한국의학물리학회지:의학물리 (Progress in Medical Physics)

  • 제34권3호
  • /
  • Pages.23-32
  • /
  • 2023
  • /
  • 2508-4445(pISSN)
  • /
  • 2508-4453(eISSN)
한국의학물리학회 (Korean Society of Medical Physics)
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Influence of Two-Dimensional and Three-Dimensional Acquisitions of Radiomic Features for Prediction Accuracy

  • Ryohei Fukui (Department of Radiological Technology, Faculty of Health Sciences, Okayama University) ;
  • Ryutarou Matsuura (Department of Radiological Technology, Faculty of Health Sciences, Okayama University) ;
  • Katsuhiro Kida (Department of Radiological Technology, Faculty of Health Sciences, Okayama University) ;
  • Sachiko Goto (Department of Radiological Technology, Faculty of Health Sciences, Okayama University)
  • 투고 : 2023.04.21
  • 심사 : 2023.07.27
  • 발행 : 2023.09.30
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초록

Purpose: In radiomics analysis, to evaluate features, and predict genetic characteristics and survival time, the pixel values of lesions depicted in computed tomography (CT) and magnetic resonance imaging (MRI) images are used. CT and MRI offer three-dimensional images, thus producing three-dimensional features (Features_3d) as output. However, in reports, the superiority between Features_3d and two-dimensional features (Features_2d) is distinct. In this study, we aimed to investigate whether a difference exists in the prediction accuracy of radiomics analysis of lung cancer using Features_2d and Features_3d. Methods: A total of 38 cases of large cell carcinoma (LCC) and 40 cases of squamous cell carcinoma (SCC) were selected for this study. Two- and three-dimensional lesion segmentations were performed. A total of 774 features were obtained. Using least absolute shrinkage and selection operator regression, seven Features_2d and six Features_3d were obtained. Results: Linear discriminant analysis revealed that the sensitivities of Features_2d and Features_3d to LCC were 86.8% and 89.5%, respectively. The coefficients of determination through multiple regression analysis and the areas under the receiver operating characteristic curve (AUC) were 0.68 and 0.70 and 0.93 and 0.94, respectively. The P-value of the estimated AUC was 0.87. Conclusions: No difference was found in the prediction accuracy for LCC and SCC between Features_2d and Features_3d.

