Milea Timbergen

141 Third, the current radiomics approach requires manual annotations. While accurate, this process is also time consuming and subject to some observer variability as indicated by our DSC, and thus limits the transition to clinical practice. Automatic segmentation methods, for example deep learning, may help to overcome these limitations 40 . Lastly, the dataset originated from 68 different scanners, which resulted in substantial heterogeneity in the acquisition protocols. The lack of standard imaging parameters can be problematic as these can affect the appearance of the tumour and thus the radiomics performance. However, our method was successfully able to create diagnostic models despite these differences. As these models were trained on a variety of imaging protocols, there is an increased chance that the reported performance can be reproduced in a routine clinical setting when using other MRI scanners. Using a single-scanner with dedicated tumour protocols may improve the model performance, but will limit the generalizability. Future work should firstly focus on the prospective validation of our findings. Although we did use a multicentre imaging dataset and performed a rigorous cross-validation experiment strictly separating training from testing data, we did not validate our model on an independent, external dataset. Afterwards, the radiomics model could be used to predict clinical outcomes of DTF receiving active surveillance or systemic treatment. Conclusions Our radiomics approach is capable of distinguishing DTF from non-DTF tumours on T1w- MRI, and can potentially aid diagnosis and shorten diagnostic delay. The performance of the model was similar to that of two experienced musculoskeletal radiologists. The model was not able to predict CTNNB1 mutation status of DTF tumours. Further optimization and external validation of the model is needed to incorporate radiomics in clinical practice. 5