リケラボ論文検索は、全国の大学リポジトリにある学位論文・教授論文を一括検索できる論文検索サービスです。

リケラボ 全国の大学リポジトリにある学位論文・教授論文を一括検索するならリケラボ論文検索大学・研究所にある論文を検索できる

リケラボ 全国の大学リポジトリにある学位論文・教授論文を一括検索するならリケラボ論文検索大学・研究所にある論文を検索できる

大学・研究所にある論文を検索できる 「Kinetic volume analysis on dynamic contrast-enhanced MRI of triple-negative breast cancer: associations with survival outcomes」の論文概要。リケラボ論文検索は、全国の大学リポジトリにある学位論文・教授論文を一括検索できる論文検索サービスです。
リケラボ

コピーが完了しました

URLをコピーしました

論文の公開元へ論文の公開元へ
書き出し

Kinetic volume analysis on dynamic contrast-enhanced MRI of triple-negative breast cancer: associations with survival outcomes

HAYASHI, YOKO 林, 葉子 名古屋大学

2020.05.14

概要

Objective: To evaluate the associations between computer-aided diagnosis (CAD)-generated kinetic volume parameters and survival in triple-negative breast cancer (TNBC) patients.

Methods: 40 patients with TNBC who underwent preoperative MRI between March 2008 and March 2014 were included. We analyzed CAD-generated parameters on dynamic contrast-enhanced MRI, visual MRI assessment, and histopathological data. Cox proportional hazards models were used to determine associations with survival outcomes.

Results: 12 of the 40 (30.0%) patients experienced recurrence and 7 died of breast cancer after a median follow-up of 73.6 months. In multivariate analysis, higher percentage volume (%V) with more than 200% initial enhancement rate correlated with worse diseasespecific survival (hazard ratio, 1.12; 95% confidence interval, 1.02–1.22; p-value, 0.014) and higher %V with more than 100% initial enhancement rate followed by persistent curve type at 30% threshold correlated with worse disease-specific survival (hazard ratio, 1.33; 95% confidence interval, 1.10–1.61; p-value, 0.004) and disease-free survival (hazard ratio, 1.27; 95% confidence interval, 1.12–1.43; p-value, 0.000).

Conclusion: CAD-generated kinetic volume parameters may correlate with survival in TNBC patients. Further study would be necessary to validate our results on larger cohorts.

Advances in knowledge: CAD generated kinetic volume parameters on breast MRI can predict recurrence and survival outcome of patients in TNBC. Varying the enhancement threshold improved the predictive performance of CAD generated kinetic volume parameter.

論文の公開元へ

この論文で使われている画像

関連論文

関連論文をもっと見る

参考文献

1. Moran MS. Should triple-negative breast cancer (TNBC) subtype affect local-regional therapy decision making? Am Soc Clin Oncol Educ Book 2014; 34: e32–6. doi: https://doi. org/10.14694/EdBook_AM.2014.34.e32

2. Irshad S, Ellis P, Tutt A. Molecular heterogeneity of triple-negative breast cancer and its clinical implications. Curr Opin Oncol 2011; 23: 566–77. doi: https://doi.org/10. 1097/CCO.0b013e32834bf8ae

3. Pistelli M, Pagliacci A, Battelli N, Santinelli A, Biscotti T, Ballatore Z, et al. Prognostic factors in early-stage triple-negative breast cancer: lessons and limits from clinical practice. Anticancer Res 2013; 33: 2737–42.

4. Dent R, Trudeau M, Pritchard KI, Hanna WM, Kahn HK, Sawka CA, et al. TripleNegative breast cancer: clinical features and patterns of recurrence. Clin Cancer Res 2007; 13(15 Pt 1): 4429–34. doi: https://doi.org/10. 1158/1078-0432.CCR-06-3045

5. Liedtke C, Mazouni C, Hess KR, André F, Tordai A, Mejia JA, et al. Response to neoadjuvant therapy and long-term survival in patients with triple-negative breast cancer. JCO 2008; 26: 1275–81. doi: https://doi.org/ 10.1200/JCO.2007.14.4147

6. von Minckwitz G, Untch M, Blohmer J-U, Costa SD, Eidtmann H, Fasching PA, et al. Definition and impact of pathologic complete response on prognosis after neoadjuvant chemotherapy in various intrinsic breast cancer subtypes. JCO 2012; 30: 1796–804. doi: https://doi.org/10.1200/ JCO.2011.38.8595

