Skip to main content
placeholder image

Assessing the prognostic impact of 3D CT image tumour rind texture features on lung cancer survival

Conference Paper


Download full-text (Open Access)

Abstract


  • In this paper we examine a technique for developing prognostic

    image characteristics, termed radiomics, for non-small cell

    lung cancer based on a tumour edge region-based analysis.

    Texture features were extracted from the rind of the tumour in

    a publicly available 3D CT data set to predict two-year survival.

    The derived models were compared against the previous

    methods of training radiomic signatures that are descriptive

    of the whole tumour volume. Radiomic features derived solely

    from regions external, but neighbouring, the tumour were

    shown to also have prognostic value. By using additional texture

    features an increase in accuracy, of 3%, is shown over

    previous approaches for predicting two-year survival, upon

    examining the outside rind including the volume compared to

    the volume without the rind. This indicates that while the centre

    of the tumour is currently the main clinical target for radiotherapy

    treatment, the tissue immediately around the tumour

    is also clinically important.

Publication Date


  • 2017

Citation


  • A. Vial, D. Stirling, M. Field, M. Ros, C. Ritz, M. Carolan, L. Holloway & A. A. Miller, "Assessing the prognostic impact of 3D CT image tumour rind texture features on lung cancer survival," in IEEE Global Conference on Signal and Information Processing (GlobalSIP 2017), 2017, pp. 735-739.

Scopus Eid


  • 2-s2.0-85048055812

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=1859&context=eispapers1

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers1/857

Start Page


  • 735

End Page


  • 739

Place Of Publication


  • New York, United States

Abstract


  • In this paper we examine a technique for developing prognostic

    image characteristics, termed radiomics, for non-small cell

    lung cancer based on a tumour edge region-based analysis.

    Texture features were extracted from the rind of the tumour in

    a publicly available 3D CT data set to predict two-year survival.

    The derived models were compared against the previous

    methods of training radiomic signatures that are descriptive

    of the whole tumour volume. Radiomic features derived solely

    from regions external, but neighbouring, the tumour were

    shown to also have prognostic value. By using additional texture

    features an increase in accuracy, of 3%, is shown over

    previous approaches for predicting two-year survival, upon

    examining the outside rind including the volume compared to

    the volume without the rind. This indicates that while the centre

    of the tumour is currently the main clinical target for radiotherapy

    treatment, the tissue immediately around the tumour

    is also clinically important.

Publication Date


  • 2017

Citation


  • A. Vial, D. Stirling, M. Field, M. Ros, C. Ritz, M. Carolan, L. Holloway & A. A. Miller, "Assessing the prognostic impact of 3D CT image tumour rind texture features on lung cancer survival," in IEEE Global Conference on Signal and Information Processing (GlobalSIP 2017), 2017, pp. 735-739.

Scopus Eid


  • 2-s2.0-85048055812

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=1859&context=eispapers1

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers1/857

Start Page


  • 735

End Page


  • 739

Place Of Publication


  • New York, United States