Abdominal Imaging Division, Department of Radiology, University of Colorado Denver, Anschutz Medical Campus, 12401 E 17th Ave, Mail Stop L954, Aurora, CO, 80045, USA.
Department of Radiology, Roswell Park Cancer Institute, Body Imaging Section Elm & Carlton Streets, Buffalo, NY, 14263, USA.
Abdom Radiol (NY). 2019 Mar;44(3):1033-1043. doi: 10.1007/s00261-018-1871-y.
To investigate whether the histogram analysis method of characterizing adrenal nodules as adenomas is affected by increased noise with modern CT technique, and if an extension that allows for noise correction will improve diagnostic performance.
This is a HIPAA-compliant, IRB-approved retrospective study performed on 58 total patients. The first group of 29 patients had 33 adrenal lesions that were pathology-proven non-adenomas. The second group had 29 patients with 33 pathology-proven or presumed adenomas based on established imaging criteria. The nodules were evaluated using the histogram method, mean attenuation method, and a Gaussian model-based algorithm without (uncorrected Gaussian algorithm) and with correction (corrected Gaussian algorithm) for image noise. Sensitivity, specificity, and accuracy for identifying adenoma were derived.
There were no significant differences in identifying adenoma from non-adenoma when using the histogram analysis method and the uncorrected Gaussian algorithm, both of which had low specificities of 42.4% and 47.0%, respectively (p = 0.30). Adding noise correction to the Gaussian algorithm resulted in a statistically significant increase in specificity relative to the histogram method (86.4% vs. 42.4%, p < 0.001). The corrected Gaussian algorithm improved sensitivity compared to the mean attenuation method (71.2% vs. 54.5%, p < 0.001), but had lower specificity (86.4% vs. 100%, p < 0.001), and similar overall accuracy (78.8% vs. 77.3%, p = 0.74).
With modern low-dose CT technique, the specificity scores of the histogram method for discrimination of adrenal adenomas and non-adenomas are lower than with previous higher dose scans. The specificity and accuracy of a histogram-equivalent method can be increased mathematically through image noise correction, and the corrected Gaussian algorithm has improved sensitivity to the mean attenuation with similar accuracy albeit with lower specificity. Although this suggests limited utility for histogram analysis in adrenal nodule characterization, our study demonstrates the potential mathematical application for other noise-dependent CT characterization methods.
研究在现代 CT 技术中，由于噪声增加，描述肾上腺结节为腺瘤的直方图分析方法是否受到影响，如果扩展允许进行噪声校正，是否会提高诊断性能。
这是一项符合 HIPAA 标准、IRB 批准的回顾性研究，共纳入 58 例患者。第一组 29 例患者有 33 个经病理证实为非腺瘤的肾上腺病变。第二组有 29 例患者，根据既定的影像学标准，有 33 个经病理证实或推测为腺瘤的病变。使用直方图法、平均衰减法和基于高斯模型的算法（无图像噪声校正的未校正高斯算法和校正图像噪声的校正高斯算法）对结节进行评估。得出识别腺瘤的敏感性、特异性和准确性。
使用直方图分析方法和未校正高斯算法识别腺瘤与非腺瘤时，结果没有显著差异，特异性均较低，分别为 42.4%和 47.0%（p=0.30）。在高斯算法中加入噪声校正后，特异性相对于直方图方法有统计学显著提高（86.4%比 42.4%，p<0.001）。校正高斯算法与平均衰减法相比，敏感性提高（71.2%比 54.5%，p<0.001），但特异性降低（86.4%比 100%，p<0.001），总体准确性相似（78.8%比 77.3%，p=0.74）。
使用现代低剂量 CT 技术，直方图方法对肾上腺腺瘤和非腺瘤进行鉴别诊断的特异性评分低于以往更高剂量的扫描。通过图像噪声校正，可对直方图等效方法的特异性和准确性进行数学上的提高，校正高斯算法对平均衰减的敏感性提高，准确性相似，特异性降低。虽然这表明直方图分析在肾上腺结节特征描述中的应用有限，但本研究证明了其他依赖噪声的 CT 特征描述方法在数学上的潜在应用。
