Friedland J A, Reinus W R, Fisher A J, Wilson A J
Mallinckrodt Institute of Radiology, Jewish Hospital, St. Louis, MO 63110, USA.
Invest Radiol. 1995 Aug;30(8):474-9. doi: 10.1097/00004424-199508000-00004.
To quantitate radiographic features that distinguish the plain radiographic appearance of nonossifying fibroma (NOF) from other solitary lesions of bone.
Seven hundred nine cases of focal bone lesions, including 34 NOFs, were analyzed according to demographic, anatomic, and plain radiographic features. Vector analysis of groups of features was performed to determine those that are most sensitive and specific for the appearance of NOF in contrast to other lesions in the data base.
The radiographic appearance of NOFs was most consistently a medullary based (97%), lytic lesion (100%) with geographic bone destruction (100%), marginal sclerosis (97%), and well-defined edges (94%). A statistically significant number of lesions were located in the distal aspect of long bones. Unicameral bone cyst shared the most radiographic features with the NOF. Vector analysis showed a large degree of overlap between NOF and other lesions such as aneurysmal bone cyst, chondromyxoid fibroma, and eosinophilic granuloma. The description that optimized sensitivity and prevalence for detection of NOF is a medullary based, ovoid lesion in the distal or proximal portions of a long bone with well-defined edges, a partial or complete rind of sclerosis, and absence of fallen fragment, periosteal reaction, and cortical disruption.
The radiographic appearance of NOF is relatively nonspecific but, using vector analysis, can be better elucidated over current textbook descriptions.
量化可区分非骨化性纤维瘤（NOF）与其他孤立性骨病变X线平片表现的影像学特征。
根据人口统计学、解剖学和X线平片特征，分析了709例局灶性骨病变，其中包括34例NOF。对特征组进行向量分析，以确定与数据库中其他病变相比，对NOF表现最敏感和特异的特征。
NOF的X线表现最常见的是髓质型（97%）、溶骨性病变（100%），伴有地图样骨质破坏（100%）、边缘硬化（97%）和边界清晰（94%）。在长骨远端有统计学意义数量的病变。单房性骨囊肿与NOF具有最多的X线特征。向量分析显示NOF与其他病变如动脉瘤样骨囊肿、软骨黏液样纤维瘤和嗜酸性肉芽肿之间有很大程度的重叠。优化检测NOF敏感性和患病率的描述是位于长骨远端或近端的髓质型、椭圆形病变，边界清晰，有部分或完整的硬化边缘，无骨片陷落、骨膜反应和皮质破坏。
NOF的X线表现相对非特异性，但使用向量分析，与当前教科书描述相比可以得到更好的阐释。
