Skip to main content
SpringerLink
Log in

Computed tomography (CT)-derived radiomic features differentiate prevascular mediastinum masses as thymic neoplasms versus lymphomas

  • COMPUTED TOMOGRAPHY
  • Published:
La radiologia medica Aims and scope Submit manuscript

Abstract

Objectives

We aimed to assess the ability of radiomics, applied to not-enhanced computed tomography (CT), to differentiate mediastinal masses as thymic neoplasms vs lymphomas.

Methods

The present study was an observational retrospective trial. Inclusion criteria were pathology-proven thymic neoplasia or lymphoma with mediastinal localization, availability of CT. Exclusion criteria were age < 16 years and mediastinal lymphoma lesion < 4 cm. We selected 108 patients (M:F = 47:61, median age 48 years, range 17–79) and divided them into a training and a validation group. Radiomic features were used as predictors in linear discriminant analysis. We built different radiomic models considering segmentation software and resampling setting. Clinical variables were used as predictors to build a clinical model. Scoring metrics included sensitivity, specificity, accuracy and area under the curve (AUC). Wilcoxon paired test was used to compare the AUCs.

Results

Fifty-five patients were affected by thymic neoplasia and 53 by lymphoma. In the validation analysis, the best radiomics model sensitivity, specificity, accuracy and AUC resulted 76.2 ± 7.0, 77.8 ± 5.5, 76.9 ± 6.0 and 0.84 ± 0.06, respectively. In the validation analysis of the clinical model, the same metrics resulted 95.2 ± 7.0, 88.9 ± 8.9, 92.3 ± 8.5 and 0.98 ± 0.07, respectively. The AUCs of the best radiomic and the clinical model not differed.

Conclusions

We developed and validated a CT-based radiomic model able to differentiate mediastinal masses on non-contrast-enhanced images, as thymic neoplasms or lymphoma. The proposed method was not affected by image postprocessing. Therefore, the present image-derived method has the potential to noninvasively support diagnosis in patients with prevascular mediastinal masses with major impact on management of asymptomatic cases.

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

Similar content being viewed by others

References

  1. Carter BW, Okumura M, Detterbeck FC, Marom EM (2014) Approaching the patient with an anterior mediastinal mass: a guide for radiologists. J Thorac Oncol 9:S110–S118. https://doi.org/10.1097/JTO.0000000000000295

    Article  PubMed  Google Scholar 

  2. Carter BW, Benveniste MF, Madan R et al (2017) ITMIG classification of mediastinal compartments and multidisciplinary approach to mediastinal masses. Radiographics 37:413–436. https://doi.org/10.1148/rg.2017160095

    Article  PubMed  Google Scholar 

  3. Carter BW, Marom EM, Detterbeck FC (2014) Approaching the patient with an anterior mediastinal mass: a guide for clinicians. J Thorac Oncol 9(9):S102–S109

    Article  Google Scholar 

  4. Dreyling M, Thieblemont C, Gallamini A et al (2013) ESMO consensus conferences: guidelines on malignant lymphoma. Part 2: marginal zone lymphoma, mantle cell lymphoma, peripheral T-cell lymphoma. Ann Oncol Off J Eur Soc Med Oncol 24:857–877. https://doi.org/10.1093/annonc/mds643

    Article  CAS  Google Scholar 

  5. Eichenauer DA, Engert A, Andre M et al (2014) Hodgkin’s lymphoma: ESMO clinical practice guidelines for diagnosis, treatment and follow-up. Ann Oncol 25:iii70–iii75. https://doi.org/10.1093/annonc/mdu181

    Article  PubMed  Google Scholar 

  6. Ghielmini M, Vitolo U, Kimby E et al (2013) ESMO guidelines consensus conference on malignant lymphoma 2011 part 1: diffuse large B-cell lymphoma (DLBCL), follicular lymphoma (FL) and chronic lymphocytic leukemia (CLL). Ann Oncol Off J Eur Soc Med Oncol 24:561–576. https://doi.org/10.1093/annonc/mds517

    Article  CAS  Google Scholar 

  7. Kattach H, Hasan S, Clelland C, Pillai R (2005) Seeding of stage I thymoma into the chest wall 12 years after needle biopsy. Ann Thorac Surg 79:323–324. https://doi.org/10.1016/j.athoracsur.2003.08.004

    Article  PubMed  Google Scholar 

  8. Nagasaka T, Nakashima N, Nunome H (1993) Needle tract implantation of thymoma after transthoracic needle biopsy. J Clin Pathol 46:278–279

    Article  CAS  Google Scholar 

  9. Sollini M, Antunovic L, Chiti A, Kirienko M (2019) Towards clinical application of image mining: a systematic review on artificial intelligence and radiomics. Eur J Nucl Med Mol Imaging 46(13):2656–2672. https://doi.org/10.1007/s00259-019-04372-x

    Article  PubMed  PubMed Central  Google Scholar 

  10. Yasaka K, Akai H, Abe O et al (2018) Quantitative computed tomography texture analyses for anterior mediastinal masses: differentiation between solid masses and cysts. Eur J Radiol 100:85–91. https://doi.org/10.1016/j.ejrad.2018.01.017

