A systematic review reporting quality of radiomics research in the diagnosis and prognosis of soft-tissue sarcoma

Radiomics Quality Score 2.0 (Adopted from https://www.radiomics.world/rqs2)


Soft tissue tumors (STT) are a rare and complex group of tumors with a broad range of differentiation. All STT subtypes significantly differ in their clinical behavior, aggressiveness, molecular background, and preferred treatments given. In order to guide personalized medicine, identifying biomarkers for patient outcomes is essential. The use of features in radiology imaging (radiomics) such as CT and MRI can be used to identify such biomarkers. These biomarkers have advantages as they can be retrieved from standard clinical practice, are non-invasive, and can be easily repeated to follow the patient in time.

In order to assess the quality of radiomics studies, Lambin et. al. (2017), have developed a quality measurement that evaluates radiomics studies methodology, of which a second version is almost ready. Currently, many applications for radiomics in the diagnosis and prognosis of STT have been devised. However, at this time, an overview and assessment of the radiomics studies on STT has yet to be conducted.

As we are also developing radiomics methods for STT, it is of high priority to create an in-depth overview of the clinical applications radiomics has been used for in STT. This can guide the field towards relevant clinical questions. Additionally, it is an opportunity to show the field what has already been achieved in recent years, and where the focus should be on.


The aim of this research is to qualitatively and quantitatively score radiomics studies in soft-tissue sarcoma. Additionally, we would like to create an overview of all the clinical applications for which radiomics has been applied in STT and discuss possible clinical endpoints which should be investigated. Finally, for you, this will be an opportunity to get a (Co-)authorship of an impactful review for radiomics in soft-tissue sarcoma

Related research:


  • Douwe Spaanderman (PhD Student)
  • Martijn Starmans
  • Stefan Klein

Feel free to mail me if you are interested in this project or want more information!

Martijn P. A. Starmans
Martijn P. A. Starmans
Assistant Professor Artificial Intelligence for Integrated Diagnostics (AIID) focused on Medical Imaging in Oncology

My research interests include radiomics, pathomics, meta-learning, and AutoML.