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참고문헌

  1. Baumann M, Krause M, Overgaard J, Debus J, Bentzen SM, Daartz J, et al. Radiation oncology in the era of precision medicine. Nat Rev Cancer. 2016;16:234-249. 
  2. Collins FS, Varmus H. A new initiative on precision medicine. N Engl J Med. 2015;372:793-795. 
  3. Gerlinger M, Rowan AJ, Horswell S, Math M, Larkin J, Endesfelder D, et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med. 2012;366:883-892. Erratum in: N Engl J Med. 2012;367:976. 
  4. Mayerhoefer ME, Materka A, Langs G, Haggstrom I, Szczypinski P, Gibbs P, et al. Introduction to radiomics. J Nucl Med. 2020;61:488-495. 
  5. Wilson R, Devaraj A. Radiomics of pulmonary nodules and lung cancer. Transl Lung Cancer Res. 2017;6:86-91. 
  6. Brancato V, Cerrone M, Lavitrano M, Salvatore M, Cavaliere C. A systematic review of the current status and quality of radiomics for glioma differential diagnosis. Cancers (Basel). 2022;14:2731. 
  7. Zheng X, Yao Z, Huang Y, Yu Y, Wang Y, Liu Y, et al. Deep learning radiomics can predict axillary lymph node status in early-stage breast cancer. Nat Commun. 2020;11:1236. Erratum in: Nat Commun. 2021;12:4370. 
  8. Li G, Li L, Li Y, Qian Z, Wu F, He Y, et al. An MRI radiomics approach to predict survival and tumour-infiltrating macrophages in gliomas. Brain. 2022;145:1151-1161. 
  9. Yu Y, Tan Y, Xie C, Hu Q, Ouyang J, Chen Y, et al. Development and validation of a preoperative magnetic resonance imaging radiomics-based signature to predict axillary lymph node metastasis and disease-free survival in patients with early-stage breast cancer. JAMA Netw Open. 2020;3:e2028086. 
  10. Fedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion-Robin JC, Pujol S, et al. 3D slicer as an image computing platform for the quantitative imaging network. Magn Reson Imaging. 2012;30:1323-1341. 
  11. National Cancer Institute (NCI). Cancer imaging archive. Bethesda (MD): NCI, 2011 [cited 2023 Jan 11]. Available from: https://www.cancerimagingarchive.net/ 
  12. American Joint Committee on Cancer (AJCC). AJCC cancer staging manual. 7th ed. New York: Springer; 2010:253-270. 
  13. Shafiq-Ul-Hassan M, Zhang GG, Latifi K, Ullah G, Hunt DC, Balagurunathan Y, et al. Intrinsic dependencies of CT radiomic features on voxel size and number of gray levels. Med Phys. 2017;44:1050-1062. 
  14. Larue RTHM, van Timmeren JE, de Jong EEC, Feliciani G, Leijenaar RTH, Schreurs WMJ, et al. Influence of gray level discretization on radiomic feature stability for different CT scanners, tube currents and slice thicknesses: a comprehensive phantom study. Acta Oncol. 2017;56:1544-1553. 
  15. Velazquez ER, Parmar C, Jermoumi M, Mak RH, van Baardwijk A, Fennessy FM, et al. Volumetric CT-based segmentation of NSCLC using 3D-Slicer. Sci Rep. 2013. 3:3529. 
  16. van Griethuysen JJM, Fedorov A, Parmar C, Hosny A, Aucoin N, Narayan V, et al. Computational radiomics system to decode the radiographic phenotype. Cancer Res. 2017. 77:e104-e107. 
  17. Hastie T, Tibshirani R, Wainwright M. Statistical learning with sparsity: the Lasso and generalizations. New York: CRC Press; 2015:2-10. 
  18. Shao J. Linear model selection by cross-validation. J Am Stat Assoc. 1993;88:486-494. 
  19. Li R, Xing L, Napel S, Rubin DL. Radiomics and radiogenomics: technical basis and clinical applications. Boca Raton: CRC Press; 2019. 
  20. Haralick RM, Shanmugam K, Dinstein IH. Textural features for image classification. IEEE Trans Syst Man Cybern. 1973;SMC-3:610-621. 
  21. Galloway MM. Texture analysis using gray level run lengths. Comput Graph Image Process. 1975;4:172-179. 
  22. Zwanenburg A, Vallieres M, Abdalah MA, Aerts HJWL, Andrearczyk V, Apte A, et al. The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping. Radiology. 2020;295:328-338. 
  23. Yan F, Kittler J, Mikolajczyk K, Tahir A. Non-sparse multiple kernel Fisher discriminant analysis. J Mach Learn Res. 2012;13:607-642. 
  24. Park SH, Goo JM, Jo CH. Receiver operating characteristic (ROC) curve: practical review for radiologists. Korean J Radiol. 2004;5:11-18. 
  25. DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988;44:837-845. 
  26. MacMahon H, Naidich DP, Goo JM, Lee KS, Leung ANC, Mayo JR, et al. Guidelines for management of incidental pulmonary nodules detected on CT images: from the Fleischner Society 2017. Radiology. 2017;284:228-243. 
  27. Honda O, Tsubamoto M, Inoue A, Johkoh T, Tomiyama N, Hamada S, et al. Pulmonary cavitary nodules on computed tomography: differentiation of malignancy and benignancy. J Comput Assist Tomogr. 2007;31:943-949. 
  28. Travis WD, Brambilla E, Noguchi M, Nicholson AG, Geisinger KR, Yatabe Y, 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;6:244-285. 
  29. Lim HJ, Ahn S, Lee KS, Han J, Shim YM, Woo S, et al. Persistent pure ground-glass opacity lung nodules ≥ 10 mm in diameter at CT scan: histopathologic comparisons and prognostic implications. Chest. 2013;144:1291-1299. 
  30. Larue RT, Defraene G, De Ruysscher D, Lambin P, van Elmpt W. Quantitative radiomics studies for tissue characterization: a review of technology and methodological procedures. Br J Radiol. 2017;90:20160665.