峰度
结核(地质)
偏斜
直方图
放射治疗
放射科
医学
肺
放射性密度
病理
数学
射线照相术
生物
统计
人工智能
计算机科学
内科学
图像(数学)
古生物学
作者
Ayano Kamiya,Sadayuki Murayama,Hisashi Kamiya,Tsuneo Yamashiro,Yasuji Oshiro,Nobuyuki Tanaka
标识
DOI:10.1007/s11604-013-0264-y
摘要
The purpose of our study was to assess pulmonary nodule characteristics using density histogram kurtosis and skewness and to distinguish malignant from benign nodules.Ninety-three lung nodules on CT were analyzed, including 72 malignant and 21 benign nodules. They were completely solid or solid with limited ground-glass opacity. Based on their CT characteristics, nodules were categorized into type A, homogeneous nodules with uniform internal structures and clear margins, and type B, inhomogeneous nodules with heterogeneous structures or uneven margins. Kurtosis and skewness were calculated from density histograms to compare type A and B nodules and malignant and benign nodules. Receiver-operating characteristic (ROC) curves were generated to assess kurtosis and skewness for discriminating between different nodule types.Type A nodules (n = 35) had greater kurtosis and reduced skewness (p < 0.001) compared to type B nodules (n = 58). Malignant tumor kurtosis was greater than that of benign nodules (type A, p < 0.05; type B, p = 0.001). Type B malignant tumors had reduced skewness compared to benign nodules (p < 0.05). ROC curves provided relatively high values for the area under the curve (0.71-0.83).Kurtosis and skewness assessments of density histograms may be useful for differentiating malignant from benign nodules.
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