MRI-based radiomics models show promise in clinical decision-making in spinal metastases patients

The aim of this study was to extract radiomics features from MRI using various machine learning algorithms which would then be integrated with clinical features to build response prediction models for spinal metastases patients who were undergoing stereotactic body radiotherapy (SBRT). The authors found that the MRI-based radiomics models showed valuable predictive capability for treatment outcomes regarding spinal metastases patients undergoing SBRT, which could have the potential to contribute to clinical decision-making and prognosis improvement.

Key points:

• Stereotactic body radiotherapy effectively delivers high doses of radiation to treat spinal metastases.
• Accurate prediction of treatment outcomes has crucial clinical significance.
• MRI-based radiomics models demonstrated good performance to predict treatment outcomes.

Article: MRI feature-based radiomics models to predict treatment outcome after stereotactic body radiotherapy for spinal metastases

Authors: Yongye Chen, Siyuan Qin, Weili Zhao, Qizheng Wang, Ke Liu, Peijin Xin, Huishu Yuan, Hongqing Zhuang & Ning Lang