RIS ID

115288

Publication Details

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 2017 IEEE GlobalSIP Symposium on Signal Processing & Machine Learning in Large Medical Datasets, 2017, pp. 1-5.

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.

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