Aims and objectives
We previously identified a radiomic signature capable of predicting disease-free survival (DFS) in NSLC patients undergoing surgery1.
In this study,
we evaluated the same population with a non-parametric,
multivariate analysis using a random forest model,
aimed at predicting DFS from a combination of input variables.
Methods and materials
Random forests for classification were developed keeping the same training and validation sets as for the parametric analysis,
to predict DFS.
Seven different combinations of variables were considered: Clinical (263 patients,
5 features),
CT (295,
41),
PET (258,
43),
PET+CT (258,
84),
CT+Clinical (263,
46),
PET+Clinical (231,
48),
PET+CT+Clinical (231,
89)
Random forests for classification were developed keeping the same training and validation sets as for the parametric analysis.
The outcome to be predicted was the DFS considered until the date of last access or...
Results
The highest AUC obtained on the validation set was 0.79.
The corresponding model was a forest with 10000 trees,
6 as split dimension,
0.25/0.75 as relative weight and on the dataset containing CT and clinical features.
The dataset was the one with only the feature with importance greater than the 80th quantile.
In particular the nine variable se-lected were: ’Contrast_NGLDM’,
‘Correlation_GLCM',
‘EntropyH',
‘LZHGE_GLZLM',
‘SZHGE_GLZLM',
‘Sphericity',
‘maxValue',
‘minValue',
‘stdValue'.
The importance grade of the feature isillustrated in Figure 2.
The graphical representation of patients belonging to...
Conclusion
Innovative statistics analysis are a promising tool to select robust radiomics signatures
References
1.
Kirienko,
M.,
Cozzi,
L.,
Antunovic,
L.
et al.
Eur J Nucl Med Mol Imaging (2018) 45: 207.
https://doi.org/10.1007/s00259-017-3837-7