Skip to main content
Springer Nature Link
Log in

Correlation Between IBSI Morphological Features and Manually-Annotated Shape Attributes on Lung Lesions at CT

  • Conference paper
  • First Online:
Medical Image Understanding and Analysis (MIUA 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13413))

Included in the following conference series:

  • 2750 Accesses

Abstract

Radiological examination of pulmonary nodules on CT involves the assessment of the nodules’ size and morphology, a procedure usually performed manually. In recent years computer-assisted analysis of indeterminate lung nodules has been receiving increasing research attention as a potential means to improve the diagnosis, treatment and follow-up of patients with lung cancer. Computerised analysis relies on the extraction of objective, reproducible and standardised imaging features. In this context the aim of this work was to evaluate the correlation between nine IBSI-compliant morphological features and three manually-assigned radiological attributes – lobulation, sphericity and spiculation. Experimenting on 300 lung nodules from the open-access LIDC-IDRI dataset we found that the correlation between the computer-calculated features and the manually-assigned visual scores was at best moderate (Pearson’s r between -0.61 and 0.59; Spearman’s \(\rho \) between -0.59 and 0.56). We conclude that the morphological features investigated here have moderate ability to match/explain manually-annotated lobulation, sphericity and spiculation.

This work was partially supported by the Department of Engineering at the Università degli Studi di Perugia, Italy, through the project Shape, colour and texture features for the analysis of two- and three-dimensional images: methods and applications (Fundamental Research Grants Scheme 2019).

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Aberle, D.R., et al.: Reduced lung-cancer mortality with low-dose computed tomographic screening. N. Engl. J. Med. 365(5), 395–409 (2011)

    Article  Google Scholar 

  2. Angelidakis, V., Nadimi, S., Utili, S.: Elongation, flatness and compactness indices to characterise particle form. Powder Technol. 396, 689–695 (2022)

    Google Scholar 

  3. Armato III, S.G., et al.: Data from LIDC-IDRI, The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2015.LO9QL9SX. Accessed 29 Jan 2022

  4. Armato III, S.G., et al.: Data from LIDC-IDRI, The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2015.LO9QL9SX. Accessed 29 Jan 2022

  5. Baessler, B.: Radiomics and imaging: the need for standardisation. HealthManag. .l 20(2), 168–170 (2020)

    Google Scholar 

  6. Balagurunathan, Y., Schabath, M.B., Wang, H., Liu, Y., Gillies, R.J.: Quantitative imaging features improve discrimination of malignancy in pulmonary nodules. Sci. Rep. 9(1), 8528(2019)

    Google Scholar 

  7. Bianconi, F., Palumbo, I., Spanu, A., Nuvoli, S., Fravolini, M.L., Palumbo, B.: PET/CT radiomics in lung cancer: an overview. Appl. Sci. 5(10) (Mar 2020)

    Google Scholar 

  8. Bianconi, F., Palumbo, I., Spanu, A., Nuvoli, S., Fravolini, M.L., Palumbo, B.: PET/CT radiomics in lung cancer: an overview. Appl. Sci. 5(10) (Mar 2020)

    Google Scholar 

  9. Clark, K., et al.: The cancer imaging archive (TCIA): Maintaining and operating a public information repository. J. Digit. Imag. 26(6), 1045–1057 (2013)

    Google Scholar 

  10. Coroller, T.P., et al.: Radiomic phenotype features predict pathological response in non-small cell radiomic predicts pathological response lung cancer. Radiot. Oncol. 119(3), 480–486 (2016)

    Google Scholar 

  11. Dhara, A.K., Mukhopadhyay, S., Dutta, A., Garg, M., Khandelwal, N.: A combination of shape and texture features for classification of pulmonary nodules in lung CT images. J. Digit. Imag. 29(4), 466–475 (2016)

    Google Scholar 

  12. Grove, O., et al.: Quantitative computed tomographic descriptors associate tumor shape complexity and intratumor heterogeneity with prognosis in lung adenocarcinoma. PLoS ONE 10(3), e0118261 (2015)

    Google Scholar 

  13. Hancock, M.: Pylidc documentation. https://pylidc.github.io/index.html. Accessed 5 Feb 2022

  14. Hassani, C., Varghese, B., Nieva, J., Duddalwar, V.: Radiomics in pulmonary lesion imaging. Am. J. Roentgenol. 212(3), 497–504 (2019)

    Google Scholar 

  15. Hatt, M., Vallieres, M., Visvikis, D., Zwanenburg, A.: IBSI: an international community radiomics standardization initiative. J. Nucl. Medi. 59(1 supp. 287) (2018)

    Google Scholar 

  16. Ibrahim, A., et al.: Radiomics for precision medicine: Current challenges, future prospects, and the proposal of a new framework. Methods 188, 20–29 (2021)

    Article  Google Scholar 

  17. Limkin, E.J., et al.:The complexity of tumor shape, spiculatedness, correlates with tumor radiomic shape features. Sci. Rep. 9(1), 4329 (2019)

