DOI QR코드

DOI QR Code

3T 자기공명영상에서 비특이 침윤성 유방암의 컴퓨터보조진단 인자들과 병리적 면역조직화학 표지자들과의 상관성

Computer-Aided Diagnosis Parameters of Invasive Carcinoma of No Special Type on 3T MRI: Correlation with Pathologic Immunohistochemical Markers

  • 정진호 (가톨릭대학교 의과대학 인천성모병원 영상의학과) ;
  • 박창숙 (가톨릭대학교 의과대학 인천성모병원 영상의학과) ;
  • 이정휘 (가톨릭대학교 의과대학 인천성모병원 영상의학과) ;
  • 김기준 (가톨릭대학교 의과대학 인천성모병원 영상의학과) ;
  • 김현숙 (가톨릭대학교 의과대학 은평성모병원 영상의학과) ;
  • 전선영 (가톨릭대학교 의과대학 인천성모병원 병리과) ;
  • 오세정 (가톨릭대학교 의과대학 인천성모병원 외과)
  • Jinho Jeong (Department of Radiology, Incheon St. Mary's Hospital, College of Medicine, The Catholic University of Korea) ;
  • Chang Suk Park (Department of Radiology, Incheon St. Mary's Hospital, College of Medicine, The Catholic University of Korea) ;
  • Jung Whee Lee (Department of Radiology, Incheon St. Mary's Hospital, College of Medicine, The Catholic University of Korea) ;
  • Kijun Kim (Department of Radiology, Incheon St. Mary's Hospital, College of Medicine, The Catholic University of Korea) ;
  • Hyeon Sook Kim (Department of Radiology, Eunpyeong St. Mary's Hospital, College of Medicine, The Catholic University of Korea) ;
  • Sun-Young Jun (Departments of Pathology, Incheon St. Mary’s Hospital, College of Medicine, The Catholic University of Korea) ;
  • Se-Jeong Oh (Departments of Surgery, Incheon St. Mary’s Hospital, College of Medicine, The Catholic University of Korea)
  • 투고 : 2021.04.14
  • 심사 : 2021.06.15
  • 발행 : 2022.01.01

초록

목적 3-tesla (이하 T) 자기공명영상에서 비특이 침윤성 유방암의 컴퓨터보조진단 인자들과 병리적 면역조직화학 표지자들과의 상관성을 알아보고자 하였다. 대상과 방법 2018년 1월부터 2019년 4월까지 비특이 침윤성 유방암으로 진단받은 총 94명의 3T 자기공명영상에서 컴퓨터보조진단 시스템을 통해 얻은 혈관조영부피, 최대 조영증강, 조기 및 지연 조영증강 양상과 면역화학인자와 유방암의 분자형 아형과의 상관성을 Dwass, Steel, Critchlow-Fligner 비교 분석과 이분형 로지스틱 회귀 분석을 이용하여 후향적으로 연구하였다. 결과 혈관조영부피가 큰 비특이 침윤성 유방암이 핵등급과 조직학적 등급이 높고, 림프절 전이가 있고, 에스트로겐 수용체/프로게스테론 수용체 음성, 인간 표피성장인자수용체 2/Ki-67 양성이 많았다. Ki-67 양성인 비특이 침윤성 유방암에서 지연기 소실 성분 비율이 높고 지연기 지속 조영증강 비율이 낮았다. 이항회귀분석에서는 컴퓨터보조진단 시스템의 요소 중 혈관조영부피 인자가 독립적으로 핵등급, 조직학적 등급, 림프절 전이, 에스트로겐/프로게스테론 수용체, 인간 표피성장인자수용체 2와 Ki-67과 상관성이 있고, 지연기 소실 및 지속 조영증강 인자가 Ki-67과 상관성이 있었다. 결론 조영증강 유방 MRI 컴퓨터보조진단 시스템 인자 중 혈관조영부피 요소와 지연기 소실/지속 조영증강 비율이 예후 예측 인자로 알려진 면역화학인자들과 연관성이 높아 임상적 예후 예측 인자로서 이용될 수 있을 것으로 사료된다.

Purpose To investigate the correlation between computer-aided diagnosis (CAD) parameters in 3-tesla (T) MRI and pathologic immunohistochemical (IHC) markers in invasive carcinoma of no special type (NST). Materials and Methods A total of 94 female who were diagnosed with NST carcinoma and underwent 3T MRI using CAD, from January 2018 to April 2019, were included. The relationship between angiovolume, curve peak, and early and late profiles of dynamic enhancement from CAD with pathologic IHC markers and molecular subtypes were retrospectively investigated using Dwass, Steel, Critchlow-Fligner multiple comparison analysis, and univariate binary logistic regression analysis. Results In NST carcinoma, a higher angiovolume was observed in tumors of higher nuclear and histologic grades and in lymph node (LN) (+), estrogen receptor (ER) (-), progesterone receptor (PR) (-), human epidermal growth factor 2 (HER2) (+), and Ki-67 (+) tumors. A high rate of delayed washout and a low rate of delayed persistence were observed in Ki-67 (+) tumors. In the binary logistic regression analysis of NST carcinoma, a high angiovolume was significantly associated with a high nuclear and histologic grade, LN (+), ER (-), PR (-), HER2 (+) status, and non-luminal subtypes. A high rate of washout and a low rate of persistence were also significantly correlated with the Ki-67 (+) status. Conclusion Angiovolume and delayed washout/persistent rate from CAD parameters in contrast enhanced breast MRI correlated with predictive IHC markers. These results suggest that CAD parameters could be used as clinical prognostic, predictive factors.

키워드

과제정보

Statistical evaluation was supported by the Department of Biostatistics of the Catholic Research Coordinating Center.

