Milea Timbergen

139 Discussion This study showed that radiomics based on T1w-MRI can distinguish DTF from STS. Adding T2w or T1w post-contrast MRI did not substantially improve the model. The DTF CTNNB1 mutation status could not be predicted through radiomics. To our knowledge, this is the first study to evaluate the DTF differential diagnosis and mutation status through an automated radiomics approach. Age and sex appeared to be strong predictors for the diagnosis of DTF, performing better than T1w-MRI. The combination of imaging, age and sex did not improve the model. This implies that age and sex are sufficient for distinguishing DTF from STS. In line with previous nationwide DTF cohort studies, females represented the majority of our cohort, with a lower median age compared to the median age of the patients from the non-DTF group 2, 32 . The relation in our database may however be too strong, and thereby not representative of clinical practice. For example, above 63 years of age, our database included 60 non-DTF and only a single DTF. While the peak incidence of DTF is between 20 to 40 years, DTF can affect patients of all ages with reported ranges from 2 to 90 years 32 . Simply classifying all tumours in patients above 63-years as non-DTF, regardless of any tumour (imaging) information, is unfeasible. Such a model cannot be applied in the general population, while the model purely based on T1w-MRI imaging, as it does not use any population-based information. Our cohort might be biased due to the focus on MRI and the extremity as a site, while other modalities (e.g., CT or ultrasound) may be used for certain tumour sites or for certain types of patients. Further research should include the expansion of our dataset to make especially the age distribution more representative. To estimate the clinical value of our model, we compared the performance with the assessment of two radiologists. The model based on imaging performed similar to the radiologists. The model combining age, sex and imaging features, using the same dataset as the radiologist, had a higher AUC than the musculoskeletal radiologists. However this model may suffer from the selection bias as mentioned in the previous section. The agreement between the radiologists was intermediate, indicating observer dependence in the prediction. The radiomics model is observer independent, assuming the segmentation is reproducible as indicated by the high DSC and ICCs, and will always give the same prediction on the same image. The DTF differential diagnosis is highly important for treatment decisions, but difficult on imaging due to its rarity, while using invasive biopsies brings risks such as tumour 5