Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea.
Br J Radiol. 2012 Oct;85(1018):e831-6. doi: 10.1259/bjr/27487871. Epub 2012 May 2.
Differentiating between malignant and benign lesions on the basis of MR images depends on the experience of the radiologist. For non-experts, we aimed to develop a simplified systematic MRI approach that uses depth, size and heterogeneity on T(2) weighted MR images (T(2)WI) to differentiate between malignant and benign lesions, and evaluated its diagnostic accuracy.
MR images of 266 patients with histologically proven soft-tissue tumours of the extremities (102 malignant, 164 benign) were analysed according to depth (superficial or deep), size (<50, ≥50 mm) and signal intensity (homogeneous or heterogeneous) on T(2)WI, to determine the ability of each to predict benign and malignant tumours. These three parameters were categorised into systematic combinations of different orders of application, and each combination was assessed for its ability to differentiate between benign and malignant lesions.
Univariate analysis showed that depth, size and heterogeneity on T(2)WI differed significantly between benign and malignant masses (p<0.0001 each). Multiple logistic regression analysis, however, showed that depth was not helpful in distinguishing benign from malignant lesions. The systematic combination of signal intensity, size and depth, in that order, was superior to other combinations, resulting in higher diagnostic values for malignancy, with a sensitivity of 64%, a specificity of 85%, a positive predictive value of 32%, a negative predictive value of 59% and an accuracy of 77%.
A simplified systematic imaging approach, in the order signal intensity, size and depth, would be a reference to distinguish between benign and malignant soft-tissue tumours for non-experts.
基于磁共振（MR）图像对良恶性病变进行区分主要依赖于放射科医生的经验。对于非专业人士，我们旨在开发一种简化的系统 MRI 方法，该方法使用 T2 加权磁共振图像（T2WI）上的深度、大小和异质性来区分良恶性病变，并评估其诊断准确性。
对 266 例经组织学证实的四肢软组织肿瘤患者（102 例恶性，164 例良性）的 MR 图像进行分析，根据 T2WI 上的深度（表浅或深部）、大小（<50mm 或≥50mm）和信号强度（均匀或不均匀）来判断每个参数预测良恶性肿瘤的能力。将这三个参数分为不同应用顺序的系统组合，并评估每个组合区分良恶性病变的能力。
单因素分析显示，T2WI 上的深度、大小和异质性在良性和恶性肿块之间存在显著差异（p<0.0001）。然而，多变量逻辑回归分析显示，深度无助于区分良性和恶性病变。信号强度、大小和深度的系统组合，按此顺序，优于其他组合，对恶性病变具有更高的诊断价值，其敏感性为 64%，特异性为 85%，阳性预测值为 32%，阴性预测值为 59%，准确性为 77%。
对于非专业人士，按信号强度、大小和深度的顺序进行简化的系统成像方法将有助于区分良恶性软组织肿瘤。