    Article  PubMed  Google Scholar 

  11. Yasaka K, Akai H, Nojima M et al (2017) Quantitative computed tomography texture analysis for estimating histological subtypes of thymic epithelial tumors. Eur J Radiol 92:84–92. https://doi.org/10.1016/j.ejrad.2017.04.017

    Article  PubMed  Google Scholar 

  12. Iannarelli A, Sacconi B, Tomei F et al (2018) Analysis of CT features and quantitative texture analysis in patients with thymic tumors: correlation with grading and staging. Radiol Med 123:345–350. https://doi.org/10.1007/s11547-017-0845-4

    Article  PubMed  Google Scholar 

  13. Lee HS, Oh JS, Park YS et al (2016) Differentiating the grades of thymic epithelial tumor malignancy using textural features of intratumoral heterogeneity via (18)F-FDG PET/CT. Ann Nucl Med 30:309–319. https://doi.org/10.1007/s12149-016-1062-2

    Article  CAS  PubMed  Google Scholar 

  14. Nakajo M, Jinguji M, Shinaji T et al (2018) Texture analysis of 18F-FDG PET/CT for grading thymic epithelial tumours: usefulness of combining SUV and texture parameters. Br J Radiol 91:20170546. https://doi.org/10.1259/bjr.20170546

    Article  PubMed  PubMed Central  Google Scholar 

  15. Hatt M, Cheze-le Rest C, van Baardwijk A et al (2011) Impact of tumor size and tracer uptake heterogeneity in (18)F-FDG PET and CT non-small cell lung cancer tumor delineation. J Nucl Med 52:1690–1697. https://doi.org/10.2967/jnumed.111.092767

    Article  PubMed  PubMed Central  Google Scholar 

  16. Nioche C, Orlhac F, Boughdad S et al (2018) Lifex: a freeware for radiomic feature calculation in multimodality imaging to accelerate advances in the characterization of tumor heterogeneity. Cancer Res 78:4786–4789. https://doi.org/10.1158/0008-5472.CAN-18-0125

    Article  CAS  PubMed  Google Scholar 

  17. Sharma A, Paliwal KK (2015) Linear discriminant analysis for the small sample size problem: an overview. Int J Mach Learn Cybern. https://doi.org/10.1007/s13042-013-0226-9

    Article  Google Scholar 

  18. Core Team R (2014) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna

    Google Scholar 

  19. Kirienko M, Cozzi L, Antunovic L et al (2018) Prediction of disease-free survival by the PET/CT radiomic signature in non-small cell lung cancer patients undergoing surgery. Eur J Nucl Med Mol Imaging 45:207–217. https://doi.org/10.1007/s00259-017-3837-7

    Article  PubMed  Google Scholar 

  20. Bae KT (2010) Intravenous contrast medium administration and scan timing at CT: considerations and approaches. Radiology 256:32–61. https://doi.org/10.1148/radiol.10090908

    Article  PubMed  Google Scholar 

  21. He L, Huang Y, Ma Z et al (2016) Effects of contrast-enhancement, reconstruction slice thickness and convolution kernel on the diagnostic performance of radiomics signature in solitary pulmonary nodule. Sci Rep 6:34921. https://doi.org/10.1038/srep34921

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Cozzi L, Comito T, Fogliata A et al (2019) Computed tomography based radiomic signature as predictive of survival and local control after stereotactic body radiation therapy in pancreatic carcinoma. PLoS ONE 14:e0210758. https://doi.org/10.1371/journal.pone.0210758

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Cozzi L, Dinapoli N, Fogliata A et al (2017) Radiomics based analysis to predict local control and survival in hepatocellular carcinoma patients treated with volumetric modulated arc therapy. BMC Cancer 17:829. https://doi.org/10.1186/s12885-017-3847-7

    Article  PubMed  PubMed Central  Google Scholar 

  24. Hernández B, Parnell A, Pennington SR (2014) Why have so few proteomic biomarkers “survived” validation? (sample size and independent validation considerations). Proteomics 14:1587–1592. https://doi.org/10.1002/pmic.201300377

    Article  CAS  PubMed  Google Scholar 

  25. Sollini M, Cozzi L, Ninatti G et al (2020) PET/CT radiomics in breast cancer: mind the step. Methods. https://doi.org/10.1016/j.ymeth.2020.01.007

    Article  PubMed  Google Scholar 

  26. Park JE, Kim D, Kim HS et al (2019) Quality of science and reporting of radiomics in oncologic studies: room for improvement according to radiomics quality score and TRIPOD statement. Eur Radiol. https://doi.org/10.1007/s00330-019-06360-z

    Article  PubMed  PubMed Central  Google Scholar 

  27. Zwanenburg A (2019) Radiomics in nuclear medicine: robustness, reproducibility, standardization, and how to avoid data analysis traps and replication crisis. Eur J Nucl Med Mol Imaging 46(13):2638–2655

