Duan, Jl., Deng, B., Song, Gh. et al. Application of Computer-Aided Tongue Inspection for Preliminary Screening of Esophageal Cancer., Chin. J. Integr. Med. 24, 746–751 (2018). https://doi.org/10.1007/s11655-018-2840-6
Jin-long Duan, Bo Deng, Guo-hui Song, et al. Application of Computer-Aided Tongue Inspection for Preliminary Screening of Esophageal Cancer[J]. Chinese Journal of Integrative Medicine, 2018,24(10):746-751.
Duan, Jl., Deng, B., Song, Gh. et al. Application of Computer-Aided Tongue Inspection for Preliminary Screening of Esophageal Cancer., Chin. J. Integr. Med. 24, 746–751 (2018). https://doi.org/10.1007/s11655-018-2840-6DOI:
Jin-long Duan, Bo Deng, Guo-hui Song, et al. Application of Computer-Aided Tongue Inspection for Preliminary Screening of Esophageal Cancer[J]. Chinese Journal of Integrative Medicine, 2018,24(10):746-751. DOI: 10.1007/s11655-018-2840-6.
Application of Computer-Aided Tongue Inspection for Preliminary Screening of Esophageal Cancer
摘要
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.
Abstract
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.
Enzinger PC, Mayer RJ. Esophageal cancer. N Engl J Med 2003;349:2241–2252.
Ajani JA, D’Amico TA, Almhanna K, Bentrem DJ, Besh S, Chao J, et al. Esophageal and esophagogastric junction cancers, version 1. 2015. J Natl Compr Canc Netw 2015;13:194–227.
Malhotra GK, Yanala U, Ravipati A, Follet M, Vijayakumar M, Are C. Global trends in esophageal cancer. J Surg Oncol 2017;115:564–579.
Shan BE, ed. 2014 Hebei cancer registry annual report. Beijing: Military Medical Science Press; 2014:81–83.
Xie N, Zhang GX, eds. Basis of Chinese medicine. Beijing: China Press of Traditional Chinese Medicine; 2016:30–35.
Xu JT, ed. Clinical illustration of tongue diagnosis of traditional Chinese medicine. Beijing: Chemical Induatry Press; 2017:16–18.
Mist S, Ritenbaugh C, Aickin M. Effects of questionnaire-based diagnosis and training on inter-rater reliability among practitioners of traditional Chinese medicine. J Altern Complement Med 2009;15:703–709.
Zhang GG, Lee WL, Lao L, Bausell B, Berman B, Handwerger B. The variability of TCM pattern diagnosis and herbal prescription on rheumatoid arthritis patients. Altern Ther Health Med 2004;10:58–63.
Lo LC, Chen YF, Chen WJ, Cheng TL, Chiang JY. The study on the agreement between automatic tongue diagnosis system and traditional Chinese medicine practitioners. Evid Based Complement Alternat Med 2012;2012:505063.
Ishigaki M, Maeda Y, Taketani A, Andriana BB, Ishihara R, Wongravee K, et al. Diagnosis of early-stage esophageal cancer by Raman spectroscopy and chemometric techniques. Analyst 2016;141:1027–1033.
van der Heijden H. Decision support for selecting optimal logistic regression models. Expert Syst Appl 2012;39:8573–8583.
Lee YW, Chen TL, Shih YR, Tsai CL, Chiang CC, Liang HH, et al. Adjunctive traditional Chinese medicine therapy improves survival in patients with advanced breast cancer: a population-based study. Cancer 2014;120:1338–1344.
Qi F, Zhao L, Zhou A, Zhang B, Li A, Wang Z, Han J. The advantages of using traditional Chinese medicine as an adjunctive therapy in the whole course of cancer treatment instead of only terminal stage of cancer. Biosci Trends 2015;9:16–34.
Freitas NR, Vieira PM, Lima E, Lima CS. Automatic T1 bladder tumor detection by using wavelet analysis in cystoscopy images. Phys Med Biol 2017; doi: 10.1088/1361-6560/aaa3af.