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Objectives <p>To develop and validate MRI-based radiomics models for predicting intraoperative massive blood loss (MBL) in patients with spinal metastases.</p> Materials and methods <p>A total of 507 patients diagnosed with spinal metastases were enrolled in this study, who were classified as MBL and non-MBL group, with the 2500&#xa0;ml as the threshold. Radiomic features were extracted from T2WI and CET1 sequences and dimensionality reduction was performed by LASSO regression analysis. Radiomics models were developed using radiomics features, yielding a radiomics signature from the best model. Clinical variables were analyzed to create a clinical model. The combined model incorporated both clinical variables and Rad-signature. The predictive performance was assessed through the area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F1 score. Calibration curves and decision curve analyses (DCA) were generated to evaluate the model’s accuracy and clinical utility. Finally, a nomogram was developed to visualize the optimal model.</p> Results <p>Tumor vascularity, preoperative embolization, tumor location, and surgical segments were independent risk factors for MBL. Among 11 machine learning models evaluated, the AdaBoost model demonstrated optimal predictive performance (AUC = 0.775) and was selected as the radiomics model. The combined radiomics-clinical model achieved the highest discriminative ability (AUC = 0.818; 95% CI: 0.7191–0.9165), outperforming both the radiomics model (AUC = 0.768) and the clinical model (AUC = 0.583). The nomogram showed good calibration and provided the greatest net clinical benefit.</p> Conclusions <p>Radiomics-based prediction models enable preoperative assessment of MBL risk in surgery and support personalized perioperative management strategies.</p>

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MRI-based radiomics models for the preoperative prediction of massive intraoperative blood loss in spinal metastases patients

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月28日

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  • Heng Deng,
  • Jiayang Yan,
  • Jiayi Zhang,
  • Fukai Li,
  • Guangwen Duan,
  • Wenhao Jia,
  • Xiang Wang,
  • Shiyuan Liu

摘要

Objectives

To develop and validate MRI-based radiomics models for predicting intraoperative massive blood loss (MBL) in patients with spinal metastases.

Materials and methods

A total of 507 patients diagnosed with spinal metastases were enrolled in this study, who were classified as MBL and non-MBL group, with the 2500 ml as the threshold. Radiomic features were extracted from T2WI and CET1 sequences and dimensionality reduction was performed by LASSO regression analysis. Radiomics models were developed using radiomics features, yielding a radiomics signature from the best model. Clinical variables were analyzed to create a clinical model. The combined model incorporated both clinical variables and Rad-signature. The predictive performance was assessed through the area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F1 score. Calibration curves and decision curve analyses (DCA) were generated to evaluate the model’s accuracy and clinical utility. Finally, a nomogram was developed to visualize the optimal model.

Results

Tumor vascularity, preoperative embolization, tumor location, and surgical segments were independent risk factors for MBL. Among 11 machine learning models evaluated, the AdaBoost model demonstrated optimal predictive performance (AUC = 0.775) and was selected as the radiomics model. The combined radiomics-clinical model achieved the highest discriminative ability (AUC = 0.818; 95% CI: 0.7191–0.9165), outperforming both the radiomics model (AUC = 0.768) and the clinical model (AUC = 0.583). The nomogram showed good calibration and provided the greatest net clinical benefit.

Conclusions

Radiomics-based prediction models enable preoperative assessment of MBL risk in surgery and support personalized perioperative management strategies.