Fast Fourier Transform Analysis of Pulmonary Nodules on Computed Tomography Images from Patients with Lung Cancer

Tatsuya Yoshimasu; Mitsumasa Kawago; Yoshimitsu Hirai; Takuya Ohashi; Yumi Tanaka; Shoji Oura; Yoshitaka Okamura

doi:10.5761/atcs.oa.13-00318

Original Articles

Fast Fourier Transform Analysis of Pulmonary Nodules on Computed Tomography Images from Patients with Lung Cancer

Tatsuya Yoshimasu, Mitsumasa Kawago, Yoshimitsu Hirai, Takuya Ohashi, Yumi Tanaka, Shoji Oura, Yoshitaka Okamura

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Keywords: fast Fourier transform, computed tomography, primary lung cancer, metastatic lung tumor, quantitative evaluation

JOURNAL FREE ACCESS

2015 Volume 21 Issue 1 Pages 1-7

DOI https://doi.org/10.5761/atcs.oa.13-00318

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Abstract

Purpose: The outlines of primary lung cancers are more complicated than those of metastatic lung tumors on computed tomography (CT) images. This feature is useful for clarifying the diagnosis of pulmonary nodules before surgery. We applied fast Fourier transform (FFT) analysis for quantification of complexity of tumor outline.Methods: Sequential cases of 72 primary lung cancers (Group PL) and 54 metastatic lung tumors (Group MT) were included. The outline of each tumor on chest CT images was described using polar coordinates, and converted to rectangular coordinates, yielding wave data of the tumor outline. The FFT was then used to analyze the wave data. The complexity index (Cxi) was defined as the sum of the amplitude of all harmonics over a fundamental frequency.Results: The Cxi was higher (P <0.0001) for group PL (10.3 ± 6.7 mm) than for group MT (3.2 ± 2.4 mm), and it was correlated with tumor diameter in both groups. The cut-off equation “Cxi = 0.127 DT + 2.23” provided the highest diagnostic accuracy for distinguishing Group PL from Group MT such as a sensitivity of 95.8%, a specificity of 81.5%, and an accuracy of 89.7%.Conclusion: FFT analysis appears useful for quantification of complexity of the tumor outline.

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