## 3 Results
### Main Analysis: Radiomic and Clinical-Radiomic Predictive Models  
Among 53 RFs extracted from the \"\(\rm SUV_{max}\) lesion\" applying Method-2, only one feature (GLCM_Correlation calculated with two voxels offset) was non-reproducible at ANOVA due to significant dependence on scanner type, and it was excluded from the subsequent analysis. The remaining 52 RFs were used as input for the elastic-net Cox regression model, to create the radiomic score. Figure 1 reports the most relevant RFs selected by the model, together with the corresponding average model coefficients. Only RFs with coefficients larger than 1.00 are represented.  
RFs significantly associated with PFS and OS were: NGLDM_Coarseness, GLZLM_LGZE, GLZLM_SZLGE, GLRLM_SRLGE, GLCM_1_Energy, GLRLM_LGRE, GLZLM_SZE, Sphericity, and HISTO_Uniformity. The radiomic scores were significantly associated with both PFS and OS in the univariate log-rank test, with \(p<0.0001\) and \(p<0.001\), respectively (Figure 2).  
In addition, the radiomic scores were significantly associated with PFS and OS at Cox univariate and multivariate models (\(p<0.001\)). In the multivariate model, the radiomic score retained significance also considering clinical (IPI score) and standard PET parameters (SUV derivates and metabolic volumes) as independent predictors (Table 3).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & & & \multicolumn{4}{c}{\(p\)-Value} \\ \cline{3-8}  & & & **Method-1** & **Method-2** & **Method-3** & **Method-4** & **Method-5** & **Method-6** \\ \hline \multirow{4}{*}{**PFS**} & \multirow{2}{*}{**Univariate**} & TMTV & 0.01 & 0.01 & 0.06 & 0.04 & 0.05 & 0.08 \\ \cline{3-8}  & & WTLG & 0.39 & 0.49 & 0.65 & 0.47 & 0.57 & 0.67 \\ \cline{2-8}  & & TMTV & 0.11 & 0.13 & 0.27 & 0.25 & 0.24 & 0.30 \\  & & IPI score & 0.13 & 0.08 & 0.05 & 0.08 & 0.08 & 0.05 \\ \cline{2-8}  & & WTLG & 0.92 & 0.98 & 0.90 & 0.98 & 0.94 & 0.88 \\  & & IPI score & 0.03 & 0.02 & 0.02 & 0.02 & 0.02 & 0.02 \\ \hline \multirow{4}{*}{**OS**} & \multirow{2}{*}{**Univariate**} & TMTV & 0.91 & 0.51 & 0.57 & 0.56 & 0.87 & 0.75 \\ \cline{2-8}  & & WTLG & 0.59 & 0.78 & 0.75 & 0.48 & 0.67 & 0.71 \\ \cline{2-8}  & & TMTV & 0.70 & 0.59 & 0.69 & 0.35 & 0.95 & 0.86 \\ \cline{2-8}  & & IPI score & 0.30 & 0.41 & 0.39 & 0.24 & 0.34 & 0.35 \\ \cline{2-8}  & & WTLG & 0.41 & 0.64 & 0.60 & 0.30 & 0.52 & 0.57 \\ \cline{2-8}  & & IPI score & 0.25 & 0.30 & 0.30 & 0.23 & 0.28 & 0.29 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Univariate and bivariate Cox-regression models for the association of TMTV and WTLG with progression-free survival (PFS) and overall survival (OS), for the different segmentation methods.  
Figure 1: Elastic net Cox model average coefficients (among 500 repetitions of the model based on a bootstrap resampling) for prediction of (**a**): PFS and (**b**): OS according to RFs extracted from “SUVmax lesion” segmented with Method-2. Only coefficients larger than 1.00 are represented.  
Figure 2: Univariate association (log-rank rank test) between dichotomized RS and (**a**): PFS; (**b**): OS; RS were obtained from radiomic features extracted from “SUVmax lesion” segmented with Method-2.