology. AJR Am J Roentgenol 2007; 188: 1622-35.

  • 9. 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: 102-25.

  • 10. Scholzen T Gerdes J. The Ki-67 protein: from the known and the unknown. J Cell Physiol 2000; 182: 311-22.

  • 11. Raĭkhlin NT Bukaeva IA Smirnova EA Gurevich LE Delektorskaia VV Polotskiĭ BE et al. Significance of the expression of nucleolar argyrophilic proteins and antigen Ki-67 in the evaluation of cell proliferative activity and in the prediction of minimal (T1) lung cancer. Arkh Patol 2008; 70: 15-18.

  • 12. Gerdes J Lemke H Baisch H Wacker HH Schwab U Stein H. Cell cycle analysis of a cell proliferation-associated human nuclear antigen defined by the monoclonal antibody Ki-67. J Immunol 1984; 133: 1710-15.

  • 13. Zhu L Ren G Li K Liang ZH Tang WJ Ji YM et al. Pineal parenchymal tumours: minimum apparent diffusion coefficient in prediction of tumour grading. J Int Med Res 2011; 39: 1456-63.

  • 14. Choi SY Chang YW Park HJ Kim HJ Hong SS Seo DY. Correlation of the apparent diffusion coefficiency values on diffusion-weighted imaging with prognostic factors for breast cancer. Br J Radiol 2012; 85(1016): e474-9.

  • 15. Onishi N Kanao S Kataoka M Iima M Sakaguchi R Kawai M et al. Apparent diffusion coefficient as a potential surrogate marker for Ki-67 index in mucinous breast carcinoma J Magn Reson Imaging 2015; 41: 610-5.

  • 16. Mesko S Kupelian P Demanes DJ Huang J Wang PC Kamrava M. Quantifying the ki-67 heterogeneity profile in prostate cancer. Prostate Cancer 2013: 2013: 717080.

  • 17. Kobayashi S Koga F Kajino K Yoshita S Ishii C Tanaka H et al. Apparent diffusion coefficient value reflects invasive and proliferative potential of bladder cancer. J Magn Reson Imaging 2014; 39: 172-8.

  • 18. Tang Y Dundamadappa SK Thangasamy S Flood T Moser R Smith T et al. Correlation of apparent diffusion coefficient with Ki-67 proliferation index in grading meningioma. AJR Am J Roentgenol 2014; 202: 1303-8.

  • 19. Martin B Paesmans M Mascaux C Berghmans T Lothaire P Meert AP et al. Ki-67 expression and patients survival in lung cancer: systematic review of the literature with meta-analysis. Br J Cancer 2004; 91: 2018-25.

  • 20. Usuda K Zhao XT Sagawa M Aikawa H Ueno M Tanaka M et al. Diffusion-weighted imaging (DWI) signal intensity and distribution represent the amount of cancer cells and their distribution in primary lung cancer. Clin Imaging 2013; 37: 265-72.

  • 21. Ohno Y Koyama H Yoshikawa T Matsumoto K Aoyama N Onishi Y et al. Diffusion-weighted MRI versus 18F-FDG PET/CT: performance as predictors of tumor treatment response and patient survival in patients with nonsmall cell lung cancer receiving chemoradiotherapy. AJR Am J Roentgenol 2012; 198: 75-82.

  • 22. Tanaka R Horikoshi H Nakazato Y Seki E Minato K Iijima M et al. Magnetic resonance imaging in peripheral lung adenocarcinoma: correlation with histopathologic features. J Thorac Imaging 2009; 24: 4-9.

  • 23. Matoba M Tonami H Kondou T Yokota H Higashi K Toga H et al. Lung carcinoma: diffusion-weighted MR imaging—preliminary evaluation with apparent diffusion coefficient. Radiology 2007; 243: 570-7.

  • 24. Martin B Paesmans M Mascaux C Berghmans T Lothaire P Meert AP et al. Ki-67 expression and patients survival in lung cancer: systematic review of the literature with meta-analysis. Br J Cancer 2004; 91: 2018-25.

  • 25. Warth A Cortis J Soltermann A Meister M Budczies J Stenzinger A et al. Tumour cell proliferation (Ki-67) in non-small cell lung cancer: a critical reappraisal of its prognostic role. Br J Cancer 2014; 111: 1222-9.

  • 26. Tabata K Tanaka T Hayashi T Hori T Nunomura S Yonezawa S et al. Ki-67 is a strong prognostic marker of non-small cell lung cancer when tissue heterogeneity is considered. BMC Clin Pathol 2014; 14: 23-30.

  • 27. Ahn HK Jung M Ha SY Lee JI Park I Kim YS et al. Clinical significance of Ki-67 and p53 expression in curatively resected non-small cell lung cancer. Tumour Biol 2014; 35: 5735-40.

  • 28. Alper F Kurt AT Aydin Y Ozgokce M Akgun M. The role of dynamic magnetic resonance imaging in the evaluation of pulmonary nodules and masses. Med Princ Pract 2013; 22: 80-6.

  • 29. Karaman A Kahraman M Bozdoğan E Alper F Akgün M. Diffusion magnetic resonance imaging of thorax. Tuberk Toraks 2014; 62: 215-30.

  • 30. Araz O Demirci E Ucar EY Calik M Karaman A Durur-Subasi I et al. Roles of Ki-67 p53 transforming growth factor-β and lysyl oxidase in the metastasis of lung cancer. Respirology 2014; 19: 1034-9.

  • 31. Siegel R Naishadham D Jemal A. Cancer statistics 2013. CA Cancer J Clin 2013; 63: 11-30.

  • 32. Zhang Z Zhou Y Qian H Shao G Lu X Chen Q et al. Stemness and inducing differentiation of small cell lung cancer NCI-H446 cells. Cell Death Dis 2013; 16: e633.

  • 33. Schaarschmidt BM Buchbender C Nensa F Grueneien J Gomez B Köhler J et al. Correlation of the apparent diffusion coefficient (ADC) with the standardized uptake value (SUV) in lymph node metastases of non-small cell lung cancer (NSCLC) patients using hybrid 18F-FDG PET/MRI. PLoS One 2015; 10(1): e0116277.

  • 34. Yoshida S Kobayashi S Koga F Ishioka J Ishii C Tanaka H et al. Apparent diffusion coefficient as a prognostic biomarker of upper urinary tract cancer: a preliminary report. Eur Radiol 2013; 23: 2206-14.

  • 35. Yoshida S Koga F Kobayashi S Ishii C Tanaka H Tanaka H et al. Role of diffusion weighted magnetic resonance imaging in predicting sensitivity to chemoradiotherapy in muscle-invasive bladder cancer. Int J Radiat Oncol Biol Phys 2012; 83: e21-e7.

  • 36. Wieduwilt MJ Valles F Issa S Behler CM Hwang J McDermott M et al. Immunochemotherapy with intensive consolidation for primary CNS lymphoma: a pilot study and prognostic assessment by diffusion-weighted MRI. Clin Cancer Res 2012; 18: 1146-55.

  • 37. Srinivasan A Chenevert TL Dwamena BA Eisbruch A Watcharotone K Myles JD et al. Utility of pretreatment mean apparent diffusion coefficient and apparent diffusion coefficient histograms in prediction of outcome to chemoradiation in head and neck squamous cell carcinoma. J Comput Assist Tomogr 2012; 36: 131-7.

  • 38. Pope WB Lai A Mehta R Qiao J Young JR Xue X et al. Apparent diffusion coefficient histogram analysis stratifies progression-free survival in newly diagnosed bevacizumab-treated glioblastoma. AJNR Am J Neuroradiol 2011; 32: 882-9.

  • 39. Kıvrak AS Paksoy Y Erol C Koplay M Özbek S Kara F. Comparison of apparent diffusion coefficient values among different MRI platforms: a multi-center phantom study. Diagn Interv Radiol 2013; 19: 433-7.

  • 40. Rosenkrantz AB Oei M Babb JS Niver BE Taouli B. Diffusion-weighted imaging of the abdomen at 3.0 Tesla: image quality and apparent diffusion coefficient reproducibility compared with 1.5 Tesla. J Magn Reson Imaging 2011; 33: 128-35.

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