Prognostic Importance of Volumetric Measurements in Stage I Lung Adenocarcinoma

References

• 1. Travis WD, Brambilla E, Noguchi M, et al. International association for the study of lung cancer/american thoracic society/european respiratory society international multidisciplinary classification of lung adenocarcinoma. J Thorac Oncol 2011;6(2):244–285.
  MedlineGoogle Scholar
• 2. Ikehara M, Saito H, Yamada K, et al. Prognosis of small adenocarcinoma of the lung based on thin-section computed tomography and pathological preparations. J Comput Assist Tomogr 2008;32(3):426–431.
  MedlineGoogle Scholar
• 3. Nakazono T, Sakao Y, Yamaguchi K, Imai S, Kumazoe H, Kudo S. Subtypes of peripheral adenocarcinoma of the lung: differentiation by thin-section CT. Eur Radiol 2005;15(8):1563–1568.
  MedlineGoogle Scholar
• 4. Sakao Y, Nakazono T, Tomimitsu S, et al. Lung adenocarcinoma can be subtyped according to tumor dimension by computed tomography mediastinal-window setting. Additional size criteria for clinical T1 adenocarcinoma. Eur J Cardiothorac Surg 2004;26(6):1211–1215.
  MedlineGoogle Scholar
• 5. Noguchi M. Stepwise progression of pulmonary adenocarcinoma—clinical and molecular implications. Cancer Metastasis Rev 2010;29(1):15–21.
  MedlineGoogle Scholar
• 6. Naidich DP, Bankier AA, MacMahon H, et al. Recommendations for the management of subsolid pulmonary nodules detected at CT: a statement from the Fleischner Society. Radiology 2013;266(1):304–317.
  Google Scholar
• 7. Yankelevitz DF, Reeves AP, Kostis WJ, Zhao B, Henschke CI. Small pulmonary nodules: volumetrically determined growth rates based on CT evaluation. Radiology 2000;217(1):251–256.
  Google Scholar
• 8. Ko JP, Rusinek H, Jacobs EL, et al. Small pulmonary nodules: volume measurement at chest CT—phantom study. Radiology 2003;228(3):864–870.
  Google Scholar
• 9. Das M, Mühlenbruch G, Katoh M, et al. Automated volumetry of solid pulmonary nodules in a phantom: accuracy across different CT scanner technologies. Invest Radiol 2007;42(5):297–302.
  MedlineGoogle Scholar
• 10. Marchianò A, Calabrò E, Civelli E, et al. Pulmonary nodules: volume repeatability at multidetector CT lung cancer screening. Radiology 2009;251(3):919–925.
  Google Scholar
• 11. Ko JP, Berman EJ, Kaur M, et al. Pulmonary Nodules: growth rate assessment in patients by using serial CT and three-dimensional volumetry. Radiology 2012;262(2):662–671.
  Google Scholar
• 12. de Hoop B, Gietema H, van de Vorst S, Murphy K, van Klaveren RJ, Prokop M. Pulmonary ground-glass nodules: increase in mass as an early indicator of growth. Radiology 2010;255(1):199–206.
  Google Scholar
• 13. Sato Y, Nakajima S, Shiraga N, et al. Three-dimensional multi-scale line filter for segmentation and visualization of curvilinear structures in medical images. Med Image Anal 1998;2(2):143–168.
  MedlineGoogle Scholar
• 14. Yanagawa M, Kuriyama K, Kunitomi Y, et al. One-dimensional quantitative evaluation of peripheral lung adenocarcinoma with or without ground-glass opacity on thin-section CT images using profile curves. Br J Radiol 2009;82(979):532–540.
  MedlineGoogle Scholar
• 15. Kuriyama K, Gamsu G, Stern RG, Cann CE, Herfkens RJ, Brundage BH. CT-determined pulmonary artery diameters in predicting pulmonary hypertension. Invest Radiol 1984;19(1):16–22.
  MedlineGoogle Scholar
• 16. Liu HL, Liu RR, Reeve DM, Shepard SJ, Willis CE. Measurement of CT radiation profile width using CR imaging plates. Med Phys 2005;32(9):2881–2887.
  MedlineGoogle Scholar
• 17. Pratt WK. Digital image processing. 4th ed. Los Altos, Calif: Wiley, 2007.
  Google Scholar
• 18. Yanagawa M, Tanaka Y, Kusumoto M, et al. Automated assessment of malignant degree of small peripheral adenocarcinomas using volumetric CT data: correlation with pathologic prognostic factors. Lung Cancer 2010;70(3):286–294.
  MedlineGoogle Scholar
• 19. Evangelou E, Kyzas PA, Trikalinos TA. Comparison of the diagnostic accuracy of lymphatic endothelium markers: Bayesian approach. Mod Pathol 2005;18(11):1490–1497.
