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Radiation Oncology
Published in: Radiation Oncology 1/2018

Open Access 01-12-2018 | Research

Effect of machine learning methods on predicting NSCLC overall survival time based on Radiomics analysis

Authors: Wenzheng Sun, Mingyan Jiang, Jun Dang, Panchun Chang, Fang-Fang Yin

Published in: Radiation Oncology | Issue 1/2018

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Abstract

Background

To investigate the effect of machine learning methods on predicting the Overall Survival (OS) for non-small cell lung cancer based on radiomics features analysis.

Methods

A total of 339 radiomic features were extracted from the segmented tumor volumes of pretreatment computed tomography (CT) images. These radiomic features quantify the tumor phenotypic characteristics on the medical images using tumor shape and size, the intensity statistics and the textures. The performance of 5 feature selection methods and 8 machine learning methods were investigated for OS prediction. The predicted performance was evaluated with concordance index between predicted and true OS for the non-small cell lung cancer patients. The survival curves were evaluated by the Kaplan-Meier algorithm and compared by the log-rank tests.

Results

The gradient boosting linear models based on Cox’s partial likelihood method using the concordance index feature selection method obtained the best performance (Concordance Index: 0.68, 95% Confidence Interval: 0.62~ 0.74).

Conclusions

The preliminary results demonstrated that certain machine learning and radiomics analysis method could predict OS of non-small cell lung cancer accuracy.
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Metadata
Title
Effect of machine learning methods on predicting NSCLC overall survival time based on Radiomics analysis
Authors
Wenzheng Sun
Mingyan Jiang
Jun Dang
Panchun Chang
Fang-Fang Yin
Publication date
01-12-2018
Publisher
BioMed Central
Published in
Radiation Oncology / Issue 1/2018
Electronic ISSN: 1748-717X
DOI
https://doi.org/10.1186/s13014-018-1140-9

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