## 4 Discussion

Table 3: 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). As a preliminary step, we took into account the fact that among different factors possibly affecting the robustness of radiomic studies, PET acquisition and reconstruction settings might alter the image texture and, consequently, the value and significance of the RFs [20]. To avoid the most relevant confounding factors and improve the robustness of our analysis, in this study, we included only images acquired at our Institute with comparable settings (3D modality, 256 \(\times\) 256 matrix). This choice was performed since suitable data to test the generalizability of our findings to different datasets, after proper application of harmonization procedures [21], were not available at the time of this work. Such data might be collected for a future validation study. Even with the selection of images acquired only at our Institute, some imaging and clinical parameters potentially affecting image quality and texture unavoidably varied among patients (scanner model, frame duration, injected activity/body weight, time between injection and acquisition, glucose level). Such parameters were specifically investigated for their potential impact on radiomic features as the first step of our analysis. However, only one RF (GLCM_Correlation) was identified as not reproducible in the main analysis. This high RFs reproductivity is probably related to the standardization of the diagnostic procedure in our center, including standardized injection and acquisition protocols, PET/CT scanner manufactured by the same vendor, and similar reconstruction algorithms. We then investigated the role of the segmentation method used to encompass the lesion since it is also crucial for radiomic analysis reproducibility. In our study, the application of a threshold equal to 25% of the SUV\({}_{\text{max}}\) in each ROI (Method-2) was selected as the optimal segmentation method, as also already reported in the literature [22]. However, consensus on the optimal methodology has not been reached yet, with fixed SUV threshold methods appearing promising when searching for agreement with experienced observers [23; 24]. Additional methodological studies designed to compare the performance of different methods are still ongoing [25]. In accordance, the second method we tested was a fixed threshold method (2.5 SUV, Method-1). The selection of one representative lesion for the radiomic analysis might also introduce an element of arbitrariness in a radiomic study, especially when patients have multiple localizations, as generally happens in lymphoma. Previous studies investigated the potential role of whole-body SUV\({}_{\text{max}}\)[26] or RFs extracted from the highest uptake lesions [14] as prognostic biomarkers. Hence, we selected the lesions exhibiting the highest uptake lesion (SUV\({}_{\text{max}}\) lesion) as the target lesion for our main radiomic analysis. Nonetheless, other authors identified the largest lesion as potentially representative of clinical endpoints [12], therefore, we replicated our analysis by extracting RFs from the largest lesion as well (VOL\({}_{\text{max}}\) lesion investigated in the sensitivity analysis). Further analysis might include the radiomic analysis of multiple lesions for each patient, attempting to capture the intra-patient heterogeneity and characterize the whole tumor burden. The main analysis of this study highlighted the potential role of the RFs as prognostic biomarkers (Figure 2), and the sensitivity analysis confirmed that such a result was retained with the use of a different segmentation method or a different selection for target lesion (Supplementary Figure S4-S6).