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Meta-Analysis of the Correlation between TCM Syndromes of Lung Cancer and CT through Data Mining and Computer Software
Author(s) -
Yifan Su,
Dehui Li,
Huanfang Fan
Publication year - 2021
Publication title -
journal of physics conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2138/1/012017
Subject(s) - phlegm , medicine , lung cancer , blood stasis , meta analysis , sign (mathematics) , cochrane library , traditional chinese medicine , pathology , radiology , mathematics , alternative medicine , mathematical analysis
To systematically evaluate the correlation between the traditional Chinese medicine (TCM) syndromes of lung cancer and the imaging manifestations of CT. Computer search of CNKI, Cochrane Library, PubMed, Springer, CBM, VIP, Wanfang database, Baidu library and other major databases. Collect the relevant literature on the TCM syndromes of lung cancer and CT imaging manifestation since the database was built until September 1, 2021. Two researchers collected literature and evaluated the quality of the literature, conducted data mining on the literature, and used the computer Revman 5.3 software to conduct a Meta-analysis of the included literature. The results showed that the phlegm dampness type lobular sign was higher than the burr sign, and there was no significant difference between vacuole sign and cavity sign; In Qi-Yin deficiency type, lobular sign was higher than burr sign, vacuole sign was higher than cavity sign; In Qi stagnation blood stasis type, lobular sign is higher than burr sign. The CT lobular sign of lung cancer are mainly phlegm dampness type, Qi-Yin deficiency type and Qi stagnation blood stasis type. Vacuole sign is mainly Qi-Yin deficiency type. Burr sign and cavity sign are less in the above three types. In this study, the combination of computer and meta-analysis technology has promoted the development of lung cancer micro-differentiation theory and assisted in improving the treatment level of lung cancer clinical syndrome differentiation.

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