Duan Jin-Long, Deng Bo, Song Guo-Hui, Chen Zhi-Feng, Gong Yan-Wei, He Yu-Hua, Jia Li-Qun
School of Clinical Medicine, Beijing University of Chinese Medicine, Beijing, 100029, China.
Department of Oncology of Integrative Chinese and Western Medicine, China-Japan Friendship Hospital, Beijing, 100029, China.
Chin J Integr Med. 2018 Oct;24(10):746-751. doi: 10.1007/s11655-018-2840-6. Epub 2018 Apr 17.
To differentiate patients with esophageal cancer or premalignant lesions from the high-risk population for preliminary screening of esophageal cancer using a feature index determined by a computer-aided tongue information acquisition and processing system (DS01-B).
Totally, 213 patients diagnosed with esophageal cancer or premalignant lesions and 2,840 normal subjects were collected including primarily screened and reexamined, all of them were confirmed with histological examinations. Their tongue color space values and manifestation features were extracted by DS01-B and analyzed. Firstly, the analysis of variance was performed to differentiate normal subjects from patients with esophageal cancer and premalignant lesions. Secondly, the logistic regression was conducted using 10 features and gender, age to get a predictive equation of the possibility of esophageal cancer or premalignant lesions. Lastly, the equation was tested by subjects undergoing primary screening.
Saturation (S) values in the HSV color space showed significant differences between patients with esophageal cancer and normal subjects or those with mild atypical hyperplasia (P<0.05); blue-to-yellow (b) values in the Lab color space showed significant differences between patients with esophageal cancer or premalignant lesions and normal subjects (P<0.05). Logistic regression analysis showed that the computer-aided tongue inspection approach had an accuracy of 72.3% (2008/2776) in identifying patients with esophageal cancer or premalignant lesions for preliminary screening in high-risk population.
Computer-aided tongue inspection, with descriptive and quantitative profile as described in this study, could be applied as a cost- and timeefficient, non-invasive approach for preliminary screening of esophageal cancer in high-risk population.
利用计算机辅助舌象信息采集与处理系统(DS01 - B)所确定的特征指标,将食管癌或癌前病变患者与食管癌高危人群进行区分,以实现食管癌的初步筛查。
共收集213例经组织学检查确诊的食管癌或癌前病变患者以及2840例正常受试者,包括初筛和复查者。通过DS01 - B提取他们的舌色空间值及表现特征并进行分析。首先,采用方差分析区分正常受试者与食管癌及癌前病变患者。其次,使用10个特征以及性别、年龄进行逻辑回归分析,得到食管癌或癌前病变可能性的预测方程。最后,用初筛受试者对该方程进行检验。
HSV颜色空间中的饱和度(S)值在食管癌患者与正常受试者或轻度非典型增生患者之间存在显著差异(P<0.05);Lab颜色空间中的蓝黄比(b)值在食管癌或癌前病变患者与正常受试者之间存在显著差异(P<0.05)。逻辑回归分析表明,计算机辅助舌诊方法在识别食管癌或癌前病变患者以进行高危人群初步筛查时,准确率为72.3%(2008/2776)。
本研究中所描述的具有描述性和定量特征的计算机辅助舌诊,可作为一种经济高效、无创的方法,用于高危人群食管癌的初步筛查。