Annals of Nuclear Medicine

, Volume 28, Issue 4, pp 329–339 | Cite as

Investigation of computer-aided diagnosis system for bone scans: a retrospective analysis in 406 patients

  • Osamu Tokuda
  • Yuko Harada
  • Yona Ohishi
  • Naofumi Matsunaga
  • Lars Edenbrandt
Original Article

Abstract

Objective

The aim of this study was to investigate the diagnostic ability of a completely automated computer-assisted diagnosis (CAD) system to detect metastases in bone scans by two patterns: one was per region, and the other was per patient.

Materials and methods

This study included 406 patients with suspected metastatic bone tumors who underwent whole-body bone scans that were analyzed by the automated CAD system. The patients were divided into four groups: a group with prostatic cancer (N = 71), breast cancer (N = 109), males with other cancers (N = 153), and females with other cancers (N = 73). We investigated the bone scan index and artificial neural network (ANN), which are parameters that can be used to classify bone scans to determine whether there are metastases. The sensitivities, specificities, positive predictive value (PPV), negative predictive value (NPV), and accuracies for the four groups were compared. Receiver operating characteristic (ROC) analyses of region-based ANN were performed to compare the diagnostic performance of the automated CAD system.

Results

There were no significant differences in the sensitivity, specificity, or NPV between the four groups. The PPVs of the group with prostatic cancer (51.0 %) were significantly higher than those of the other groups (P < 0.01). The accuracy of the group with prostatic cancer (81.5 %) was significantly higher than that of the group with breast cancer (68.6 %) and the females with other cancers (65.9 %) (P < 0.01). For the evaluation of the ROC analysis of region-based ANN, the highest Az values for the groups with prostatic cancer, breast cancer, males with other cancers, and females with other cancers were 0.82 (ANN = 0.4, 0.5, 0.6, 0.7, and 0.8), 0.83 (ANN = 0.7), 0.81 (ANN = 0.5), and 0.81 (ANN = 0.6), respectively.

Conclusion

The special CAD system “BONENAVI” trained with a Japanese database appears to have significant potential in assisting physicians in their clinical routine. However, an improved CAD system depending on the primary lesion of the cancer is required to decrease the proportion of false-positive findings.

Keywords

Bone scan Computer-assisted diagnosis Prostatic cancer Breast cancer Bone metastases 