    Article  Google Scholar 

  28. Sollini M, Gelardi F, Matassa G et al (2020) Interdisciplinarity: an essential requirement for translation of radiomics research into clinical practice – a systematic review focused on thoracic oncology. Rev Esp Med Nucl Imagen Mol. https://doi.org/10.1016/j.remn.2019.10.003

    Article  PubMed  Google Scholar 

  29. Sollini M, Cozzi L, Antunovic L et al (2017) PET radiomics in NSCLC: state of the art and a proposal for harmonization of methodology. Sci Rep 7:358. https://doi.org/10.1038/s41598-017-00426-y

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

MK PhD scholarship was funded by the AIRC Grant (IG-2016-18585). The scientific guarantor of this publication is Arturo Chiti, who is the Principal Investigator of this retrospective trial.

Funding

The authors state that this work has not received any funding.

Author information

Authors and Affiliations

  1. Department of Biomedical Sciences, Humanitas University, via Rita Levi Montalcini, 4, 20090, Pieve Emanuele, Milano, Italy

    Margarita Kirienko, Gaia Ninatti, Luca Cozzi, Emanuele Voulaz, Isabella Barajon, Carmelo Carlo-Stella, Martina Sollini & Arturo Chiti

  2. Radiotherapy, Humanitas Cancer Center, Humanitas Clinical and Research Center - IRCCS, via Manzoni, 56, 20089, Rozzano, Milano, Italy

    Luca Cozzi

  3. Thoracic Surgery, Humanitas Clinical and Research Center - IRCCS, via Manzoni, 56, 20089, Rozzano, Milano, Italy

    Emanuele Voulaz

  4. Training Program in Radiology, Humanitas University, via Rita Levi Montalcini, 4, 20090, Pieve Emanuele, Milano, Italy

    Nicolò Gennaro

  5. Department of Oncology and Hematology, Humanitas Clinical and Research Center - IRCCS, via Manzoni, 56, 20089, Rozzano, Milano, Italy

    Francesca Ricci, Carmelo Carlo-Stella & Paolo Zucali

  6. Nuclear Medicine, Diagnostic Imaging Department, Humanitas Clinical and Research Center - IRCCS, via Manzoni, 56, 20089, Rozzano, Milano, Italy

    Martina Sollini & Arturo Chiti

  7. Radiology, Diagnostic Imaging Department, Humanitas Clinical and Research Center - IRCCS, via Manzoni, 56, 20089, Rozzano, Milano, Italy

    Luca Balzarini

Authors
  1. Margarita Kirienko
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gaia Ninatti
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Luca Cozzi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Emanuele Voulaz
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Nicolò Gennaro
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Isabella Barajon
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Francesca Ricci
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Carmelo Carlo-Stella
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Paolo Zucali
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Martina Sollini
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Luca Balzarini
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Arturo Chiti
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

MK, MS and AC conceptualized the study; GN participated to data collection and image processing; LC performed data analysis; EV, PZ, CCS, FR participated in patient selection and were in charge of treatment; NG and LB participated to patient selection; MK and MS supervised image processing, critically interpreted the results and drafted the paper; IB, LB and AC supervised the activities; and all the authors read, commented and approved the manuscript.

Corresponding author

Correspondence to Martina Sollini.

Ethics declarations

Conflict of interest

The authors of this manuscript declare relationships with the following companies: Kirienko, Ninatti, Voulaz, Gennaro, Barajon, Ricci, Sollini, Balzarini—none. Cozzi—acts as Scientific Advisor to Varian Medical Systems outside the scope of the submitted work. Carlo Stella—received speaker honoraria from MSD, BMS, Amgen, Janssen, AstraZeneca; acted as scientific advisor for Genenta Science, ADC Therapeutics, Sanofi, Boehringer Ingelheim; benefited from an unrestricted Grant from Rhizen Pharmaceuticals. These honoraria and Grants are outside the scope of the submitted work. Zucali—received speaker honoraria from Astellas, Pfizer, Sanofi, Janssen, BMS, Ipsen, and Novartis; acted as scientific advisor for Astellas, Pfizer, BMS, Janssen, MSD and Novartis. All honoraria and Grants are outside the scope of the submitted work. Chiti—received speaker honoraria from General Electric and Blue Earth Diagnostics, acted as scientific advisor for Blue Earth Diagnostics and Advanced Accelerator Applications, and benefited from an unconditional Grant from Sanofi to Humanitas University. All honoraria and Grants are outside the scope of the submitted work. As above mentioned, all declared honoraria and Grants are outside the scope of the submitted work.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee (Humanitas Clinical and Research Center Review Board approval was obtained, authorization number 3/18, April 17, 2018) and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Informed consent

Written informed consent was waived by the institutional review board.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 30 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kirienko, M., Ninatti, G., Cozzi, L. et al. Computed tomography (CT)-derived radiomic features differentiate prevascular mediastinum masses as thymic neoplasms versus lymphomas. Radiol med 125, 951–960 (2020). https://doi.org/10.1007/s11547-020-01188-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11547-020-01188-w

Keywords

Search

Navigation