scholarly journals A Data Mining-Based Analysis of Core Herbs on Different Patterns (Zheng) of Non-Small Cell Lung Cancer

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Xiangjun Qi ◽  
Zehuai Guo ◽  
Qianying Chen ◽  
Wanning Lan ◽  
Zhuangzhong Chen ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Data Mining ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Blood Stasis ◽  
Small Cell ◽  
Small Cell Lung ◽  
Kidney Deficiency ◽  
Yin Deficiency ◽  
Stomach Meridian

Objective. To explore the role of Chinese prescriptions in non-small cell lung cancer (NSCLC) and provide references for the application of herbs and prescriptions. Methods. Randomized and quasirandomized controlled clinical trials on Chinese herbal medicine in the treatment of NSCLC were collected from seven databases to establish a database of prescriptions on NSCLC. Data-mining analyses were performed by RStudio (v4.0.3) software. Results. A total of 970 prescriptions were obtained from 945 included studies, involving 7 syndromes and 428 herbs. The main patterns of NSCLC included qi deficiency pattern, yin deficiency pattern, blood deficiency pattern, kidney deficiency pattern, heat toxin pattern, phlegm-dampness pattern, and blood stasis pattern. High-frequency herbs on NSCLC were Astragali Radix (Huangqi), Atractylodis Macrocephalae Rhizome (Baizhu), Glycyrrhizae Radix Rhizome (Gancao), Poria (Fuling), Ophiopogonis Radix (Maidong), Hedyotidis Diffusae Herba (Baihuasheshecao), Codonopsis Radix (Dangshen), and Glehniae Radix (Beishashen). The properties of the herbs were mainly cold, warm, and mild. The flavors of the herbs were mainly sweet, bitter, and pungent. The main meridian tropisms were Lung Meridian of Hand-Taiyin, Spleen Meridian of Foot-Taiyin, and Stomach Meridian of Foot-Yangming. