Skip to main content
SpringerLink
Log in

Quantitative sonographic image analysis for hepatic nodules: a pilot study

  • Original Article
  • Published:
Journal of Medical Ultrasonics Aims and scope Submit manuscript

Abstract

Purpose

The aim of this study was to investigate the feasibility of quantitative image analysis to differentiate hepatic nodules on gray-scale sonographic images.

Methods

We retrospectively evaluated 35 nodules from 31 patients with hepatocellular carcinoma (HCC), 60 nodules from 58 patients with liver hemangioma, and 22 nodules from 22 patients with liver metastasis. Gray-scale sonographic images were evaluated with subjective judgment and image analysis using ImageJ software. Reviewers classified the shape of nodules as irregular or round, and the surface of nodules as rough or smooth.

Results

Circularity values were lower in the irregular group than in the round group (median 0.823, 0.892; range 0.641–0.915, 0.784–0.932, respectively; P = 3.21 × 10−10). Solidity values were lower in the rough group than in the smooth group (median 0.957, 0.968; range 0.894–0.986, 0.933–0.988, respectively; P = 1.53 × 10−4). The HCC group had higher circularity and solidity values than the hemangioma group. The HCC and liver metastasis groups had lower median, mean, modal, and minimum gray values than the hemangioma group. Multivariate analysis showed circularity [standardized odds ratio (OR), 2.077; 95 % confidential interval (CI) = 1.295–3.331; P = 0.002] and minimum gray value (OR 0.482; 95 % CI = 0.956–0.990; P = 0.001) as factors predictive of malignancy. The combination of subjective judgment and image analysis provided 58.3 % sensitivity and 89.5 % specificity with AUC = 0.739, representing an improvement over subjective judgment alone (68.4 % sensitivity, 75.0 % specificity, AUC = 0.701) (P = 0.008).

Conclusion

Quantitative image analysis for ultrasonic images of hepatic nodules may correlate with subjective judgment in predicting malignancy.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Mailloux GE, Bertrand M, Stampfler R, et al. Texture analysis of ultrasound B-mode images by segmentation. Ultrason Imaging. 1984;6:262–77.

    Article  CAS  PubMed  Google Scholar 

  2. Layer G, Zuna I, Loren A, et al. Computerized ultrasound B-scan texture analysis of experimental diffuse parenchymal liver disease: correlation with histopathology and tissue composition. J Clin Ultrasound. 1991;19:193–201.

    Article  CAS  PubMed  Google Scholar 

  3. Sujana H, Swarnamani S, Suresh S. Application of artificial neural networks for the classification of liver lesions by image texture parameters. Ultrasound Med Biol. 1996;22:1177–81.

    Article  CAS  PubMed  Google Scholar 

  4. Badawi AM, Derbala AS, Youssef AM. Fuzzy logic algorithm for quantitative tissue characterization of diffuse liver diseases from ultrasound images. Int J Med Inform. 1999;55:135–47.

    Article  CAS  PubMed  Google Scholar 

  5. Wang Y, Itou K, Taniguchi N, et al. Studies on tissue characterization by texture analysis with cooccurrence matrix method using ultrasonography and CT imaging. J Med Ultrason. 2002;29:211–23.

    Article  Google Scholar 

  6. Yamada H, Ebara M, Yamaguchi T, et al. A pilot approach for quantitative assessment of liver fibrosis using ultrasound: preliminary results in 79 cases. J Hepatol. 2006;44:68–75.

    Article  PubMed  Google Scholar 

  7. Jung EM, Clevert DA, Schreyer AG, et al. Evaluation of quantitative contrast harmonic imaging to assess malignancy of liver tumors: a prospective controlled two-center study. World J Gastroenterol. 2007;13:6356–64.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  8. Wakui N, Takayama R, Kanekawa T, et al. Usefulness of arrival time parametric imaging in evaluating the degree of liver disease progression in chronic hepatitis C infection. J Ultrasound Med. 2012;31:373–82.

    PubMed  Google Scholar 

  9. Yoshida H, Casalino DD, Keserci B, et al. Wavelet-packet-based texture analysis for differentiation between benign and malignant liver tumours in ultrasound images. Phys Med Biol. 2003;48:3735–53.

