Automated classification of pulmonary nodules through a retrospective analysis of conventional CT and two-phase PET images in patients undergoing biopsy

Document Type: Original Article

Authors

1 Fujita Health University

2 Fujita Health University Hospital

3 School of Medicine, Fujita Health Hniversity

4 School of Medicine, Fujita Health University

5 Department of Radiology, Fujita Health University

6 School of Health Sciences, Fujita Health University

7 Dep. Electrical, Electronic & Computer Engineering, Fac. Engineering Gifu University

Abstract

Objective(s): Positron emission tomography/computed tomography (PET/CT) examination is commonly used for the evaluation of pulmonary nodules since it provides both anatomical and functional information. However, given the dependence of this evaluation on physician’s subjective judgment, the results could be variable. The purpose of this study was to develop an automated scheme for the classification of pulmonary nodules using early and delayed phase PET/ CT and conventional CT images.
Methods: We analysed 36 early and delayed phase PET/CT images in patients who underwent both PET/CT scan and lung biopsy, following bronchoscopy. In addition, conventional CT images at maximal inspiration were analysed. The images consisted of 18 malignant and 18 benign nodules. For the classification scheme, 25 types of shape and functional features were first calculated from the images. The random forest algorithm, which is a machine learning technique, was used for classification.
Results: The evaluation of the characteristic features and classification accuracy was accomplished using collected images. There was a significant difference between the characteristic features of benign and malignant nodules with regard to standardised uptake value and texture. In terms of classification performance, 94.4% of the malignant nodules were identified correctly assuming that 72.2% of the benign nodules were diagnosed accurately. The accuracy rate of benign nodule detection by means of CT plus two-phase PET images was 44.4% and 11.1% higher than those obtained by CT images alone and CT plus early phase PET images, respectively.
Conclusion: Based on the findings, the proposed method may be useful to improve the accuracy of malignancy analysis.

Keywords

Main Subjects


1. American cancer society, cancer facts and figures 2015. Available at: URL: https://www.cancer. org/content/dam/cancer-org/research/cancerfacts-and-statistics/annual-cancer-facts-andfigures/2015/cancer-facts-and-figures-2015.pdf; Accessed September 14, 2018.

2. Sone S, Takashima S, Li F, Yang Z, Honda T, Maruyama Y, et al. Mass screening for lung cancer with mobile spiral computed tomography scanner. Lancet. 1998;351(9111):1242-5.

3. National Lung Screening Trial Research Team, Aberle DR, Adams AM, Berg CD, Black WC, Clapp JD, et al. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med. 2011;365(5):395-409.

4. Gould MK, Maclean CC, Kuschner WG, Rydzak CE, Owens DK. Accuracy of positron emission tomography for diagnosis of pulmonary nodules and mass lesions: a meta-analysis. JAMA. 2001; 285(7):914-24.

5. Armato SG 3rd, Altman MB, Wilkie J, Sone S, Li F, Doi K, et al. Automated lung nodule classification following automated nodule detection on CT: a serial approach. Med Phys. 2003;30(6):1188-97.

6. Way TW, Hadjiiski LM, Sahiner B, Chan HP, Cascade PN, Kazerooni EA, et al. Computer-aided diagnosis of pulmonary nodules on CT scans: segmentation and classification using 3D active contours. Med Phys. 2006;33(7):2323-37.

7. Zhang F, Song, Y, Cai W, Lee MZ, Zhou Y, Huang H, et al. Lung nodule classification with multilevel patchbased context analysis. IEEE Trans Biomed Eng. 2014;61(4):1155-66.

8. Madero Orozco H, Vergara Villegas OO, Cruz Sánchez VG, Ochoa Domínguez Hde J, Nandayapa Alfaro Mde J. Automated system for lung nodules classification based on wavelet feature descriptor and support vector machine. Biomed Eng Online. 2015;14:9.

9. Shen W, Zhou M, Yang F, Yu D, Dong D, Yang C, et al. Multi-crop convolutional neural networks for lung nodule malignancy suspiciousness classification. Pattern Recognition. 2017;61:663-73.

10. Nie Y, Li Q, Li F, Pu Y, Appelbaum D, Doi K. Integrating PET and CT Information to improve diagnostic accuracy for lung nodules: a semiautomatic computer-aided method. J Nucl Med. 2006;47(7):1075-80.

11. 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(1):228-43.

12. Sim YT, Poon FW. Imaging of solitary pulmonary nodule-a clinical review. Quant Imaging Med Surg. 2013;3(6):316-26.

13. Keyes JW Jr. SUV: standard uptake or silly useless value? J Nucl Med. 1995;36(10):1836-9.

14. Li Q, Sone S, Doi K. Selective enhancement filters for nodules, vessels, and airway walls in twoand three-dimensional CT scans. Med Phys. 2003;30(8):2040-51.

15. Rangayyan RM, Ayres FJ. Gabor filter and phase portraits for the detection of architectural distortion in mammograms. Med Biol Eng Comput. 2006;44(10):883-94.

16. Yoshikawa R, Teramoto A, Matsubara T, Fujita H. Automated detection of architectural distortion using improved adaptive Gabor filter. International Workshop on Digital Mammography, Springer, Cham; 2014 Jun 29. P. 606-11.

17. Haralick RM, Shanmugam K, Dinstein I. Textural features for image classification. IEEE Trans Syst Man Cybernetics. 1973;3(6):610-21.

18. Breiman L. Random forests. Machine Learn. 2001;45(1):5-32.

19. Cohen J. Statistical power analysis for the behavioral sciences. 2nd ed. Hillsdale: Lawrence Erlbaum Associates; 1988.

20. Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. Adv Neural Informat Proc Syst. 2012;25(2):1106-14.

21. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436-44.

22. Teramoto A, Fujita H, Yamamuro O, Tamaki T. Automated detection of pulmonary nodules in PET/ CT images: ensemble false-positive reduction using a convolutional neural network technique. Med Phys. 2016;43(6):2821-7.

23. Teramoto A, Tsukamoto T, Kiriyama Y, Fujita H. Automated classification of lung cancer types from cytological images using deep convolutional neural networks. Biomed Res Int. 2017;2017:4067832.