Journal of radiological science and technology (대한방사선기술학회지:방사선기술과학)

Korean Society of Radiological Science (대한방사선과학회)
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Evaluation of Usefulness for Diagnosis of Lung Cancer on Integrated PET-MRI Using Decision Matrix

판정행렬을 기반한 일체형 PET-MRI의 폐암 진단 유용성 평가

  • Kim, Jung-Soo (Department of Radiological Technology, Dongnam Health University) ;
  • Yang, Hyun-Jin (Department of Radiological Technology, Dongnam Health University) ;
  • Kim, Yoo-Mi (Department of Radiological Technology, Dongnam Health University) ;
  • Kwon, Hyeong-Jin (Department of Nuclear Medicine, Seoul National University Hospital) ;
  • Park, Chanrok (Department of Radiological Science, Jeonju University)
  • 김정수 (동남보건대학교 방사선과) ;
  • 양현진 (동남보건대학교 방사선과) ;
  • 김유미 (동남보건대학교 방사선과) ;
  • 권형진 (서울대학교병원 핵의학과) ;
  • 박찬록 (전주대학교 방사선학과)
  • Received : 2021.08.27
  • Accepted : 2021.11.11
  • Published : 2021.12.31
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Abstract

The results of empirical researches on the diagnosis of lung cancer are insufficient, so it is limited to objectively judge the clinical possibility and utilization according to the accuracy of diagnosis. Thus, this study retrospectively analyzed the lung cancer diagnostic performance of PET-MRI (Positron Emission Tomography-Magnetic Resonance Imaging) by using the decision matrix. This study selected and experimented total 165 patients who received both hematological CEA (Carcinoembryonic Antigen) test and hybrid PET-MRI (18F-FDG, 5.18 MBq/kg / Body TIM coil. VIVE-Dixon). After setting up the result of CEA (positive:>4 ㎍/ℓ. negative:<2.5㎍/ℓ) as golden data, the lung cancer was found in the image of PET-MRI, and then the SUVmax (positive:>4, negative:<1.5) was measured, and then evaluated the correlation and significance of results of relative diagnostic performance of PET-MRI compared to CEA through the statistical verification (t-test, P>0.05). Through this, the PET-MRI was analyzed as 96.29% of sensitivity, 95.23% of specificity, 3.70% of false negative rate, 4.76% of false positive rate, and 95.75% of accuracy. The false negative rate was 1.06% lower than the false positive rate. The PET-MRI that significant accuracy of diagnosis through high sensitivity and specificity, and low false negative rate and false positive rate of lung cancer, could acquire the fusion image of specialized soft tissue by combining the radio-pharmaceuticals with various sequences, so its clinical value and usefulness are regarded as latently sufficient.

Keywords

Acknowledgement

This paper is supported by the research fund of Dongnam Health University.