    Google Scholar 

  18. MacMahon, H., et al.: Guidelines for management of incidental pulmonary nodules detected on CT images: from the Fleischner Society 2017. Radiology 284(1), 228–243 (2017)

    Google Scholar 

  19. McWilliams, A., et al.: Probability of cancer in pulmonary nodules detected on first screening CT. N. Eng. J. Med. 369(10), 910–919 (2013)

    Article  Google Scholar 

  20. Overholser, B.R., Sowinski, K.M.: Biostatistics primer: Part 2. Nutr. Clin. Prac. 23(1), 76–84 (2008)

    Article  Google Scholar 

  21. Palumbo, B., et al.: Value of shape and texture features from 18F-FDG PET/CT to discriminate between benign and malignant solitary pulmonary nodules: An experimental evaluation. Diagnostics 10, 696 (2020)

    Google Scholar 

  22. Qiu, S., Sun, J., Zhou, T., Gao, G., He, Z., Liang, T.: Spiculation sign recognition in a pulmonary nodule based on spiking neural P systems. BioMed. Res. Int. 2020, 6619076 (2020)

    Google Scholar 

  23. Rundo, L., et al.: A low-dose CT-based radiomic model to improve characterization and screening recall intervals of indeterminate prevalent pulmonary nodules. Diagnostics 11(9), 1610 (2021)

    Google Scholar 

  24. Scrivener, M., de Jong, E., van Timmeren, Pieters, T., Ghaye, B., Geets, X.: Radiomics applied to lung cancer: a review. Trans. Cancer Res.5(4), 398–409 (2016)

    Google Scholar 

  25. Shaikh, F., et al.: Technical challenges in the clinical application of radiomics. JCO Clin. Cancer Inform. 2017, 1–8 (2017)

    Google Scholar 

  26. Snoeckx, A., et al.: Evaluation of the solitary pulmonary nodule: size matters, but do not ignore the power of morphology. Insights into Imag. 9(1), 73–86 (2017). https://doi.org/10.1007/s13244-017-0581-2

  27. van der Walt, S., et al.: scikit-image: Image processing in Python. Peer J. 2, e453 (2014). https://peerj.com/articles/453/

  28. Various authors: The image biomarker standardisation initiative, https://ibsi.readthedocs.io/en/latest/index.html. Accessed 4 May 2021

  29. Wu, W., Hu, H., Gong, J., Li, X., Huang, G., Nie, S.: Malignant-benign classification of pulmonary nodules based on random forest aided by clustering analysis. Phys. Med. Biol. 64(3), 035017 (2019)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Engineering, Università degli Studi di Perugia, Via Goffredo Duranti, 93, 06125, Perugia, Italy

    Francesco Bianconi & Mario Luca Fravolini

  2. Department of Mechanical and Aerospace Engineering (DIMEAS), Politecnico di Torino, Corso Duca Degli Abruzzi, 24, 10129, Turin, Italy

    Giulia Pascoletti

  3. Section of Radiation Oncology, Department of Medicine and Surgery Università degli Studi di Perugia, Piazza Lucio Severi 1, 06132, Perugia, Italy

    Isabella Palumbo & Cynthia Aristei

  4. Division of Diagnostic Imaging, Department of Medicine and Surgery, Piazza Lucio Severi 1, 06132, Perugia, Italy

    Michele Scialpi

  5. Section of Nuclear Medicine and Health Physics, Department of Medicine and Surgery Università Degli Studi di Perugia, Piazza Lucio Severi 1, 06132, Perugia, Italy

    Barbara Palumbo

Authors
  1. Francesco Bianconi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mario Luca Fravolini
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Giulia Pascoletti
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Isabella Palumbo
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Michele Scialpi
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Cynthia Aristei
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Barbara Palumbo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Francesco Bianconi .

Editor information

Editors and Affiliations

  1. Imperial College London, London, UK

    Guang Yang

  2. University of Cambridge, Cambridge, UK

    Angelica Aviles-Rivero

  3. University of Cambridge, Cambridge, UK

    Michael Roberts

  4. University of Cambridge, Cambridge, UK

    Carola-Bibiane Schönlieb

Ethics declarations

Ethical Statement

The present study is based on publicly available data in anonymous form, therefore does not constitute research on human subjects.

Conflict of Interest

The authors declare no conflict of interest.

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Bianconi, F. et al. (2022). Correlation Between IBSI Morphological Features and Manually-Annotated Shape Attributes on Lung Lesions at CT. In: Yang, G., Aviles-Rivero, A., Roberts, M., Schönlieb, CB. (eds) Medical Image Understanding and Analysis. MIUA 2022. Lecture Notes in Computer Science, vol 13413. Springer, Cham. https://doi.org/10.1007/978-3-031-12053-4_56

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-12053-4_56

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-12052-7

  • Online ISBN: 978-3-031-12053-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics