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A comparative study of machine learning techniques for the improved prediction of NSCLC survival analysis

Conference Paper


Abstract


  • This paper aims to characterise the 2-year survival of non-small cell lung cancer patients. It involves a novel approach that explores the rind around the tumour volume that is delineated by an oncologist as the ground truth. This study also compares various machine learning techniques to determine the ideal method for predicting cancer survival. This paper found improved prediction results at 6 pixels outside the tumour volume, a distance of approximately 5mm outside the original GTV, when applying a support vector machine achieving an accuracy of 71.18%. This paper challenges the traditional clinical ideas of radiotherapy where the centre of the tumour is treated with the highest dose, however this research indicates the periphery of the tumour is highly predictive of survival.

Publication Date


  • 2018

Citation


  • A. Vial, D. Stirling, M. Field, M. Ros, C. Ritz, M. Carolan, L. Holloway & A. A. Miller, "A comparative study of machine learning techniques for the improved prediction of NSCLC survival analysis," in 2018 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2018 - Proceedings, 2018, pp. 1-2.

Scopus Eid


  • 2-s2.0-85073115034

Start Page


  • 1

End Page


  • 2

Place Of Publication


  • United States

Abstract


  • This paper aims to characterise the 2-year survival of non-small cell lung cancer patients. It involves a novel approach that explores the rind around the tumour volume that is delineated by an oncologist as the ground truth. This study also compares various machine learning techniques to determine the ideal method for predicting cancer survival. This paper found improved prediction results at 6 pixels outside the tumour volume, a distance of approximately 5mm outside the original GTV, when applying a support vector machine achieving an accuracy of 71.18%. This paper challenges the traditional clinical ideas of radiotherapy where the centre of the tumour is treated with the highest dose, however this research indicates the periphery of the tumour is highly predictive of survival.

Publication Date


  • 2018

Citation


  • A. Vial, D. Stirling, M. Field, M. Ros, C. Ritz, M. Carolan, L. Holloway & A. A. Miller, "A comparative study of machine learning techniques for the improved prediction of NSCLC survival analysis," in 2018 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2018 - Proceedings, 2018, pp. 1-2.

Scopus Eid


  • 2-s2.0-85073115034

Start Page


  • 1

End Page


  • 2

Place Of Publication


  • United States