참고문헌

  1. Peters NH, Borel Rinkes IH, Zuithoff NP, Mali WP, Moons KG, Peeters PH. Meta-analysis of MR imaging in the diagnosis of breast lesions. Radiology 2008;246:116-124 
  2. Morris EA. Diagnostic breast MR imaging: current status and future directions. Radiol Clin North Am 2007;45:863-880, vii 
  3. Morrow M, Waters J, Morris E. MRI for breast cancer screening, diagnosis, and treatment. Lancet 2011;378:1804-1811 
  4. Pinker-Domenig K, Bogner W, Gruber S, Bickel H, Duffy S, Schernthaner M, et al. High resolution MRI of the breast at 3 T: which BI-RADS® descriptors are most strongly associated with the diagnosis of breast cancer? Eur Radiol 2012;22:322-330 
  5. Leithner D, Wengert GJ, Helbich TH, Thakur S, Ochoa-Albiztegui RE, Morris EA, et al. Clinical role of breast MRI now and going forward. Clin Radiol 2018;73:700-714 
  6. Kim JJ, Kim JY, Kang HJ, Shin JK, Kang T, Lee SW, et al. Computer-aided diagnosis-generated 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 
  7. Koh J, Park AY, Ko KH, Jung HK. Can enhancement types on preoperative MRI reflect prognostic factors and surgical outcomes in invasive breast cancer? Eur Radiol 2019;29:7000-7008 
  8. Lehman CD, Peacock S, DeMartini WB, Chen X. A new automated software system to evaluate breast MR examinations: improved specificity without decreased sensitivity. AJR Am J Roentgenol 2006;187:51-56 
  9. Goldhirsch A, Winer EP, Coates AS, Gelber RD, Piccart-Gebhart M, Thurlimann B, et al. Personalizing the treatment of women with early breast cancer: highlights of the St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2013. Ann Oncol 2013;24:2206-2223 
  10. Weigelt B, Horlings HM, Kreike B, Hayes MM, Hauptmann M, Wessels LF, et al. Refinement of breast cancer classification by molecular characterization of histological special types. J Pathol 2008;216:141-150 
  11. Baltzer PA, Vag T, Dietzel M, Beger S, Freiberg C, Gajda M, et al. Computer-aided interpretation of dynamic magnetic resonance imaging reflects histopathology of invasive breast cancer. Eur Radiol 2010;20:1563-1571 
  12. Song SE, Cho KR, Seo BK, Woo OH, Jung SP, Sung DJ. Kinetic features of invasive breast cancers on computer-aided diagnosis using 3T MRI data: correlation with clinical and pathologic prognostic factors. Korean J Radiol 2019;20:411-421 
  13. Yamaguchi K, Abe H, Newstead GM, Egashira R, Nakazono T, Imaizumi T, et al. Intratumoral heterogeneity of the distribution of kinetic parameters in breast cancer: comparison based on the molecular subtypes of invasive breast cancer. Breast Cancer 2015;22:496-502 
  14. Levman JE, Causer P, Warner E, Martel AL. Effect of the enhancement threshold on the computer-aided detection of breast cancer using MRI. Acad Radiol 2009;16:1064-1069 
  15. Soliman NA, Yussif SM. Ki-67 as a prognostic marker according to breast cancer molecular subtype. Cancer Biol Med 2016;13:496-504 
  16. Castaneda-Gill JM, Vishwanatha JK. Antiangiogenic mechanisms and factors in breast cancer treatment. J Carcinog 2016;15:1 
  17. Weidner N, Folkman J, Pozza F, Bevilacqua P, Allred EN, Moore DH, et al. Tumor angiogenesis: a new significant and independent prognostic indicator in early-stage breast carcinoma. J Natl Cancer Inst 1992;84:1875-1887 
  18. Madu CO, Wang S, Madu CO, Lu Y. Angiogenesis in breast cancer progression, diagnosis, and treatment. J Cancer 2020;11:4474-4494 
  19. Bharti JN, Rani P, Kamal V, Agarwal PN. Angiogenesis in breast cancer and its correlation with estrogen, progesterone receptors and other prognostic factors. J Clin Diagn Res 2015;9:EC05-EC07 
  20. Song SE, Bae MS, Chang JM, Cho N, Ryu HS, Moon WK. MR and mammographic imaging features of HER2- positive breast cancers according to hormone receptor status: a retrospective comparative study. Acta Radiol 2017;58:792-799 
  21. Uematsu T. MR imaging of triple-negative breast cancer. Breast Cancer 2011;18:161-164 
  22. Arima N, Nishimura R, Osako T, Okumura Y, Nakano M, Fujisue M, et al. Ki-67 index value and progesterone receptor status can predict prognosis and suitable treatment in node-negative breast cancer patients with estrogen receptor-positive and HER2-negative tumors. Oncol Lett 2019;17:616-622 
  23. Caiazzo C, Di Micco R, Esposito E, Sollazzo V, Cervotti M, Varelli C, et al. The role of MRI in predicting Ki-67 in breast cancer: preliminary results from a prospective study. Tumori 2018;104:438-443 
  24. Lee SH, Cho N, Kim SJ, Cha JH, Cho KS, Ko ES, et al. Correlation between high resolution dynamic MR features and prognostic factors in breast cancer. Korean J Radiol 2008;9:10-18 
  25. Abdelrahman AE, Rashed HE, Abdelgawad M, Abdelhamid MI. Prognostic impact of EGFR and cytokeratin 5/6 immunohistochemical expression in triple-negative breast cancer. Ann Diagn Pathol 2017;28:43-53 
  26. Nam SY, Ko ES, Lim Y, Han BK, 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