References

  1. 1.
    Sadik M, Jakobsson D, Olofsson F, Ohlsson M. Suurkula, Edenbrandt L. A new computer-based decision-support system for the interpretation of bone scans. Nucl Med Commun. 2006;27:417–23.PubMedCrossRefGoogle Scholar
  2. 2.
    Maffioli L, Florimonte L, Pagani L, Bitti I, Roca I. Current role of bone scan with phosphonate in the follow-up of breast cancer. Eur J Nucl Med Mol Imaging. 2004;31:143–8.CrossRefGoogle Scholar
  3. 3.
    Bombardieri E, Aktolun C, Baum RP, Bishof-Delaloye A, Buscombe J, Chatal JF, et al. Bone scintigraphy procedures guidelines for tumor imaging. Eur J Nucl Med Mol Imaging. 2003;30:107–14.Google Scholar
  4. 4.
    Sadik M, Suurkula M, Höglund P, Järund A, Edenbrandt L. Improved classifications of planar whole-body bone scans using a computer-assisted diagnosis system: a multicenter, multiple-reader, multi-case study. J Nucl Med. 2009;50:368–75.PubMedCrossRefGoogle Scholar
  5. 5.
    Doi K. Computer-aided diagnosis in medical imaging: historical review, current status and future potential. Comput Med Imaging Graph. 2007;31:198–211.PubMedCentralPubMedCrossRefGoogle Scholar
  6. 6.
    Goldin JG, Brown MS, Petkovska I. Computer-aided diagnosis in lung nodule assessment. J Thorac Imaging. 2008;23:97–104.PubMedCrossRefGoogle Scholar
  7. 7.
    Kawamoto K, Houlihan CA, Balas EA, Lobach DF. Improving clinical practice using clinical decision support systems: a systemic review of trials to identify features critical to success. BMJ. 2005;330:765–8.PubMedCentralPubMedCrossRefGoogle Scholar
  8. 8.
    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.PubMedCrossRefGoogle Scholar
  9. 9.
    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.PubMedGoogle Scholar
  10. 10.
    Brown MS, Chu GH, Kim HJ, Allen-Auerbach Poon CM, Bridges J, Vidovic A, Ramakrishna B, Ho J, Morris MJ, Larson SM, Scher HI, Goldin JG. Computer-aided quantitative bone scan assessment of prostate cancer treatment response. Nucl Med Commun. 2012;33(4):384–94.PubMedCentralPubMedCrossRefGoogle Scholar
  11. 11.
    Sadik M, Hamadeh I, Nordblom P, Suurkula M, Höglund P, Ohlsson M, et al. Computer-assisted interpretation of planar whole-body bone scans. J Nucl Med. 2008;49:1958–65.PubMedCrossRefGoogle Scholar
  12. 12.
    Burhene LJ, Wood SA, D’Orsi CJ, Feig SA, Kopans DB, O’Shaughnessy KF, et al. Potential contribution of computer-aided detection to the sensitivity of screening mammography. Radiology. 2000;215:554–62.CrossRefGoogle Scholar
  13. 13.
    Birdwell RL, Bandodkar P, Ikeda DM. Computer-aided detection with screening mammography in a university hospital setting. Radiology. 2005;236:451–7.PubMedCrossRefGoogle Scholar
  14. 14.
    Cupples TE, Cunningham JE, Reynolds JC. Impact of computer-aided detection in a regional screening mammography program. AJR. 2005;185:944–50.PubMedCrossRefGoogle Scholar
  15. 15.
    Freer TW, Uissery MJ. Screening mammography with computer-aided detection: prospective study of 12,869 patients in a community breast center. Radiology. 2001;220:781–6.PubMedCrossRefGoogle Scholar
  16. 16.
    Baker ME, Bogoni L, Obuchowski NA, Dass C, Kendzierski RM, Remer EM, et al. Computer-aided detection of colorectal polyps: can it improve sensitivity of less experienced readers? Preliminary findings. Radiology. 2007;245:140–9.PubMedCrossRefGoogle Scholar
  17. 17.
    Tögil K, Bondouy M, Chaborel JP, Djaballah W, Franken PR, Grandpierre S, et al. A decision support system improves the interpretation of myocardial perfusion imaging. Eur J Nucl Med Mol Imaging. 2008;35:1602–7.CrossRefGoogle Scholar
  18. 18.
    Horikoshi H, Kukuchi A, Onoguchi M, Sjöstrand K, Edenbrandt L. Computer-aided diagnosis system for bone scintigrams from Japanese patients: importance of training database. Ann Nucl Med. 2012;26:622–6.PubMedCentralPubMedCrossRefGoogle Scholar
  19. 19.
    Kukuchi A, Onoguchi M, Horikoshi H, Sjöstrand K, Edenbrandt L. Automated segmentation of the skeleton in whole-body bone scans: influence of difference in atlas. Nucl Med Commun. 2012;33:947–53.CrossRefGoogle Scholar
  20. 20.
    Imbriaco M, Larson SM, Yeung H, Mawlawi OR, Erdi Y, Venkatraman ES, et al. A new parameter for measuring metastatic bone involvement by prostate cancer: the bone scan index. Clin Cancer Res. 1998;4:1765–72.PubMedGoogle Scholar
  21. 21.
    Sadik M, Suurkula M, Höglund P, Järund A, Edenbrandt L. Quality of planar whole-body bone scan interpretations: a nationwide survey. Eur J Nucl Med Mol Imaging. 2008;35:1464–72.PubMedCrossRefGoogle Scholar
  22. 22.
    Messiou C, Cook G, de Souza MM. Imaging metastatic bone disease from carcinoma of the prostate. Br J Cancer. 2009;101:1225–32.PubMedCentralPubMedCrossRefGoogle Scholar
  23. 23.
    Cheville JC, Tindall D, Boelter C, Jenkins R, Lohse CM, Pankratz VS, et al. Metastatic prostate carcinoma to bone. Cancer. 2002;95:1028–36.PubMedCrossRefGoogle Scholar
  24. 24.
    Costelloe C, Rohren E, Madewell J, Hamaoka T, Theriault RL, Yu TK, et al. Imaging bone metastases in breast cancer: techniques and recommendations for diagnosis. Lancet Oncol. 2009;10:606–14.PubMedCrossRefGoogle Scholar
  25. 25.
    Reinbold WD, Genant HK, Reiser UJ, Harris ST, Ettinger B. Bone mineral content in early-postmenopausal and postmenopausal osteoporotic women: comparison of measurement methods. Radiology. 1986;160:469–78.PubMedGoogle Scholar
  26. 26.
    Yamaguchi T, Tamai K, Yamato M, Honma K, Ueda Y, Saotome K. Intertrabecular pattern of tumors metastatic to bone. Cancer. 1996;78:1388–94.PubMedCrossRefGoogle Scholar
  27. 27.
    Soubrier M, Dubost JJ, Boisgard S, Sauvezie B, Gaillard P, Michel JL, et al. Insufficiency fracture. A survey of 60 cases and review of the literature. Jt Bone Spine. 2003;70:209–18.CrossRefGoogle Scholar

Copyright information

© The Japanese Society of Nuclear Medicine 2014

Authors and Affiliations

  • Osamu Tokuda
    • 1
  • Yuko Harada
    • 1
  • Yona Ohishi
    • 1
  • Naofumi Matsunaga
    • 1
  • Lars Edenbrandt
    • 2
  1. 1.Department of RadiologyYamaguchi University Graduate School of MedicineUbeJapan
  2. 2.Department of Molecular and Clinical Medicine, Sahlgrenska AcademyGothenburg UniversityGöteborgSweden

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