AI in Radiological Imaging of Soft-Tissue and Bone Tumours: A Systematic Review Evaluating Against CLAIM and FUTURE-AI Guidelines

Douwe J. Spaanderman, Matthew Marzetti, Xinyi Wan, Andrew F. Scarsbrook, Philip Robinson, Edwin H. G. Oei, Jacob J. Visser, Robert Hemke, Kirsten van Langevelde, David F. Hanff, Geert J. L. H. van Leenders, Cornelis Verhoef, Dirk J. Grünhagen, Wiro J. Niessen, Stefan Klein, Martijn P. A. Starmans
April, 2025
Cite DOI URL

Abstract

Soft-tissue and bone tumours (STBT) are rare, diagnostically challenging lesions with variable clinical behaviours and treatment approaches. This systematic review aims to provide an overview of Artificial Intelligence (AI) methods using radiological imaging for diagnosis and prognosis of these tumours, highlighting challenges in clinical translation, and evaluating study alignment with the Checklist for AI in Medical Imaging (CLAIM) and the FUTURE-AI international consensus guidelines for trustworthy and deployable AI to promote the clinical translation of AI methods.

Type
Journal article
Publication
EBioMedicine
SAIReview
Douwe J. Spaanderman
PhD Student, Erasmus MC, Rotterdam, NL

Automatic grading and phenotyping of soft-tissue tumors through machine learning to guide personalized cancer treatment

Matthew Marzetti
PhD Student, Leeds Biomedical Research Centre, UK, and Erasmus MC, Rotterdam, NL

Evaluating the added value of quantitative MRI in automatic grading and phenotyping of soft-tissue tumors using AI

Xinyi Wan
PhD Student, Erasmus MC, Rotterdam, NL

Trustworthy AI for improved diagnosis of bone and soft-tissue lesions on MRI

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

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