TY - JOUR ID - 12014 TI - Automated classification of pulmonary nodules through a retrospective analysis of conventional CT and two-phase PET images in patients undergoing biopsy JO - Asia Oceania Journal of Nuclear Medicine and Biology JA - AOJNMB LA - en SN - 2322-5718 AU - Teramoto, Atsushi AU - Tsujimoto, Masakazu AU - Inoue, Takahiro AU - Tsukamoto, Tetsuya AU - Imaizumi, Kazuyoshi AU - Toyama, Hiroshi AU - Saito, Kuniaki AU - Fujita, Hiroshi AD - Fujita Health University AD - Fujita Health University Hospital AD - School of Medicine, Fujita Health Hniversity AD - School of Medicine, Fujita Health University AD - Department of Radiology, Fujita Health University AD - School of Health Sciences, Fujita Health University AD - Dep. Electrical, Electronic & Computer Engineering, Fac. Engineering Gifu University Y1 - 2019 PY - 2019 VL - 7 IS - 1 SP - 29 EP - 37 KW - classification KW - computer-aided diagnosis KW - Lung cancer KW - pulmonary nodule KW - PET/CT DO - 10.22038/aojnmb.2018.12014 N2 - 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. UR - https://aojnmb.mums.ac.ir/article_12014.html L1 - https://aojnmb.mums.ac.ir/article_12014_2737ff2538b8f01510e4148e0fa775b5.pdf ER -