References

  1. Korea Central Cancer Registry. Annual report of cancer statistics in Korea in 2018; 2021.
  2. Park JY, Jang SH. Epidemiology of Lung Cancer in Korea: Recent trends. Tuberculosis and Respiratory Diseases. 2016;79(2):58-69. https://doi.org/10.4046/trd.2016.79.2.58
  3. Papadopoulos A, Guida F, LeffondrL K, CLnLe S, Cyr D, Schmaus A, et al. Heavy smoking and lung cancer: Are women at higher risk? Result of the ICARE study. Brt J Cancer. 2014;110:1385-91. https://doi.org/10.1038/bjc.2013.821
  4. McLean AEB, Barnes DJ, Troy LK. Diagnosing lung cancer: The complexities of obtaining a tissue diagnosis in the era of minimally invasive and personalised medicine. J Clin Med. 2018;7(7):163. https://doi.org/10.3390/jcm7070163
  5. RiihimLki M, Hemminki A, Fallah M, Thomsen H, Sundquist K, Sundquist J, et al. Metastatic sites and survival in lung cancer. Lung Cancer. 2014;86(1):78-84. https://doi.org/10.1016/j.lungcan.2014.07.020
  6. Conti M, Bendriem B. The new opportunities for high time resolution clinical TOF PET. Clin Trans Img. 2019;7(2):139-47. https://doi.org/10.1007/s40336-019-00316-5
  7. Erdi YE. Limits of tumor detectability in nuclear medicine and PET. Mol Img Radionucl Ther. 2012;21(1):23-8. https://doi.org/10.4274/Mirt.138
  8. Qu X, Huang X, Yan W, Wu L, Dai K. A meta-analysis of 18FDG-PET-CT, 18FDG-PET, MRI and bone scintigraphy for diagnosis of bone metastases in patients with lung cancer. Eur J Radiol. 2012;81(5):1007-15. https://doi.org/10.1016/j.ejrad.2011.01.126
  9. Lee SJ, Seo HJ, Cheon GJ, Kim JH, Kim EE, Kang KW, et al. Usefulness of integrated PET/MRI in head and neck cancer: A preliminary study. Nucl Med Mol Imaging. 2014;48(2):98-105. https://doi.org/10.1007/s13139-013-0252-2
  10. Khiewvan B, Torigian DA, Emamzadehfard S, Paydary K, Salavati A, Houshmand S, et al. Update of the role of PET/CT and PET/MRI in the management of patients with cervical cancer. Hell J Nucl Med. 2016;19(3):254-68.
  11. Ehman EC, Johnson GB, Villanueva-Meyer JE, Cha S, Leynes AP, Larson PEZ, et al. PET/MRI: Where might it replace PET/CT? J Magn Reson Imaging. 2017;46(5):1247-62. https://doi.org/10.1002/jmri.25711
  12. Kwon HW, Becker AK, Goo JM, Cheon GJ. FDG Whole-Body PET/MRI in Oncology: A systematic review. Nucl Med Mol Imaging. 2017;51(1):22-31. https://doi.org/10.1007/s13139-016-0411-3
  13. Delso G, Ziegler S. PET/MRI system design. Eur J Nucl Med and Mol Imaging. 2009;36:86-92. https://doi.org/10.1007/s00259-008-1008-6
  14. Jung JH, Choi Y, Im KC. PET/MRI: Technical challenges and recent advances. Nucl Med Mol Imaging. 2016;50(1):3-12. https://doi.org/10.1007/s13139-016-0393-1
  15. Loeffelbein DJ, Souvatzoglou M, Wankerl V, Martinez-MLller A, Dinges J, Schwaiger M, et al. PET-MRI fusion in head and neck oncology: Current status and implications for hybrid PET/MRI. J Oral Maxillofac Surg. 2012;70(2):473-83. https://doi.org/10.1016/j.joms.2011.02.120
  16. Nensa F, Beiderwellen K, Heusch P, Wetter A. Clinical applications of PET/MRI: current status and future perspectives. Diagn Interv Radiol. 2014; 20(5):438-47. https://doi.org/10.5152/dir.2014.14008
  17. Duan XY, Wang W, Li M, Li Y, Guo YM. Predictive significance of standardized uptake value parameters of FDG-PET in patients with non-small cell lung carcinoma. Braz J Med Biol Res. 2015;48(3):267-72. https://doi.org/10.1590/1414-431x20144137
  18. BLsing KA, SchLnberg SO, Brade J, Wasser K. Impact of blood glucose, diabetes, insulin, and obesity on standardized uptake values in tumors and healthy organs on 18F-FDG PET/CT. Nucl Med Biol. 2013;40:206-13. https://doi.org/10.1016/j.nucmedbio.2012.10.014
  19. Malladi A, Viner M, Jackson T, Mercier G, Subramaniam RM. PET/CT mediastinal and liver FDG uptake: Effects of biological and procedural factors. J Med Imaging Radiat Oncol. 2013;57:169-75. https://doi.org/10.1111/1754-9485.12015
  20. Kim MY. Relationship between pSUV of 18F-FDG PET/CT and pathological diagnosis in breast cancer. Journal of Radiological Science and Technology. 2013:36(4);305-11.
  21. Martinez-Moller A, Souvatzoglou M. Tissue classification as a potential approach for attenuation correction in whole-body PET-MRI. J Nucl Med. 2009;50:520-6. https://doi.org/10.2967/jnumed.108.054726
  22. Catana C, Van der Kouwe A, Benner T, Michel CJ. Toward implementing an MRI-based PET attenuation-correction method for neurologic studies on the MR-PET brain prototype. J Nucl Med. 2010;51:1431-8. https://doi.org/10.2967/jnumed.109.069112
  23. Soongsathitanon S, Masa-Ah P, Tuntawiroon M. A new standardized uptake values (SUV) calculation based on pixel intensity values. Math Comput Simul. 2012;6:26-33.
  24. Franklin WA, Aisner DL, Davies KD. Pathology, biomarkers, and molecular diagnostics. In: Niederhuber JE, Armitage JO, Kastan MB, Doroshow JH, Tepper JE (eds) Abeloff's Clinical Oncology. 6th ed. Philadelphia, PA: Elsevier; 2020:Chap 15.