7. Hernandez-Aya LF, Chavez-MacGregor M, Lei X, Meric-Bernstam F, Buchholz TA, Hsu L, et al. Nodal status and clinical outcomes in a large cohort of patients with triple-negative breast cancer. JCO 2011; 29: 2628–34. doi: https://doi.org/10.1200/JCO.2010.32.1877

8. Pogoda K, Niwińska A, Murawska M, Pieńkowski T. Analysis of pattern, time and risk factors influencing recurrence in triplenegative breast cancer patients. Med Oncol 2013; 30: 388. doi: https://doi.org/10.1007/ s12032-012-0388-4

9. Steward L, Conant L, Gao F, Margenthaler JA. Predictive factors and patterns of recurrence in patients with triple negative breast cancer. Ann Surg Oncol 2014; 21: 2165–71. doi: https://doi.org/10.1245/ s10434-014-3546-4

10. Loibl S, Müller BM, von Minckwitz G, Schwabe M, Roller M, Darb-Esfahani S, et al. Androgen receptor expression in primary breast cancer and its predictive and prognostic value in patients treated with neoadjuvant chemotherapy. Breast Cancer Res Treat 2011; 130: 477–87. doi: https://doi. org/10.1007/s10549-011-1715-8

11. Pistelli M, Caramanti M, Biscotti T, Santinelli A, Pagliacci A, De Lisa M, et al. Androgen receptor expression in early triple-negative breast cancer: clinical significance and prognostic associations. Cancers 2014; 6: 1351–62. doi: https://doi.org/10.3390/ cancers6031351

12. Korkaya H, Liu S, Wicha MS. Breast cancer stem cells, cytokine networks, and the tumor microenvironment. J Clin Invest 2011; 121: 3804–9. doi: https://doi.org/10.1172/ JCI57099

13. de Kruijf EM, van Nes JGH, van de Velde CJH, Putter H, Smit VTHBM, Liefers GJ, et al. Tumor-Stroma ratio in the primary tumor is a prognostic factor in early breast cancer patients, especially in triple-negative carcinoma patients. Breast Cancer Res Treat 2011; 125: 687–96. doi: https://doi.org/10. 1007/s10549-010-0855-6

14. Dekker TJA, van de Velde CJH, van Pelt GW, Kroep JR, Julien J-P, Smit VTHBM, et al. Prognostic significance of the tumor-stroma ratio: validation study in node-negative premenopausal breast cancer patients from the EORTC perioperative chemotherapy (POP) trial (10854. Breast Cancer Res Treat 2013; 139: 371–9. doi: https://doi.org/10. 1007/s10549-013-2571-5

15. Moorman AM, Vink R, Heijmans HJ, van der Palen J, Kouwenhoven EA. The prognostic value of tumour-stroma ratio in triple-negative breast cancer. Eur J Surg Oncol 2012; 38: 307–13. doi: https://doi.org/ 10.1016/j.ejso.2012.01.002

16. Kramer CJH, Vangangelt KMH, van Pelt GW, Dekker TJA, Tollenaar RAEM, Mesker WE, et al. The prognostic value of tumourstroma ratio in primary breast cancer with special attention to triple-negative tumours: a review. Breast Cancer Res Treat 2019; 173: 55–64. doi: https://doi.org/10.1007/s10549- 018-4987-4

17. Park VY, Kim E-K, Kim MJ, Yoon JH, Moon HJ. Perfusion parameters on breast dynamic contrast-enhanced MRI are associated with disease-specific survival in patients with triple-negative breast cancer. AJR Am J Roentgenol 2017; 208: 687–94. doi: https:// doi.org/10.2214/AJR.16.16476

18. Nagasaka K, Satake H, Satoko Ishigaki ·, et al. Histogram analysis of quantitative pharmacokinetic parameters on DCE-MRI: correlations with prognostic factors and molecular subtypes in breast cancer. Breast Cancer 2018; 0: 3.