    Article  PubMed  Google Scholar 

  10. Mitrea D, Mitrea P, Nedevschi S, et al. Abdominal tumor characterization and recognition using superior-order cooccurrence matrices, based on ultrasound images. Comput Math Methods Med. 2012;2012:348135.

    PubMed Central  PubMed  Google Scholar 

  11. Virmani J, Kumar V, Kalra N, et al. Characterization of primary and secondary malignant liver lesions from B-mode ultrasound. J Digit Imaging. 2013;26:1058–70.

    Article  PubMed Central  PubMed  Google Scholar 

  12. ImageJ User guide. V Menu commands 30 Analyze. Available from http://imagej.nih.gov/ij/docs/guide/146-30.html#toc-Subsection-30.2.

  13. Makuuchi M, Hasegawa H, Yamazaki S, et al. Ultrasonic characteristics of the small hepatocellular carcinoma. Ultrasound Med Biol. 1983;489–91.

  14. Mirk P, Rubaltelli L, Bazzocchi M, et al. Ultrasonographic patterns in hepatic hemangiomas. J Clin Ultrasound. 1982;8:373–8.

    Article  Google Scholar 

  15. Ito H, Tsujimoto F, Nakajima Y, et al. Sonographic characterization of 271 hepatic hemangiomas with typical appearance on CT imaging. J Med Ultrasonics. 2012;39:61–8.

    Article  Google Scholar 

  16. Yoshida T, Matsue H, Okazaki N, et al. Ultrasonographic differentiation of hepatocellular carcinoma from metastatic liver cancer. J Clin Ultrasound. 1987;15:431–7.

    Article  CAS  PubMed  Google Scholar 

  17. Le Y, Xianze Xu, Li Zha, et al. Tumor boundary detection in ultrasound imagery using multi-scale generalized gradient vector flow. J Med Ultrasound. 2015;42:25–38.

    Article  Google Scholar 

  18. Kanai T, Hirohashi S, Upton MP, et al. Pathology of small hepatocellular carcinoma. A proposal for a new gross classification. Cancer. 1987;60:810–9.

    Article  CAS  PubMed  Google Scholar 

  19. Tsujimoto F, Abe T, Murakami Y, et al. Temporal changes of internal echoes in hepatic hemangiomas. J Jpn Radiol Soc. 1989;49:574–82.

    CAS  Google Scholar 

Download references

Conflict of interest

The authors declare that there is no conflict of interest. Naoki Matsumoto has no conflict of interest. Masahiro Ogawa has no conflict of interest. Kentaro Takayasu has no conflict of interest. Midori Hirayama has no conflict of interest. Takao Miura has no conflict of interest. Katsuhiko Shiozawa has no conflict of interest. Masahisa Abe has no conflict of interest. Hiroshi Nakagawara has no conflict of interest. Mitsuhiko Moriyama has no conflict of interest. Seiichi Udagawa has no conflict of interest.

Ethical standard

All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2008. Informed consent was obtained from all patients for being included in the study.

Author information

Authors and Affiliations

  1. Division of Gastroenterology and Hepatology, Department of Medicine, Nihon University School of Medicine, 30-1 Oyaguchi Kamimachi, Itabashi, Tokyo, 173-8610, Japan

    Naoki Matsumoto, Masahiro Ogawa, Kentaro Takayasu, Midori Hirayama, Takao Miura, Katsuhiko Shiozawa, Masahisa Abe, Hiroshi Nakagawara & Mitsuhiko Moriyama

  2. Department of Mathematics, Nihon University School of Medicine, 30-1 Oyaguchi Kamimachi, Itabashi, Tokyo, 173-8610, Japan

    Seiichi Udagawa

Authors
  1. Naoki Matsumoto
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Masahiro Ogawa
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kentaro Takayasu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Midori Hirayama
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Takao Miura
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Katsuhiko Shiozawa
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Masahisa Abe
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Hiroshi Nakagawara
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Mitsuhiko Moriyama
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Seiichi Udagawa
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Naoki Matsumoto.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Matsumoto, N., Ogawa, M., Takayasu, K. et al. Quantitative sonographic image analysis for hepatic nodules: a pilot study. J Med Ultrasonics 42, 505–512 (2015). https://doi.org/10.1007/s10396-015-0627-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10396-015-0627-3

Keywords

Search

Navigation