Department of Anatomy and Anthropology, Sackler Faculty of Medicine, Tel Aviv University, Tel-Aviv, Israel.
Anat Rec (Hoboken). 2010 Dec;293(12):2007-11. doi: 10.1002/ar.21274.
The aim of this study was to recognize the radiological characteristics of hyperostosis frontalis interna (HFI) and to establish a valid and reliable method for its identification and classification. A reliability test was carried out on 27 individuals who had undergone a head computerized tomography (CT) scan. Intra-observer reliability was obtained by examining the images three times, by the same researcher, with a 2-week interval between each sample ranking. The inter-observer test was performed by three independent researchers. A validity test was carried out using two methods for identifying and classifying HFI: 46 cadaver skullcaps were ranked twice via computerized tomography scans and then by direct observation. Reliability and validity were calculated using Kappa test (SPSS 15.0). Reliability tests of ranking HFI via CT scans demonstrated good results (K > 0.7). As for validity, a very good consensus was obtained between the CT and direct observation, when moderate and advanced types of HFI were present (K = 0.82). The suggested classification method for HFI, using CT, demonstrated a sensitivity of 84%, specificity of 90.5%, and positive predictive value of 91.3%. In conclusion, volume rendering is a reliable and valid tool for identifying HFI. The suggested three-scale classification is most suitable for radiological diagnosis of the phenomena. Considering the increasing awareness of HFI as an early indicator of a developing malady, this study may assist radiologists in identifying and classifying the phenomena.
本研究旨在认识颅顶部内骨膜增生症（HFI）的放射学特征，并建立一种有效的 HFI 识别和分类方法。对 27 名接受头部计算机断层扫描（CT）的个体进行了可靠性测试。通过同一位研究者在 2 周的间隔内对图像进行三次检查，获得了观察者内的可靠性。通过三位独立的研究人员进行了观察者间的测试。使用两种方法对 HFI 进行识别和分类，进行了有效性测试：通过计算机断层扫描对 46 个头盖骨进行了两次排名，然后进行了直接观察。使用 Kappa 检验（SPSS 15.0）计算可靠性和有效性。通过 CT 扫描对 HFI 进行排名的可靠性测试显示出良好的结果（K>0.7）。对于有效性，当存在中度和高级 HFI 时，CT 与直接观察之间达成了非常好的共识（K=0.82）。使用 CT 提出的 HFI 分类方法，具有 84%的敏感性、90.5%的特异性和 91.3%的阳性预测值。总之，容积再现是识别 HFI 的可靠和有效工具。所提出的三尺度分类最适合于该现象的放射学诊断。鉴于 HFI 作为潜在疾病的早期指标的意识不断提高，本研究可能有助于放射科医生识别和分类该现象。
