## 3 Results
### Primary Outcomes  
#### 3.4.1 PREDI-CO Score  
The prediction of the PREDI-CO score was performed considering the dichotomized values obtained by thresholding the labels according to the cutoff of 1: values \(\leq\)1 (47% of the patients) were labelled as 0 and values > 1 (53% of the patients) were labelled as 1. The considered dataset includes only two patients with a PREDI-CO score \(\geq\)6 proving the strong unbalancing of the classes and highlighting the prominence of non-severe patients.  
The three best features selected by the Fisher criterion are (with type of feature indicated in parenthesis):  
* Patient age (Clinical);
* Median of the distance distribution (Radiomics);
* Interquartile (25-75) of the roundness distribution (Radiomics).  
The three best features selected by the \(\chi^{2}\) criterion are (with type of feature indicated in parenthesis):  
* Patient age (Clinical);
* Interquartile (25-75) of the elongation distribution (Radiomics);
* Maximum of the distance distribution (Radiomics).  
We show in Table 13 the results obtained in terms of global adjusted accuracy and in Table 14 the precision, sensitivity and F\({}_{1}\) score of the same prediction.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Fisher** & \(\chi^{2}\) & **All** \\ \hline Logistic & 0.47 & 0.47 & **0.62** \\ Ridge & **0.52** & **0.52** & 0.61 \\ KNN & **0.52** & **0.52** & 0.51 \\ R. Forest & 0.42 & 0.42 & 0.53 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Global adjusted accuracy of the models to predict the presence of peripheral GGO using different feature selection strategies. From the left: dichotomized score value, Precision obtained using the 3 best features identified by the Fisher test, Precision obtained using the 3 best features identified by the \(\chi^{2}\) test; Sensitivity obtained using the 3 best features identified by the Fisher test, Sensitivity obtained using the 3 best features identified by the \(\chi^{2}\) test; F\({}_{1}\) score obtained using the 3 best features identified by the Fisher test; F\({}_{1}\) score obtained using the 3 best features identified by the \(\chi^{2}\) test. For each column the max-imum value was indicated with bold font.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Peripheral** & **Fisher** & \(\chi^{2}\) & **PPV** & **Fisher** & \(\chi^{2}\) & **TPR** & **Fisher** \\  & **1 = Presence** & **PPV** & **\(\chi^{2}\) & **TPR** & **\(\chi^{2}\) & **TPR** & **F\({}_{1}\)** & \(\chi^{2}\) **F\({}_{1}\)** \\ \hline Logistic & 0 & 0.74 & 0.74 & 0.43 & 0.43 & 0.55 & 0.55 \\  & 1 & 0.21 & 0.21 & 0.50 & 0.50 & 0.30 & 0.30 \\ Ridge & 0 & **0.79** & **0.79** & 0.43 & 0.43 & 0.56 & 0.56 \\  & 1 & 0.24 & 0.24 & 0.61 & 0.61 & 0.35 & 0.35 \\ KNN & 0 & 0.78 & 0.78 & **0.87** & **0.87** & **0.82** & **0.82** \\  & 1 & 0.27 & 0.27 & 0.17 & 0.17 & 0.21 & 0.21 \\ R. Forest & 0 & 0.74 & 0.74 & 0.83 & 0.83 & 0.78 & 0.78 \\  & 1 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Precision, Sensitivity, and F\({}_{1}\) Score for each model and each feature selection strategy to predict the presence of peripheral GGO lesions. From the left: dichotomized score value, Precision obtained using the 3 best features identified by the Fisher test, Precision obtained using the 3 best features identified by the \(\chi^{2}\) test; Sensitivity obtained using the 3 best features identified by the Fisher test, Sensitivity obtained using the 3 best features identified by the \(\chi^{2}\) test; F\({}_{1}\) score obtained using the 3 best features identified by the \(\chi^{2}\) test. For each column the max-imum value was indicated with bold font.