## 1 Introduction
The authors further reported that local features, e.g., entropy and heterogeneity, were more robust when compared to regional features, e.g., intensity variability and size-zone variability. To the best of our knowledge, no studies have analyzed the robustness of PET radiomics features in a real patients' dataset after the co-registration with MRI. We hypothesize that image co-registration can change the voxel intensity relationships between neighboring voxels which in turn changes the feature values. Furthermore, thevolume shape is likely to differ from the original one, changing the shape based feature values, such as the sphericity, compactness, convexity, etc. [17]. In practice, we expected that the image co-registration would introduce further uncertainty to radiomics studies. Specifically, we consider PET images, and we assess the variation in PET radiomics features after the co-registration with T1-weighted MRI, and FLAIR MR images obtained using the same acquisition protocols and the same scanners. MRI is generally used for standard clinical care of patients with brain tumors (i.e., diagnoses, monitor tumor progression, and treatment response assessment) but the clinical role of PET in the management of these patients has evolved considerably in recent years. Consequently, MRI and PET are applied to diagnose and classify brain tumors before surgery, to plan and manage intraoperative phase, to monitor and evaluate response to treatment, and to understand the effects of treatment on the patient's brain. A recent radiomics study [18] suggests that [18F]-Fluorodeoxiglucose (FDG) PET-based radiomics is a reliable non-invasive method to distinguish lymphoma and glioblastoma. Specifically, thirteen features were selected for the differential diagnosis of lymphoma and glioblastoma. The same research group [19] affirms that the radiomics signature based on FDG-PET is a promising method for the non-invasive measurement of glioma proliferative activity and facilitates the prediction of patient prognoses. Nevertheless, although FDG is considered the best oncology radio-tracer in PET, it shows a high-glucose metabolism in normal brain tissue, which hinders the identification of a low- or intermediate-grade tumor with similar or less activity. For this reason, an alternative radio-tracer, [11C]- Methionine (MET), is studied since it provides a high detection rate of focal lesions in the central nervous system [20]. Particularly, [11C]-MET reflects amino acid transport in tumor which demonstrates a higher efficiency compared to [18F]-FDG in delineating the tumor extent, especially in the low-grade gliomas. The uptake of amino acid in a normal brain is relatively low as compared to those with gliomas since cancers need to consume more methionine for extensive proliferation and survival, while normal cells do not [21]. For this reason, it is important to incorporate MET-PET imaging in addition to MRI to provide specific information for defining the target volume for the radio-surgical treatment in patients with recurrent brain tumors to optimize target identification for infiltrating or ill-defined brain lesions. Considering MET-PET radiomics studies, [PERSON], et al. [5] were able to select a sub-panel of three features (namely asphericity, low-intensity run emphasis, and complexity) with valuable association with patient outcome (sensitivity, 81.23%; specificity, 73.97%; accuracy, 78.27%). [PERSON] et al. [22] developed a radiomics model to differentiate recurrent brain tumor from radiation necrosis based on MET-PET in a mixed cohort of 41 patients with brain metastasis or glioma. A random forest classifier was trained to separate radiation necrosis from recurrent brain tumor. The implemented radiomics model obtained an area under the receiver operating characteristic curve (AUC) of 0.98 (specificity, 94%; sensitivity, 90%). [PERSON] at al. [23] considered machine-learning-driven survival models for glioma built on in vivo MET-PET characteristics, ex vivo characteristics, and patient characteristics with an AUC of 0.9.