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.
Similar content being viewed by others
References
Coleman RE. (2001) Metastatic bone disease: clinical features, pathophysiology and treatment strategies. Cancer Treat Rev. 2001;27:165–76.
Lukaszewski B, Nazar J, Goch M, Lukaszewska M, Stepinski A, Jurczyk MU. Diagnostic methods for detection of bone metastases. Contemp Oncol (Pozn). 2017;21:98–103. https://doi.org/10.5114/wo.2017.68617.
Woolf DK, Padhani AR, Makris A. Assessing response to treatment of bone metastases from breast cancer: what should be the standard of care? Ann Oncol. 2015;26:1048–57. https://doi.org/10.1093/annonc/mdu558.
Del Vescovo R, Frauenfelder G, Francesco Giurazza F, et al. Role of whole-body diffusion-weighted MRI in detecting bone metastasis. Radiol Med (Torino). 2014;119:758–66. https://doi.org/10.1007/s11547-014-0395-y.
Nakajima K, Nakajima Y, Horikoshi H, et al. Enhanced diagnostic accuracy for quantitative bone scan using an artificial neural network system: a Japanese multi-center database project. EJNMMI Res. 2013;3:83. https://doi.org/10.1186/2191-219X-3-83.
Doi K. Computer-aided diagnosis in medical imaging: historical review, current status and future potential. Comput Med Imaging Graph. 2007;31:198–21111.
Taylor P, Potts HW. Computer aids and human second reading as interventions in screening mammography: two systematic reviews to compare effects on cancer detection and recall rate. Eur J Cancer. 2008;44:798–807.
Suzuki K. A supervised 'lesion-enhancement' filter by use of a massive-training artificial neural network (MTANN) in computer-aided diagnosis (CAD). Phys Med Biol. 2009;54:31–45.
Petrick N, Haider M, Summers RM, Yeshwant SC, Brown L, Iuliano EM, Louie A, Choi JR, Pickhardt PJ. CT colonography with computer-aided detection as a second reader: observer performance study. Radiology. 2008;246:148–56.
Mazzetti S, Giannini V, Russo F, Regge D. Computer-aided diagnosis of prostate cancer using multi-parametric MRI: comparison between PUN and Tofts models. Phys Med Biol. 2018;63:095004. https://doi.org/10.1088/1361-6560/aab956.
Kang KW, Chang HJ, Shim H, Kim YJ, Choi BW, Yang WI, Shim JY, Ha J, Chung N. Feasibility of an automatic computer-assisted algorithm for the detection of significant coronary artery disease in patients presenting with acute chest pain. Eur J Radiol. 2012;81:e640–e646646. https://doi.org/10.1016/j.ejrad.2012.01.017.
Dong ZC. Detection of subjects and brain regions related to Alzheimer's disease using 3D MRI scans based on eigenbrain and machine learning. Front Comput Neurosci. 2015;66:1–15.
Erdi YE, Humm JL, Imbriaco M, Yeung H, Larson SM. Quantitative bone metastases analysis based on image segmentation. J Nucl Med. 1997;38:1401–6.
Yin TK, Chiu NT. A computer-aided diagnosis for locating abnormalities in bone scintigraphy by a fuzzy system with a three-step minimization approach. IEEE Trans Med Imaging. 2004;23:639–54.
Sajn L, Kukar M, Kononenko I, Milcinski M. Computerized segmentation of whole-body bone scintigrams and its use in automated diagnostics. Comput Methods Progr Biomed. 2005;80:47–55.
Sadik M, Hamadeh I, Nordblom P, Suurkula M, Hoglund P, Ohlsson M, Edenbrandt L. Computer-assisted interpretation of planar whole-body bone scans. J Nucl Med. 2008;49:1958–65.
Kikuchi A, Onoguchi M, Horikoshi H, Sjostrand K, Edenbrandt L. Automated segmentation of the skeleton in whole-body bone scans: influence of difference in atlas. Nucl Med Commun. 2012;3:947–53.
Horikoshi H, Kikuchi A, Onoguchi M, Sjostrand K, Edenbrandt L. Computer-aided diagnosis system for bone scintigrams from Japanese patients: importance of training database. Ann Nucl Med. 2012;3:622–6.
Koizumi M, Miyaji N, Murata T, Motegi K, Miwa K, Koyama M, Terauchi T, Wagatsuma K, Kawakami K, Richter J. Evaluation of a revised version of computer-assisted diagnosis system, BONENAVI version 2.1.7, for bone scintigraphy in cancer patients. Ann Nucl Med. 2015;29:659–65.
Ogawa K, Sakata M, Li Y. Adaptive noise reduction of scintigrams with a wavelet transform. Int J Biomed Imaging. 2012. https://doi.org/10.1155/2012/130482 (ID 130482).
Kuwahara M, Hachimura K, Ehiu S, Kinoshita M. Processing of riangiocardiographic images. Digit Process Biomed Images N Y. 1976;1980:187–203.
Schowengerdt RA. Remote sensing: models and methods for image processing. 3rd ed. San Diego: Academic Press; 1997. p. 411–412.
Cohen J. A coefficient of agreement for nominal scales. Educ Psychol Meas. 1960;20:37–46.
Landis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics. 1997;33:159–74.
Funding
This study was funded by the Romanian Ministry of Research and Innovation (Grant number PN 33N/16.03.2018).
Author information
Authors and Affiliations
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
Ethics declarations
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.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
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
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12149-019-01399-w