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Abstract #3111

A semantic-enhanced integrated radiomics model based on spinal MRI for predicting early relapse in multiple myeloma: A multi-center study

Caolin Liu1, Chupeng Ling2,3, Qinmei Yang1, Wei Yang2,3, and Yinghua Zhao1
1Department of Radiology, The Third Affiliated Hospital of Southern Medical University (Academy of Orthopedics, Guangdong Province), GuangZhou, China, 2School of Biomedical Engineering, Southern Medical University, GuangZhou, China, 3Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, GuangZhou, China

Synopsis

Keywords: Bone/Skeletal, Hematology, Oncology

Motivation: Early relapse (ER) in MM leads to poor prognosis, making early identification of high-risk patients essential. Current models are limited by biopsy variability and high gene detection costs. A non-invasive ER prediction tool for MM is urgently needed.

Goal(s): Develop and validate a radiomics model combining MRI, clinical, and semantic features to predict ER in MM.

Approach: We used multicenter retrospective data and 5-fold cross-validation, evaluating performance on an external test set with ROC and decision curves.

Results: The model (T2 + SF + clinic) showed AUC of 0.856, good calibration, and high net benefit. ER patients had shorter PFS (P < 0.001).

Impact: Integrating imaging and semantic features improved model accuracy and interpretability, enabling early risk identification and supporting personalized MM treatment.

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