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Computer-aided detection of bone metastasis in bone scintigraphy images using parallelepiped classification method

Annals of Nuclear Medicine volume 33, pages 866–874 (2019)Cite this article

Abstract

Objective

Accurate diagnosis of metastatic tissue on bone scintigraphy images is of paramount importance in making treatment decisions. Although several automated systems have developed, more and better interpretation methods are still being sought. In the present study, a new modality for bone metastasis detection from bone scintigraphy images using parallelepiped classification (PC) as method for mapping the radionuclide distribution is presented.

Methods

Bone scintigraphy images from 12 patients with bone metastases were analyzed using the parallelepiped classifier that generated color maps of scintigraphic images. Seven classes of radionuclide accumulation have been identified and fed into machine learning software. The accuracy of the proposed method was evaluated by statistical measurements in a confusion matrix. Overall accuracy, producer’s and user’s accuracies and κ coefficient were computed from each confusion matrix associated with the individual case.

Results

The results revealed that the method is sufficiently precise to differentiate the metastatic bone from normal tissue (overall classification accuracy = 87.58 ± 2.25% and κ coefficient = 0.8367 ± 0.0252). The maps are easier to read (due to better contrast) and can detect even slightest differences in accumulation levels among pixels.

Conclusions

In conclusion, these preliminary data suggest that bone scintigraphy combined with PC method could play an important role in the detection of bone metastasis, allowing for an easier but correct interpretation of the images, with effects on the diagnosis accuracy and decision making on the treatment to be applied.

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Funding

This study was funded by the Romanian Ministry of Research and Innovation (Grant number PN 33N/16.03.2018).

Author information

Affiliations

  1. “Saint John” Emergency Clinical Hospital, 13 Vitan-Barzesti Street, Bucharest, Romania

    Florina-Gianina Elfarra

  2. Faculty of Physics, The University of Bucharest, 405 Atomistilor Street, 077125, Magurele, Romania

    Florina-Gianina Elfarra

  3. National Institute of Research and Development for Optoelectronics INOE 2000, 409 Atomistilor Street, P.O. Box MG5, 077125, Magurele, Ilfov, Romania

    Mihaela Antonina Calin

  4. Carol Davila University of Medicine and Pharmacy, 37 Dionisie Lupu Street, 020022, Bucharest, Romania

    Sorin Viorel Parasca

Authors
  1. Florina-Gianina Elfarra
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  2. Mihaela Antonina Calin
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  3. Sorin Viorel Parasca
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Contributions

All authors contributed to the study conception and design. Data acquisition was performed by F-GE. The data processing and analysis were performed by F-GE and MAC. Interpretation of the results was carried out by SVP and MAC. All authors contributed to the writing of the manuscript. All authors read and approved the final version of the manuscript.

Corresponding author

Correspondence to Mihaela Antonina Calin.

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Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in this study involving human participants were in accordance with the ethical standards of the “Saint John” Emergency Clinical Hospital Research Committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.

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Elfarra, FG., Calin, M.A. & Parasca, S.V. Computer-aided detection of bone metastasis in bone scintigraphy images using parallelepiped classification method. Ann Nucl Med 33, 866–874 (2019). https://doi.org/10.1007/s12149-019-01399-w

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  • DOI: https://doi.org/10.1007/s12149-019-01399-w

Keywords

  • Cancer
  • Bone
  • Nuclear imaging
  • Radionuclide accumulation
  • Parallelepiped classification
  • Machine learning