| 李宇璞,赵鹏飞,张小娟,张昭静,王梓怡,乔鹏飞.MRI影像组学联合ResNet101深度学习鉴别腰椎布鲁氏菌性脊柱炎与脊柱转移癌[J].中国医学影像技术,2025,41(6):958~962 |
| MRI影像组学联合ResNet101深度学习鉴别腰椎布鲁氏菌性脊柱炎与脊柱转移癌 |
| MRI radiomics combined with ResNet101 deep learning for differentiating lumbar spine brucella spondylitis and spinal metastases |
| 投稿时间：2024-12-20 修订日期：2025-06-12 |
| DOI：10.13929/j.issn.1003-3289.2025.06.023 |
| 中文关键词: 布鲁氏菌病 脊柱炎 脊柱肿瘤 肿瘤转移 深度学习 影像组学 磁共振成像 |
| 英文关键词:brucellosis spondylitis spinal cord neoplasms neoplasm metastasis deep learning radiomics magnetic resonance imaging |
| 基金项目:内蒙古医科大学附属医院院级科研项目(2023NYFYPY007)、内蒙古自治区自然科学基金(2023QN08044)、内蒙古医学科学院公立医院科研联合基金科技项目(2024GLLH0356)。 |
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| 中文摘要: |
| 目的 观察MRI影像组学联合ResNet101深度学习鉴别腰椎布鲁氏菌性脊柱炎(BS)与脊柱转移癌(SM)的价值。方法 回顾性纳入腰椎BS及SM各71例为训练集、腰椎BS及SM各33例为测试集。以单因素及多因素logistic分析筛选临床特征、构建临床模型(Mclinic);于腰椎矢状位T2WI中勾画病灶ROI,提取其影像组学特征,构建影像组学模型(Mradiomics);联合应用ResNet101深度学习与影像组学,提取深度学习影像组学特征,构建深度学习影像组学模型(MDL+R);联合临床特征与深度学习影像组学特征,构建联合模型(Mcombined)。分析上述模型鉴别腰椎BS与SM的效能。结果 训练集、测试集中,BS与SM患者年龄、发热及附件受累占比差异均有统计学意义(P均＜0.05);单因素及多因素logistic分析显示后二者为临床特征(P均＜0.001)。Mclinic鉴别训练集及测试集腰椎BS与SM的曲线下面积(AUC)分别为0.794及0.773;Mradiomics的AUC分别为0.895及0.791,而MDL+R为0.926及0.882、Mcombined为0.967及0.906。Mcombined在训练集的AUC显著大于其他模型(P均＜0.05),在测试集则显著大于Mclinic及Mradiomics(P均＜0.05)。结论 MRI影像组学联合ResNet101深度学习有助于鉴别腰椎BS与SM;联合临床可进一步提高其诊断效能。 |
| 英文摘要: |
| Objective To observe the value of MRI radiomics combined with ResNet101 deep learning for differentiating lumbar spine brucella spondylitis (BS) and spinal metastases (SM). Methods Seventy-one cases of lumbar spine BS and the same amount of lumbar spine SM patients were retrospectively enrolled in training set, while 33 cases of lumbar spine BS and the same amount of lumbar spine SM patients were enrolled in test set. Clinical features were screened with univariate and multivariate logistic analysis, and a clinical model (Mclinic) was constructed. ROI of lesions were drawn on lumbar sagittal T2WI, then radiomics features were extracted to construct a radiomics model (Mradiomics). ResNet101 deep learning was integrated with radiomics, then deep learning radiomics features were extracted to construct deep learning radiomics model (MDL+R). Finally a combined model (Mcombined) was constructed through combining clinical features and deep learning radiomics features. The efficacy of the above models for differentiating BS and SM were analyzed. Results Significant differences of patients' age and proportion of fever and accessory involvement were found between BS and SM patients in training and test sets (all P＜0.05), and univariate and multivariate logistic analysis showed the latter two were clinical features (both P＜0.001). The area under the curve (AUC) of Mclinic for differentiating lumbar spine BS and SM was 0.794 and 0.773 in training set and test set, of Mradiomics was 0.895 and 0.791, of MDL+R was 0.926 and 0.882, while of Mcombined was 0.967 and 0.906, respectively. AUC of Mcombined was the highest in training set (all P＜0.05), while in test set, AUC of Mcombined was significantly higher than that of Mclinic and Mradiomics(both P＜0.05). Conclusion MRI radiomics combined with ResNet101 deep learning was helpful for differentiating lumbar spine BS and SM. Combining with clinical data could improve its diagnostic efficacy. |
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