Takigawa Tomoyuki, Tanaka Masato, Sugimoto Yoshihisa, Tetsunaga Tomoko, Nishida Keiichiro, Ozaki Toshifumi
Department of Orthopaedic Surgery, Okayama University Hospital, Okayama, Japan.
Department of Human Morphology, Okayama University Graduate School of Medicine, Density and Pharmaceutical Sciences, Okayama, Japan.
Asian Spine J. 2017 Jun;11(3):478-483. doi: 10.4184/asj.2017.11.3.478. Epub 2017 Jun 15.
Retrospective analysis using magnetic resonance imaging (MRI).
To identify MRI features that could discriminate benign from malignant vertebral fractures.
Discrimination between benign and malignant vertebral fractures remains challenging, particularly in patients with osteoporosis and cancer. Presently, the most sensitive means of detecting and assessing fracture etiology is MRI. However, published reports have focused on only one or a few discriminators.
Totally, 106 patients were assessed by MRI within six weeks of sustaining 114 thoracic and/or lumbar vertebral fractures (benign, n=65; malignant, n=49). The fractures were pathologically confirmed if malignant or clinically diagnosed if benign and were followed up for a minimum of six months. Seventeen features were analyzed in all fractures' magnetic resonance images. Single parameters were analyzed using the chi-square test; a logit model was established using multivariate logistic regression analysis.
The chi-square test revealed 11 malignant and 4 benign parameters. Multivariate logistic regression analysis selected (i) posterior wall diffuse protrusion (odds ratio [OR], 48; 95% confidence interval [CI], 4.2-548; =0.002), (ii) pedicle involvement (OR, 21; 95% CI, 2.0-229; =0.01), (iii) posterior involvement (OR, 21; 95% CI, 1.5-21; =0.02), and (iv) band pattern (OR, 0.047; 95% CI, 0.0005-4.7; =0.19). The logit model was expressed as P=1/[1+exp (x)], x=-3.88×(i)-3.05×(ii)-3.02×(iii)+3.05×(iv)+5.00, where P is the probability of malignancy. The total predictive value was 97.3%. The only exception was multiple myeloma with features of a benign fracture.
Although each MRI feature had a different meaning with a variable differentiation power, combining them led to an accurate diagnosis. This study identified the most relevant MRI features that would be helpful in discriminating benign from malignant vertebral fractures.
使用磁共振成像(MRI)进行回顾性分析。
确定能够区分良性与恶性椎体骨折的MRI特征。
区分良性和恶性椎体骨折仍然具有挑战性,尤其是在骨质疏松症和癌症患者中。目前,检测和评估骨折病因最敏感的方法是MRI。然而,已发表的报告仅关注一个或几个鉴别因素。
共有106例患者在发生114处胸椎和/或腰椎骨折后的六周内接受了MRI评估(良性骨折65例;恶性骨折49例)。骨折若为恶性则经病理证实,若为良性则经临床诊断,并至少随访六个月。对所有骨折的磁共振图像分析了17项特征。使用卡方检验分析单个参数;使用多变量逻辑回归分析建立逻辑模型。
卡方检验显示11项恶性参数和4项良性参数。多变量逻辑回归分析选择了:(i)后壁弥漫性突出(优势比[OR],48;95%置信区间[CI],4.2 - 548;P = 0.002),(ii)椎弓根受累(OR,21;95% CI,2.0 - 229;P = 0.01),(iii)后部受累(OR,21;95% CI,1.5 - 21;P = 0.02),以及(iv)条带模式(OR,0.047;95% CI,0.0005 - 4.7;P = 0.19)。逻辑模型表示为P = 1/[1 + exp(x)],x = -3.88×(i) - 3.05×(ii) - 3.02×(iii) + 3.05×(iv) + 5.00,其中P为恶性概率。总预测值为97.3%。唯一的例外是具有良性骨折特征的多发性骨髓瘤。
虽然每个MRI特征的意义不同,鉴别能力也不同,但将它们结合起来可实现准确诊断。本研究确定了有助于区分良性与恶性椎体骨折的最相关MRI特征。