Lung tumor segmentation methods: Impact on the uncertainty of radiomics features for non-small cell lung cancer

Constance A. Owens, Christine B. Peterson, Chad Tang, Eugene J. Koay, Wen Yu, Dennis S. Mackin, Jing Li, Mohammad R. Salehpour, David T. Fuentes, Laurence E. Court, Jinzhong Yang, Yong Fan
2018 PLoS ONE  
Purpose To evaluate the uncertainty of radiomics features from contrast-enhanced breath-hold helical CT scans of non-small cell lung cancer for both manual and semi-automatic segmentation due to intra-observer, inter-observer, and inter-software reliability. OPEN ACCESS Citation: Owens CA, Peterson CB, Tang C, Koay EJ, Yu W, Mackin DS, et al. (2018) Lung tumor segmentation methods: Impact on the uncertainty of radiomics features for non-small cell lung cancer. PLoS ONE 13(10): e0205003.
more » ... doi. ICC value varied greatly for each observer when using GrowCut and the manual segmentation tools. For inter-software reliability, features were not reproducible across the software tools for either manual or semi-automatic segmentation methods. Additionally, no feature category was found to be more reproducible than another feature category. Feature ranges of LSTK contours were smaller than those of manual contours for all features. Conclusion Radiomics features extracted from LSTK contours were highly reliable across and among observers. With semi-automatic segmentation tools, observers without formal clinical training were comparable to physicians in evaluating tumor segmentation. Segmentation uncertainty for radiomics studies PLOS ONE | https://doi.org/10.1371/journal.pone.0205003 October 4, 2018 2 / 22 number P30 CA016672. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. Feature reliability analysis Correlation between ICC and CCC. For all reliability relationships, the results for the Spearman rank correlation coefficients between the CCC and ICC values showed a strong and Fig 6. Spearman correlation coefficient heat map including all initial 83 features. Spearman correlation coefficients were computed for 83 radiomics features. Green, white, and red denote positive, random, and negative correlations, respectively. A large number of features were highly correlated.
doi:10.1371/journal.pone.0205003 fatcat:dsmcopymhbgcdku35jrya3ozrq