Milea Timbergen

140 growth. The use of our T1w-MRI radiomics model may therefore aid early recognition and diagnosis of DTF, thus shortening the diagnostic delay by enabling direct referral to an STS expertise centre. Since all routine MRI protocols include a T1w-MRI, our radiomics method is generalizable, feasible and applicable for use in daily clinical practice. After further model optimization, it may serve as a quick, non-invasive, and low-cost alternative for a biopsy, currently limited to extremities due to the used dataset. Additionally, we investigated the predictive value of sequences other than T1w-MRI. The number of available sequences was however limited due to the multicentre imaging dataset. Although T2w-MRI is often used to correlate DTF signal intensity with prognosis or response to therapy 33-36 , in the current study T2w-MRI added little predictive value to the T1w-MRI, similar to the T1w post-contrast MRI. This may however be attributed to the fact that these sequences were only available for a subset of the patients. Our cohort contained too few patients with PD, DCE, or DWI sequences to be analysed. However, there is little to no indication of the added value of these sequences in DTF 37-39 . The second aim of this study was to predict the DTF CTNNB1 mutation status. Our radiomics model was not able to stratify the CTNNB1 mutation type, which is in line with the absence of literature linking DTF MRI appearance to the CTNNB1 mutation. The current study enclosed several limitations. First, due to the rarity of DTF, the DTF sample size was limited and possibly too small for the mutation stratification model to learn from. This also resulted in little statistical power for the mutation analysis, as shown by the large width of our confidence intervals, and for the comparison with the radiologists in the differential diagnosis. Besides primary tumours, the DTF cohort contained also recurrent tumours. As this number was low, and to our knowledge, there are no indications that recurrent DTF appear different on MRI than primary DTF, the expected influence is small. Within the DTF cohort, the WT group was relatively large and might have been subjected to incorrect allocation, as Sanger Sequencing is not always sensitive enough to detect all mutations 11 . The results of the CTNBB1 mutation status stratification showed a strong bias towards the majority classes, which may be attributed to the class imbalance. Although we exploited commonly used imbalanced learning strategies such as resampling and ensembling. other strategies may improve the performance. Second, only extremity DTFs were included for comparison with STS. This was due to the limited availability of MRI in non-extremity soft tissue tumours. However, this is not representative for the entire DTF population, which also occurs frequently in the abdominal wall and trunk 3 . 5