Liu Ke, Ning Jinlai, Qin Siyuan, Xu Jun, Hao Dapeng, Lang Ning
Department of Radiology, Peking University Third Hospital, Beijing, China.
Department of Informatics, King's College London, London, UK.
J Magn Reson Imaging. 2025 Jul;62(1):176-186. doi: 10.1002/jmri.29720. Epub 2025 Jan 27.
The spinal column is a frequent site for metastases, affecting over 30% of solid tumor patients. Identifying the primary tumor is essential for guiding clinical decisions but often requires resource-intensive diagnostics.
To develop and validate artificial intelligence (AI) models using noncontrast MRI to identify primary sites of spinal metastases, aiming to enhance diagnostic efficiency.
Retrospective.
A total of 514 patients with pathologically confirmed spinal metastases (mean age, 59.3 ± 11.2 years; 294 males) were included, split into a development set (360) and a test set (154).
FIELD STRENGTH/SEQUENCE: Noncontrast sagittal MRI sequences (T1-weighted, T2-weighted, and fat-suppressed T2) were acquired using 1.5 T and 3 T scanners.
Two models were evaluated for identifying primary sites of spinal metastases: the expert-derived features (EDF) model using radiologist-identified imaging features and a ResNet50-based deep learning (DL) model trained on noncontrast MRI. Performance was assessed using accuracy, precision, recall, F1 score, and the area under the receiver operating characteristic curve (ROC-AUC) for top-1, top-2, and top-3 indicators.
Statistical analyses included Shapiro-Wilk, t tests, Mann-Whitney U test, and chi-squared tests. ROC-AUCs were compared via DeLong tests, with 95% confidence intervals from 1000 bootstrap replications and significance at P < 0.05.
The EDF model outperformed the DL model in top-3 accuracy (0.88 vs. 0.69) and AUC (0.80 vs. 0.71). Subgroup analysis showed superior EDF performance for common sites like lung and kidney (e.g., kidney F1: 0.94 vs. 0.76), while the DL model had higher recall for rare sites like thyroid (0.80 vs. 0.20). SHapley Additive exPlanations (SHAP) analysis identified sex (SHAP: -0.57 to 0.68), age (-0.48 to 0.98), T1WI signal intensity (-0.29 to 0.72), and pathological fractures (-0.76 to 0.25) as key features.
AI techniques using noncontrast MRI improve diagnostic efficiency for spinal metastases. The EDF model outperformed the DL model, showing greater clinical potential.
Spinal metastases, or cancer spreading to the spine, are common in patients with advanced cancer, often requiring extensive tests to determine the original tumor site. Our study explored whether artificial intelligence could make this process faster and more accurate using noncontrast MRI scans. We tested two methods: one based on radiologists' expertise in identifying imaging features and another using a deep learning model trained to analyze MRI images. The expert-based method was more reliable, correctly identifying the tumor site in 88% of cases when considering the top three likely diagnoses. This approach may help doctors reduce diagnostic time and improve patient care.
3 TECHNICAL EFFICACY: Stage 2.
脊柱是转移瘤的常见部位，超过30%的实体瘤患者会受到影响。确定原发肿瘤对于指导临床决策至关重要，但通常需要耗费大量资源的诊断方法。
开发并验证使用非增强磁共振成像（MRI）识别脊柱转移瘤原发部位的人工智能（AI）模型，旨在提高诊断效率。
回顾性研究。
共纳入514例经病理证实的脊柱转移瘤患者（平均年龄59.3±11.2岁；男性294例），分为训练集（360例）和测试集（154例）。
场强/序列：使用1.5T和3T扫描仪获取非增强矢状面MRI序列（T1加权、T2加权和脂肪抑制T2）。
评估了两种用于识别脊柱转移瘤原发部位的模型：使用放射科医生识别的影像特征的专家衍生特征（EDF）模型和基于非增强MRI训练的基于ResNet50的深度学习（DL）模型。使用准确率、精确率、召回率、F1分数以及前1、前2和前3指标的受试者操作特征曲线下面积（ROC-AUC）评估性能。
统计分析包括Shapiro-Wilk检验、t检验、Mann-Whitney U检验和卡方检验。通过DeLong检验比较ROC-AUC，通过1000次自助重复抽样得到95%置信区间，P<0.05具有统计学意义。
EDF模型在前3准确率（0.88对0.69）和AUC（0.80对0.71）方面优于DL模型。亚组分析显示，EDF模型在肺和肾等常见部位表现更优（如肾F1：0.94对0.76），而DL模型对甲状腺等罕见部位的召回率更高（0.80对0.20）。SHapley加性解释（SHAP）分析确定性别（SHAP：-0.57至0.68）、年龄（-0.48至0.98）、T1WI信号强度（-0.29至0.72）和病理性骨折（-0.76至0.25）为关键特征。
使用非增强MRI的AI技术提高了脊柱转移瘤的诊断效率。EDF模型优于DL模型，显示出更大的临床潜力。
脊柱转移瘤，即癌症扩散至脊柱，在晚期癌症患者中很常见，通常需要进行大量检查以确定原发肿瘤部位。我们的研究探讨了人工智能是否可以使用非增强MRI扫描使这一过程更快、更准确。我们测试了两种方法：一种基于放射科医生识别影像特征的专业知识，另一种使用经过训练以分析MRI图像的深度学习模型。基于专家的方法更可靠，在考虑前三个可能诊断时，88%的病例能正确识别肿瘤部位。这种方法可能有助于医生减少诊断时间并改善患者护理。
3 技术效能：2级