  25. Jain S, Pincus MR, Bluth MH, McPherson RA, Bowne WB, Lee P. Diagnosis and management of cancer using serologic and other body fluid markers. In: McPherson RA, Pincus MR (eds) Henry's Clinical Diagnosis and Management by Laboratory Methods. 23rd ed. St Louis, MO: Elsevier; 2017:Chap 74.
  26. Boellaard R, Krak NC, Hoekstra OS, Lammertsma AA. Effects of noise, image resolution, and ROI definition on the accuracy of standard uptake values: A simulation study. J Nucl Med. 2004;45(9):1519-27.
  27. Kim JE, Kim JS, Choi NG, Han JB. Evaluation of the liver cancer diagnosis function of PET-MRI based on decision matrix analysis. J Kor Cont Asc. 2017;17(11):50-9. https://doi.org/10.5392/JKCA.2017.17.01.050
  28. Kang GW, Kim SE, Lee DS, Jeong JK. Koh's nuclear medicine. Kor Med. 2008;(3):8-20.
  29. Hutton BF, Erlandsson K, Thielemans K. Advances in clinical molecular imaging instrumentation. Clin Transl Imaging. 2018;6:31-5. https://doi.org/10.1007/s40336-018-0264-0
  30. Cangemi V, Volpino P, Drudi FM, D'Andrea N, Cangemi R, Piat G. Assessment of the accuracy of diagnostic chest CT scanning. Impact on lung cancer management. Int Surg. 1996;81(1):77-82.
  31. Ambrosini V, Nicolini S, Caroli P, Nanni C, Massaro A, Cristina Marzola M, et al. PET/CT imaging in different types of lung cancer: An overview. Eur J Radiol. 2021;81(5):988-1001. https://doi.org/10.1016/j.ejrad.2011.03.020
  32. Silvestri GA, Gonzalez AV, Jantz MA, Margolis ML, Gould MK, Tanoue LT, et al. Methods for staging non-small cell lung cancer. Chest J. 2013;143(5): 211-50.
  33. Collins LG, Haines C, Perkel R, Enck RE. Lung cancer: Diagnosis and management. Am Fam Physician. 2007;75(1):56-63.
  34. Rivera MP, Detterbeck F, Mehta AC. Diagnosis of lung cancer* The guidelines. Chest. 2003;123:129-36. https://doi.org/10.1378/chest.123.1_suppl.129S
  35. Shin GS, Dong GR. The difference of standardized uptake valueon PET-CT according to change of CT parameters. Journal of Radiological Science and Technology. 2007;30(4):373-9.
  36. Pournazari K, Jahangiri P, Mehdizadeh Seraj S, Khosravi M, Arani L, Torigian D, et al. PET/MRI applications in lung cancer. J Nucl Med. 2018;59(Supplement 1):1157.
  37. Mayerhoefer ME, Prosch H, Beer L, Tamandl D, Beyer T, Hoeller C, et al. PET/MRI versus PET/CT in oncology: A prospective single-center study of 330 examinations focusing on implications for patient management and cost considerations. Eur J Nucl and Mol Img. 2020;47:51-60. https://doi.org/10.1007/s00259-019-04452-y
  38. Hartenbach M, Hartenbach S, Bechtloff W, Danz B, Kraft K, Klemenz B, et al. Combined PET/MRI improves diagnostic accuracy in patients with prostate cancer: A prospective diagnostic trial. Clin Cancer Res. 2014;20(12):3244-53. https://doi.org/10.1158/1078-0432.CCR-13-2653
  39. Grunnet M, Sorensen JB. Carcinoembryonic antigen (CEA) as tumor marker in lung cancer. Lung Cancer. 2012;76(2):138-43. https://doi.org/10.1016/j.lungcan.2011.11.012
  40. Hsu WH, Huang CS, Hsu HS, Huang WJ, Lee HC, Huang BS, et al. Preoperative serum carcinoembryonic antigen level is a prognostic factor in women with early non-small-cell lung cancer. Ann Thorac Surg. 2007;83(2):419-24. https://doi.org/10.1016/j.athoracsur.2006.07.079
  41. Matsuoka K, Sumitomo S, Nakashima N, Nakajima D, Misaki N. Prognostic value of carcinoembryonic antigen and CYFRA21-1 in patients with pathological stage I non-small cell lung cancer. Eur J Cardiothorac Surg. 2007;32(3):435-9. https://doi.org/10.1016/j.ejcts.2007.05.014
  42. Okada M, Nishio W, Sakamoto T, Uchino K, Yuki T, Nakagawa A, et al. Effect of histologic type and smoking status on interpretation of serum carcinoembryonic antigen value in non-small cell lung carcinoma. Ann Thorac Surg. 2004;78(3):1004-9. https://doi.org/10.1016/j.athoracsur.2004.03.019
  43. Okada M, Nishio W, Sakamoto T, Uchino K, Yuki T, Nakagawa A, et al. Prognostic significance of perioperative serum carcinoembryonic antigen in non-small cell lung cancer: Analysis of 1,000 consecutive resections for clinical stage I disease. Ann Thorac Surg. 2004;78(1):216-21. https://doi.org/10.1016/j.athoracsur.2004.02.009
  44. Vickers AJ. Decision analysis for the evaluation of diagnostic tests, prediction models and molecular markers. Am Stat. 2008;62(4):314-20. https://doi.org/10.1198/000313008X370302
  45. Killeen PR. Beyond statistical inference: A decision theory for science. Psychon Bull Rev. 2006;13(4):549-62. https://doi.org/10.3758/BF03193962
  46. Kubota K, Matsuzawa T, Fujiwara T, Ito M, Hatazawa J, Ishiwata K, et al. Differential diagnosis of lung tumor with positron emission tomography: A prospective study. J Nucl Med. 1990:31(12);1927-32.