# An Intelligent Radomic Approach for Lung Cancer Screening
## 2 Materials and Methods
### Nodule Embedding  
To extract radiomics features we used PyRadiomics [21] (version 3.01), an open-source python package for the extraction of radiomic features from medical imaging volumes. PyRadiomics features include shape features, first order features, and textural features (Gray Level Co-occurrence Matrix (GLCM), Gray Level Size Zone (GLSZM), Gray Level Run Length Matrix (GLRLM) and Gray Level Dependency Matrix (GLDM)) describing several aspects of the lesion. In this study, we used GLCM texture features [25] by its proven efficiency for cancer diagnosis in a wide range of medical imaging modalities [26; 27; 28; 29; 30].  
GLCM textural features are probabilistic descriptors computed from a gray level co-occurrence matrix. This matrix encodes textural patterns in a neighbourhood of each pixel based on different contrasts. To do so, intensity gray values are first discretized using the histogram of the original volume intensity. The width of the histogram bins sets the granularity that GLCM features describe, since neighbouring pixels with a difference in gray level below such parameter are filtered. Bin width, namely \(\Delta\), is given by:  
\[\Delta=\frac{max(Pixel\_value)-min(Pixel\_value)}{Nbins} \tag{1}\]  
for \(Pixel\_value\) the intensities of the volume and \(Nbins\) the number of histogram bins.  
In [29; 30], the authors showed the importance of, both, intensity ranges and number of bins. It is reported that a fixed bin count between 30 and 130 bins has good reproducibility and performance. In order to allow for different ranges of intensity in ROIs, while still keeping the texture features informative and comparable inter lesion [26], we first normalize  
Figure 1: Overall workflow of the proposed work.  
the CT Hounsfield Units (HU) to a common intensity range. Hounsfield Units are related to intensity values with the following linear transformation:  
\[HUI=Pixel\_value*Slope+Intercept \tag{2}\]  
for \(Slope\) and \(Intercept\) are acquisition parameters of the CT scan.  
HU were mapped to a common intensity range \([0,MaxIntensity]\) using the following formula:  
\[Pixel\_value =\frac{(HUI-Intercept)/Slope}{\max(HUI)-\min(HUI)}*MaxIntensity \tag{3}\] \[=\frac{(HUI-Intercept)/Slope}{4000}*MaxIntensity \tag{4}\]  
where \(\max(HUI)=2976\) was computed as the maximum of the training set volumes HU and \(\min(HUI)=-1024\) is the HU value for air. The intensity range \(MaxIntensity\) and number of bins, \(Nbins\in[30,130]\), are hyper-parameters that were set using grid-search. The optimal values were \(MaxIntensity=24\) and \(Nbins=128\).  
The GLCM features (extracted on 2D slices) are selected according to their correlation to lesion malignancy. It is expected that the most discriminant features have values significantly different for slices containing malign and benign nodules. Such differences are detected, for each GLCM feature, with a Student \(t\)-test comparing average values for malign and benign slices. In order to account for unbalancing between malignant and benign cases, a k-fold subsampling of malignant slices was performed and max-voting aggregation of significance was used to select the most discriminative features. Sample size was large enough to guarantee normality.  
The concatenation of the GLCM features with a \(p\)-value under 0.01 are selected to be the input to the classification network. Given that the performance of neural networks is not bias in case of correlated features (unlike other classifiers like logistic regression), no further selection to discard correlations is needed. The most statistically significant 19 GLCM textural features that were selected are shown in Table 3.