19. Pickles MD, Lowry M, Manton DJ, Turnbull LW. Prognostic value of DCE-MRI in breast cancer patients undergoing neoadjuvant chemotherapy: a comparison with traditional survival indicators. Eur Radiol 2015; 25: 1097–106. doi: https://doi.org/10.1007/ s00330-014-3502-5

20. Blaschke E, Abe H. Mri phenotype of breast cancer: kinetic assessment for molecular subtypes. J Magn Reson Imaging 2015; 42: 920–4. doi: https://doi.org/10.1002/jmri. 24884

21. Li SP, Padhani AR, Taylor NJ, Beresford MJ, Ah-See M-LW, Stirling JJ, et al. Vascular characterisation of triple negative breast carcinomas using dynamic MRI. Eur Radiol 2011; 21: 1364–73. doi: https://doi.org/10. 1007/s00330-011-2061-2

22. Koo HR, Cho N, Song IC, Kim H, Chang JM, Yi A, et al. Correlation of perfusion parameters on dynamic contrast-enhanced MRI with prognostic factors and subtypes of breast cancers. J Magn Reson Imaging 2012; 36: 145–51. doi: https://doi.org/10.1002/jmri. 23635

23. Yamaguchi K, Hara Y, Kitano I, Hamamoto T, Kiyomatsu K, Yamasaki F, et al. TumorStromal ratio (Tsr) of invasive breast cancer: correlation with multi-parametric breast MRI findings. Br J Radiol 2019; 92: 20181032. doi: https://doi.org/10.1259/bjr. 20181032

24. Chen W, Giger ML, Lan L, Bick U. Computerized interpretation of breast MRI: investigation of enhancement-variance dynamics. Med Phys 2004; 31: 1076–82. doi: https://doi.org/10.1118/1.1695652

25. Wood C. Computer aided detection (CAD) for breast MRI. Technol Cancer Res Treat 2005; 4: 49–53. doi: https://doi.org/10.1177/ 153303460500400107

26. Kim JJ, Kim JY, Kang HJ, Shin JK, Kang T, Lee SW, et al. Computer-Aided Diagnosisgenerated kinetic features of breast cancer at preoperative MR imaging: association with disease-free survival of patients with primary operable invasive breast cancer. Radiology 2017; 284: 45–54. doi: https://doi.org/10. 1148/radiol.2017162079

27. Nam SY, Ko ES, Lim Y, Han B-K, Ko EY, Choi JS, et al. Preoperative dynamic breast magnetic resonance imaging kinetic features using computer-aided diagnosis: association with survival outcome and tumor aggressiveness in patients with invasive breast cancer. PLoS One 2018; 13: e0195756. doi: https://doi.org/10.1371/journal.pone. 0195756

28. Dietzel M, Zoubi R, Vag T, Gajda M, Runnebaum IB, Kaiser WA, et al. Association between survival in patients with primary invasive breast cancer and computer aided MRI. J Magn Reson Imaging 2013; 37: 146–55. doi: https://doi.org/10.1002/jmri. 23812

29. Jafri NF, Newitt DC, Kornak J, Esserman LJ, Joe BN, Hylton NM, et al. Optimized breast MRI functional tumor volume as a biomarker of recurrence-free survival following neoadjuvant chemotherapy. J Magn Reson Imaging 2014; 40: 476–82. doi: https:// doi.org/10.1002/jmri.24351

30. Hylton NM, Gatsonis CA, Rosen MA, Lehman CD, Newitt DC, Partridge SC, et al. Neoadjuvant chemotherapy for breast cancer: functional tumor volume by MR imaging predicts recurrence-free Survival-Results from the ACRIN 6657/CALGB 150007 I-SPY 1 trial. Radiology 2016; 279: 44–55. doi: https://doi.org/10.1148/radiol.2015150013

31. Lo W-C, Li W, Jones EF, Newitt DC, Kornak J, Wilmes LJ, et al. Effect of imaging parameter thresholds on MRI prediction of neoadjuvant chemotherapy response in breast cancer subtypes. PLoS One 2016; 11: e0142047. doi: https://doi.org/10.1371/ journal.pone.0142047

32. Hammond MEH, Hayes DF, Dowsett M, Allred DC, Hagerty KL, Badve S, et al. American Society of clinical Oncology/ College of American pathologists guideline recommendations for immunohistochemical testing of estrogen and progesterone receptors in breast cancer. JCO 2010; 28: 2784–95. doi: https://doi.org/10.1200/JCO. 2009.25.6529

33. Wolff AC, Hammond MEH, Schwartz JN, Hagerty KL, Allred DC, Cote RJ, et al. American Society of clinical Oncology/ College of American pathologists guideline recommendations for human epidermal growth factor receptor 2 testing in breast cancer. Arch Pathol Lab Med 2007; 131: 118–45. doi: https://doi.org/10.1200/JCO. 2006.09.2775

34. Tuncbilek N, Tokatli F, Altaner S, Sezer A, Türe M, Omurlu IK, et al. Prognostic value DCE-MRI parameters in predicting factor disease free survival and overall survival for breast cancer patients. Eur J Radiol 2012; 81: 863–7. doi: https://doi.org/10.1016/j.ejrad. 2011.02.021

