Scholarlink

Objective <p>To develop and validate a clinical-radiomics model based on multiparametric MRI for differentiating solitary primary spinal tumors from solitary spinal metastases.</p> Methods <p>This dual-center retrospective study included 510 patients with pathologically confirmed spinal tumors, randomly split into training (<i>n</i> = 328), internal validation (<i>n</i> = 82), and external test (<i>n</i> = 100) cohorts. Radiomics and deep learning features were extracted from T1-weighted, T2-weighted, T2-weighted fat-suppressed, and T1-weighted contrast-enhanced sequences. Three models were constructed and compared: a radiomics model (Rad-M), a deep learning-radiomics fusion model (DRad-M), and a clinical-radiomics model (CRad-M) that integrated radiomics features with patient age. The performance of seven machine learning classifiers was evaluated for each model.</p> Results <p>The CRad-M, utilizing a logistic regression (LR) classifier, demonstrated superior performance, achieving areas under the curve (AUCs) of 0.909 and 0.824 on the internal and external test sets, respectively. It significantly outperformed both the Rad-M and DRad-M models (all <i>p</i> &lt; 0.05). The incorporation of deep learning features did not yield a significant improvement over the radiomics-only model. Calibration and decision curve analyses confirmed the robust clinical utility of the CRad-M.</p> Conclusion <p>The proposed LR-based CRad-M is an effective non-invasive tool for the preoperative differentiation of solitary primary spinal tumors and solitary spinal metastases, with its performance enhanced by the integration of clinical data (age) alongside radiomic features.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A clinical–radiomics model based on multiparametric MRI for discriminating solitary primary spinal tumors from solitary spinal metastases

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月25日

查看全文
  • Chenxi Wang,
  • Jun Xu,
  • Meng Zhang,
  • Dapeng Hao,
  • Ning Lang

摘要

Objective

To develop and validate a clinical-radiomics model based on multiparametric MRI for differentiating solitary primary spinal tumors from solitary spinal metastases.

Methods

This dual-center retrospective study included 510 patients with pathologically confirmed spinal tumors, randomly split into training (n = 328), internal validation (n = 82), and external test (n = 100) cohorts. Radiomics and deep learning features were extracted from T1-weighted, T2-weighted, T2-weighted fat-suppressed, and T1-weighted contrast-enhanced sequences. Three models were constructed and compared: a radiomics model (Rad-M), a deep learning-radiomics fusion model (DRad-M), and a clinical-radiomics model (CRad-M) that integrated radiomics features with patient age. The performance of seven machine learning classifiers was evaluated for each model.

Results

The CRad-M, utilizing a logistic regression (LR) classifier, demonstrated superior performance, achieving areas under the curve (AUCs) of 0.909 and 0.824 on the internal and external test sets, respectively. It significantly outperformed both the Rad-M and DRad-M models (all p < 0.05). The incorporation of deep learning features did not yield a significant improvement over the radiomics-only model. Calibration and decision curve analyses confirmed the robust clinical utility of the CRad-M.

Conclusion

The proposed LR-based CRad-M is an effective non-invasive tool for the preoperative differentiation of solitary primary spinal tumors and solitary spinal metastases, with its performance enhanced by the integration of clinical data (age) alongside radiomic features.