  MedlineGoogle Scholar
• 20. Giatromanolaki A, Koukourakis MI, Theodossiou D, et al. Comparative evaluation of angiogenesis assessment with anti-factor-VIII and anti-CD31 immunostaining in non-small cell lung cancer. Clin Cancer Res 1997;3(12 Pt 1):2485–2492.
  MedlineGoogle Scholar
• 21. Maclure M, Willett WC. Misinterpretation and misuse of the kappa statistic. Am J Epidemiol 1987;126(2):161–169.
  MedlineGoogle Scholar
• 22. Gould MK, Donington J, Lynch WR, et al. Evaluation of individuals with pulmonary nodules: when is it lung cancer? Diagnosis and management of lung cancer,3rd ed: American College of Chest Physicians evidence-based clinical practice guidelines. Chest 2013;143(5 Suppl):e93S–120S.
  MedlineGoogle Scholar
• 23. Arenas-Jiménez J. Measurement of solid component in part-solid lesions with a mediastinal window setting? Radiology 2013;268(1):305–306.
  Google Scholar
• 24. Nakamura H, Saji H, Ogata A, Saijo T, Okada S, Kato H. Lung cancer patients showing pure ground-glass opacity on computed tomography are good candidates for wedge resection. Lung Cancer 2004;44(1):61–68.
  MedlineGoogle Scholar
• 25. Park JH, Lee KS, Kim JH, et al. Malignant pure pulmonary ground-glass opacity nodules: prognostic implications. Korean J Radiol 2009;10(1):12–20.
  MedlineGoogle Scholar
• 26. Lim HJ, Ahn S, Lee KS, et al. Persistent pure ground-glass opacity lung nodules ≥ 10 mm in diameter at CT scan: histopathologic comparisons and prognostic implications. Chest 2013;144(4):1291–1299.
  MedlineGoogle Scholar
• 27. Revel MP, Lefort C, Bissery A, et al. Pulmonary nodules: preliminary experience with three-dimensional evaluation. Radiology 2004;231(2):459–466.
  Google Scholar
• 28. Zhao B, Reeves A, Yankelevitz D, Henshke C. Three-dimensional multi-criterion automatic segmentation of pulmonary nodules of helical computed tomography images. Opt Eng 1999;38(8):1340–1347.
  Google Scholar
• 29. Reeves AP, Chan AB, Yankelevitz DF, Henschke CI, Kressler B, Kostis WJ. On measuring the change in size of pulmonary nodules. IEEE Trans Med Imaging 2006;25(4):435–450.
  MedlineGoogle Scholar
• 30. Kauczor HU, Heitmann K, Heussel CP, Marwede D, Uthmann T, Thelen M. Automatic detection and quantification of ground-glass opacities on high-resolution CT using multiple neural networks: comparison with a density mask. AJR Am J Roentgenol 2000;175(5):1329–1334.
  MedlineGoogle Scholar
• 31. Tan Y, Schwartz LH, Zhao B. Segmentation of lung lesions on CT scans using watershed, active contours, and Markov random field. Med Phys 2013;40(4):043502.
  Google Scholar
• 32. Kim H, Park CM, Woo S, et al. Pure and part-solid pulmonary ground-glass nodules: measurement variability of volume and mass in nodules with a solid portion less than or equal to 5 mm. Radiology 2013;269(2):585–593.
  Google Scholar
• 33. Tsutani Y, Miyata Y, Nakayama H, et al. Prognostic significance of using solid versus whole tumor size on high-resolution computed tomography for predicting pathologic malignant grade of tumors in clinical stage IA lung adenocarcinoma: a multicenter study. J Thorac Cardiovasc Surg 2012;143(3):607–612.
  MedlineGoogle Scholar
• 34. Maeyashiki T, Suzuki K, Hattori A, Matsunaga T, Takamochi K, Oh S. The size of consolidation on thin-section computed tomography is a better predictor of survival than the maximum tumour dimension in resectable lung cancer. Eur J Cardiothorac Surg 2013;43(5):915–918.
  MedlineGoogle Scholar
• 35. Sakao Y, Miyamoto H, Sakuraba M, et al. Prognostic significance of a histologic subtype in small adenocarcinoma of the lung: the impact of nonbronchioloalveolar carcinoma components. Ann Thorac Surg 2007;83(1):209–214.
  MedlineGoogle Scholar
• 36. Hashizume T, Yamada K, Okamoto N, et al. Prognostic significance of thin-section CT scan findings in small-sized lung adenocarcinoma. Chest 2008;133(2):441–447.
  MedlineGoogle Scholar