Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan 48109-2099, USA.
Gastrointest Endosc. 2010 Jan;71(1):53-63. doi: 10.1016/j.gie.2009.08.027. Epub 2009 Nov 17.
Quantitative spectral analysis of the radiofrequency (RF) signals that underlie grayscale EUS images can be used to provide additional, objective information about tissue state.
Our purpose was to validate RF spectral analysis as a method to distinguish between (1) benign and malignant lymph nodes and (2) normal pancreas, chronic pancreatitis, and pancreatic cancer.
A prospective validation study of eligible patients was conducted to compare with pilot study RF data.
Forty-three patients underwent EUS of the esophagus, stomach, pancreas, and surrounding intra-abdominal and mediastinal lymph nodes (19 from a previous pilot study and 24 additional patients).
Midband fit, slope, intercept, and correlation coefficient from a linear regression of the calibrated RF power spectra were determined.
Discriminant analysis of mean pilot-study parameters was then performed to classify validation-study parameters. For benign versus malignant lymph nodes, midband fit and intercept (both with t test P < .058) provided classification with 67% accuracy and area under the receiver operating curve (AUC) of 0.86. For diseased versus normal pancreas, midband fit and correlation coefficient (both with analysis of variance P < .001) provided 93% accuracy and an AUC of 0.98. For pancreatic cancer versus chronic pancreatitis, the same parameters provided 77% accuracy and an AUC of 0.89. Results improved further when classification was performed with all data.
Moderate sample size and spatial averaging inherent to the technique.
This study confirms that mean spectral parameters provide a noninvasive method to quantitatively discriminate benign and malignant lymph nodes as well as normal and diseased pancreas.
对灰阶超声内镜图像所依赖的射频（RF）信号进行定量频谱分析，可提供有关组织状态的额外客观信息。
本研究旨在验证 RF 频谱分析作为一种方法，用于区分（1）良性和恶性淋巴结，以及（2）正常胰腺、慢性胰腺炎和胰腺癌。
对合格患者进行前瞻性验证研究，与试点研究 RF 数据进行比较。
43 例患者接受了食管、胃、胰腺及周围腹腔和纵隔淋巴结的超声内镜检查（19 例来自先前的试点研究，24 例为额外患者）。
从中校准的 RF 功率谱的线性回归中确定中带拟合、斜率、截距和相关系数。
然后对平均试点研究参数进行判别分析，以对验证研究参数进行分类。对于良性与恶性淋巴结，中带拟合和截距（t 检验 P<0.058）具有分类能力，准确率为 67%，受试者工作特征曲线下面积（AUC）为 0.86。对于病变胰腺与正常胰腺，中带拟合和相关系数（方差分析 P<0.001）的准确率为 93%，AUC 为 0.98。对于胰腺癌与慢性胰腺炎，相同参数的准确率为 77%，AUC 为 0.89。当使用所有数据进行分类时，结果进一步改善。
该技术固有中等样本量和空间平均。
本研究证实，平均光谱参数提供了一种非侵入性方法，可定量区分良性和恶性淋巴结以及正常和病变胰腺。