35. Toi M, Inada K, Suzuki H, Tominaga T. Tumor angiogenesis in breast cancer: its importance as a prognostic indicator and the association with vascular endothelial growth factor expression. Breast Cancer Res Treat 1995; 36: 193–204. doi: https://doi.org/10. 1007/BF00666040

36. Uzzan B, Nicolas P, Cucherat M, Perret G-Y. Microvessel density as a prognostic factor in women with breast cancer: a systematic review of the literature and meta-analysis. Cancer Res 2004; 64: 2941–55. doi: https:// doi.org/10.1158/0008-5472.can-03-1957

37. Gasparini G. Prognostic value of vascular endothelial growth factor in breast cancer. Oncologist 2000; 5 Suppl 1(Suppl 1): 37–44. doi: https://doi.org/10.1634/theoncologist. 5-suppl_1-37

38. Yim H, Kang DK, Jung YS, Jeon GS, Kim TH. Analysis of kinetic curve and modelbased perfusion parameters on dynamic contrast enhanced MRI in breast cancer patients: correlations with dominant stroma type. Magn Reson Imaging 2016; 34: 60–5. doi: https://doi.org/10.1016/j.mri.2015.07. 010

39. Ku YJ, Kim HH, Cha JH, Shin HJ, Chae EY, Choi WJ, et al. Predicting the level of tumor-infiltrating lymphocytes in patients with triple-negative breast cancer: usefulness of breast MRI computer-aided detection and diagnosis. J Magn Reson Imaging 2018; 47: 760–6. doi: https://doi.org/10.1002/jmri. 25802

40. Adams S, Gray RJ, Demaria S, Goldstein L, Perez EA, Shulman LN, et al. Prognostic value of tumor-infiltrating lymphocytes in triple-negative breast cancers from two phase III randomized adjuvant breast cancer trials: ECoG 2197 and ECoG 1199. JCO 2014; 32: 2959–66. doi: https://doi.org/10.1200/JCO. 2013.55.0491

41. Ibrahim EM, Al-Foheidi ME, Al-Mansour MM, Kazkaz GA. The prognostic value of tumor-infiltrating lymphocytes in triplenegative breast cancer: a meta-analysis. Breast Cancer Res Treat 2014; 148: 467–76. doi: https://doi.org/10.1007/s10549-014- 3185-2

42. Uematsu T. Focal breast edema associated with malignancy on T2-weighted images of breast MRI: peritumoral edema, prepectoral edema, and subcutaneous edema. Breast Cancer 2015; 22: 66–70. doi: https://doi.org/ 10.1007/s12282-014-0572-9

43. Uematsu T, Kasami M, Watanabe J. Is evaluation of the presence of prepectoral edema on T2-weighted with fat-suppression 3 T breast MRI a simple and readily available noninvasive technique for estimation of prognosis in patients with breast cancer? Breast Cancer 2014; 21: 684–92. doi: https:// doi.org/10.1007/s12282-013-0440-z

44. Cheon H, Kim HJ, Lee SM, Cho SH, Shin KM, Kim GC, et al. Preoperative MRI features associated with lymphovascular invasion in node-negative invasive breast cancer: a propensity-matched analysis. J Magn Reson Imaging 2017; 46: 1037–44. doi: https://doi.org/10.1002/jmri.25710

45. Bae MS, Shin SU, Ryu HS, Han W, Im S-A, Park I-A, et al. Pretreatment MR imaging features of triple-negative breast cancer: association with response to neoadjuvant chemotherapy and recurrence-free survival. Radiology 2016; 281: 392–400. doi: https:// doi.org/10.1148/radiol.2016152331

46. Jansen SA, Shimauchi A, Zak L, Fan X, Wood AM, Karczmar GS, et al. Kinetic curves of malignant lesions are not consistent across MRI systems: need for improved standardization of breast dynamic contrastenhanced MRI acquisition. AJR Am J Roentgenol 2009; 193: 832–9. doi: https://doi. org/10.2214/AJR.08.2025

47. Bolouri MS, Elias SG, Wisner DJ, Behr SC, Hawkins RA, Suzuki SA, et al. TripleNegative and non-triple-negative invasive breast cancer: association between Mr and fluorine 18 fluorodeoxyglucose PET imaging. Radiology 2013; 269: 354–61. doi: https://doi. org/10.1148/radiol.13130058

参考文献をもっと見る