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Abstract #3116

Differentiating multiple myeloma and bone metastasis on spine MRI using radiomics-based machine learning and deep learning models

Luu-Ngoc Do1, Seung Wan Kang2, Deokho Yun3, and Ilwoo Park1,4,5
1Radiology, Chonnam National Univeristy, Gwangju, Korea, Republic of, 2Chonnam National University Hwasun Hospital, Hwasun, Korea, Republic of, 3Medicine, Chonnam National University, Gwangju, Korea, Republic of, 4Radiology, Chonnam National University Hospital, Gwangju, Korea, Republic of, 5Artificial Intelligence Convergence, Chonnam National University, Gwanju, Korea, Republic of

Synopsis

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.

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Keywords