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  P3.19 - Poster Session 3 - Imaging (ID 181)

  • Event: WCLC 2013
  • Type: Poster Session
  • Track: Imaging, Staging & Screening
  • Presentations: 1
  • Moderators:
  • Coordinates: 10/30/2013, 09:30 - 16:30, Exhibit Hall, Ground Level

  • 12807

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    P3.19-016 - Fast Fourier transform analysis for the contour of pulmonary nodules. (ID 2641)

    09:30 - 09:30  |  Author(s): M. Kawago
    • Abstract

    Background
    Differential diagnosis of primary lung cancer and metastatic lung tumor before surgery is important. However, histological diagnosis using bronchofiberscopy is often difficult in these small peripheral lung nodules. It appears to be useful to diagnose pulmonary nodules using chest CT. As already known, primary lung cancer presents complicated appearance in chest CT. Contour of primary lung cancer is expressed using the words such as undulated, irregular, and spiculated. Contrary, metastatic lung tumor usually shows simple round shadow. These characteristics are used for the differential diagnosis of tumors. However, we often meet tumors with borderline complexity that we are not able to clearly classify. Chest CT finding is expressed by words at the diagnosis. Therefore, it is difficult to standardize or compare the diagnostic properties. Numerical evaluation of complexity of tumor outline results in the quantitative evaluation of tumor shape and may help the standardization of diagnosis of pulmonary nodules on chest CT. Malignant pulmonary tumors basically show round appearance. Therefore, complexity of tumor outline is to be expressed by the deviation from a circle. And the extent of deviation can be expressed numerically. The array data set of the deviation is to be regarded as the composition of various kinds of waves. Fast Fourier transform (FFT) analysis is suitable to evaluate these components of the wave data. In this study, we performed the quantitative analysis for the complexity of tumor outline of both primary lung cancer and metastatic lung tumor utilizing FFT analysis. And then we evaluated the usefulness and adequacy of our evaluation method.

    Methods
    Sequential cases of 72 histologically proven primary lung cancers (Group PL) and 54 metastatic lung tumors (Group MT) were included. The diameters of tumors in groups PL and MT were 18.9±7.4 mm and 12.2±6.1 mm, respectively. 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: PL (r =0.667, P <0.0001) and MT (r = 0.809, P <0.0001). 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
    Complexity of outline of the pulmonary nodules can be evaluated quantitatively using FFT analysis. This analytical procedure was designed from the beginning as it can be equipped on the graphic workstation, and we are now starting to develop it. This analytical method will help the diagnosis of primary lung cancer. FFT analysis appears useful for quantification of complexity of the tumor outline.