## 4 Conclusions  
Radiomics involves the extraction of a huge number of quantitative features from medical images to predict patient outcomes and to support clinical decision-making systems. However, the clinical application of radiomics is limited due to uncertainty about the robustness of the extracted features. To analyze this aspect in the context of PET/MRI co-registration, we applied a semi-automatic segmentation based on the thresholding method combined with an automatic PET feature extraction process using Pyradiomics. In this way, the difference in percentage coefficients was evaluated between features extracted from: i) original PET, ii) PET after co-registration with T1-weighted MRI and iii) PET after co-registration with FLAIR MRI. In addition, feature selection using [PERSON]'s correlation method was proposed to reduce the high dimension of extracted features and verify if the selected radiomics features are consistent after co-registration of PET images on MRI ones. The results showed that the shape features, first-order statistical features, NGTDM and GLCM features are robust, as the percentage difference is less than 20%. Conversely, GLSZM, GLRLM and GLDM showed very weak robustness due to co-registration. In addition, approximately 40% of different features were identified in the three image datasets after eliminating all highly correlated features in each dataset. In conclusion, co-registration compromises the robustness of radiomics features; thus, authors need to be careful when radiomics studies are performed on co-registered images. Any results found using PET images are not transferable to the co-registered images. Understanding the robustness of radiomics features after image co-registration can aid future radiomics research to enhance the clinical outcome prediction and improve diagnosis and prognosis of cancer.  
Conceptualization, A.S. and G.R.; Data curation, A.S., S.R. and P.T.; Formal analysis, A.S. and P.T.; Funding acquisition, A.S. and A.C.; Investigation; A.S. and P.T.; Methodology, A.S.; Project administration, A.S.; Resources, S.R., A.C., V.B., S.C., M.G.S., A.T., R.A., F.C., G.M.V.B. and M.I.; Software, P.T.; Supervision, A.S., A.L., and G.R.; Validation, A.S.; Visualization, A.S. and P.T.; Writing--original draft, A.S. and P.T.; Writing--review and editing, A.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**PET** & **PET T1 Co-Registration** \\ \hline shape-MinorAxisLength & shape-Maximum2 DDiameterSlice \\ \hline shape-Sphericity & glcm-ClusterShade \\ \hline firstorder-Skewness & glcm-Imc1 \\ \hline glrlm-LongRunHighGrayLevelEmphasis & glrlm-LongRunLowGrayLevelEmphasis \\ \hline glszm-GrayLevelNonUniformity & glrlm-RunVariance \\ \hline glszm-GrayLevelNonUniformityNormalized & glszm-SizeZoneNonUniformity \\ \hline gldm-LargeDependenceLowGrayLevelEmphasis & \\ \hline gldm-SmallDependenceLowGrayLevelEmphasis & \\ \hline \hline \end{tabular}
\end{table}
Table 3: The different features selected in PET and PET co-registered with T1 datasets.  
**Institutional Review Board Statement:** The proposed research has no implication on patient treatment. Review board approval was not sought: the proposed image analysis was performed offline and thus did not change the current treatment protocol.  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.  
**Data Availability Statement:** Data sharing not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.