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首页> 外文期刊>Japanese journal of radiology >Computer-aided diagnosis of lung cancer: definition and detection of ground-glass opacity type of nodules by high-resolution computed tomography.
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Computer-aided diagnosis of lung cancer: definition and detection of ground-glass opacity type of nodules by high-resolution computed tomography.

机译:肺癌的计算机辅助诊断:高分辨率计算机断层摄影术定义和检测毛玻璃样结节。

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PURPOSE: The ground-glass opacity (GGO) of lung cancer is identified only subjectively on computed tomography (CT) images as no quantitative characteristic has been defined for GGOs. We sought to define GGOs quantitatively and to differentiate between GGOs and solid-type lung cancers semiautomatically with a computer-aided diagnosis (CAD). METHODS AND MATERIALS: High-resolution CT images of 100 pulmonary nodules (all peripheral lung cancers) were collected from our clinical records. Two radiologists traced the contours of nodules and distinguished GGOs from solid areas. The CT attenuation value of each area was measured. Differentiation between cancer types was assessed by a receiver-operating characteristic (ROC) analysis. RESULTS: The mean CT attenuation of the GGO areas was -618.4 +/- 212.2 HU, whereas that of solid areas was -68.1 +/- 230.3 HU. CAD differentiated between solidand GGO-type lung cancers with a sensitivity of 86.0% and specificity of 96.5% when the threshold value was -370 HU. Four nodules of mixed GGOs were incorrectly classified as the solid type. CAD detected 96.3% of GGO areas when the threshold between GGO and solid areas was 194 HU. CONCLUSION: Objective definition of GGO area by CT attenuation is feasible. This method is useful for semiautomatic differentiation between GGOs and solid types of lung cancer.
机译:目的:仅在计算机断层扫描(CT)图像上主观地识别出肺癌的玻璃杯混浊度(GGO),因为尚未为GGO定义定量特征。我们试图定量定义GGO,并通过计算机辅助诊断(CAD)半自动地区分GGO和实体型肺癌。方法和材料:从我们的临床记录中收集了100个肺结节(所有周围型肺癌)的高分辨率CT图像。两名放射科医生追踪了结节的轮廓,并从实体区域中区分出了GGO。测量每个区域的CT衰减值。癌症类型之间的区别通过接受者操作特征(ROC)分析进行评估。结果:GGO区域的平均CT衰减为-618.4 +/- 212.2 HU,而实心区域的平均CT衰减为-68.1 +/- 230.3 HU。当阈值为-370 HU时,CAD可以区分实体型和GGO型肺癌,灵敏度为86.0%,特异性为96.5%。混合的GGO的四个结节被错误地分类为实体类型。当GGO与实体区域之间的阈值为194 HU时,CAD检测到GGO区域的96.3%。结论:通过CT衰减客观确定GGO面积是可行的。此方法可用于GGO和实体类型肺癌之间的半自动区分。

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