Keywords: Bone/Skeletal, Radiomics
Motivation: Spinal bone metastasis and multiple myeloma share similarities in MRI patterns and clinical symptoms, making their differentiation challenging.
Goal(s): To evaluate the feasibility of radiomics-based machine learning and deep learning models in distinguishing between multiple myeloma and metastasis on T2-weighted MRI.
Approach: Radiomics-based random forest and two deep learning architectures that utilized the recurrent residual convolutional neural network and the spatial attention mechanism were developed for the prediction models.
Results: The proposed models demonstrated the potential of differentiating between two challenging conditions, with an accuracy of 75.0% and an AUC of 0.804.
Impact: The results from this study suggest that the proposed approach may provide a valuable assisting tool in distinguishing two challenging conditions, MM and metastasis on spine MRI.
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Keywords