## 5 Conclusions  
In this study, the performance of prognostic models built on radiomic and clinical data of DLBCL patients was not affected by the use of either among two PET segmentation methods (fixed threshold equal to 2.5 SUV and percentage threshold equal to 25% of the maximum SUV in the lesion) and two target lesions for radiomic analysis (highest uptake lesion and highest volume lesion)The robustness of the results to variations in image analysis methodology supports the hypothesis that prognostic information is contained in baseline PET images of DLBCL patients, prompting further investigation on a validation dataset. The following supporting information can be downloaded at [[https://www.mdpi.com/article/10.3390/app12199678/s1](https://www.mdpi.com/article/10.3390/app12199678/s1)]([https://www.mdpi.com/article/10.3390/app12199678/s1](https://www.mdpi.com/article/10.3390/app12199678/s1)), Table S1: Baseline characteristics of study population; Table S2: PET acquisition parameters of study population; Table S3: Summary of Progression-Free Survival (PFS) and Overall Survival (OS) times; Table S4: Sensitivity Analysis Case 1 (SUVmax lesion, segmentation Method-1). Univariate and multivariable Hazard Ratio (HR) with 95% Confidence Intervals (CI) for the association of continuous variables with Progression Free Survival (PFS) and Overall Survival (OS); Table S5: Sensitivity Analysis Case 2 (VOLmax lesion, segmentation Method-2). Univariate and multivariable Hazard Ratio (HR) with 95% Confidence Intervals (CI) for the association of continuous variables with Progression Free Survival (PFS) and Overall Survival (OS); Table S6: Sensitivity Analysis Case 3 (VOLmax lesion, segmentation Method-1). Univariate and multivariable Hazard Ratio (HR) with 95% Confidence Intervals (CI) for the association of continuous variables with Progression Free Survival (PFS) and Overall Survival (OS). Figure S1: 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-1. Only coefficients larger than 1.00 are represented; Figure S2: 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 \"VOLmax lesion\" segmented with Method-2. Only coefficients larger than 1.00 are represented; Figure S3: 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 \"VOLmax lesion\" segmented with Method-1. Only coefficients larger than 1.00 are represented; Figure S4: Univariate association (Log-Rank rank test) between dichotomized RS and a: PFS; b: OS. RS were obtained from radiomic features extracted from \"VOLmax lesion\" segmented with Method-2; Figure S6: Univariate association (Log-Rank rank test) between dichotomized RS and a: PFS; b: OS. RS were obtained from radiomic features extracted from \"VOLmax lesion\" segmented with Method-1. [PERSON] was involved in conceptualization, methodology, formal analysis, data curation, investigation, writing--original draft preparation, writing--review and editing, visualization, supervision; [PERSON] was involved in conceptualization, methodology, data curation, visualization, writing--review and editing; [PERSON] was involved in conceptualization, methodology, formal analysis, visualization, software, investigation, writing--review and editing; [PERSON] and F.C.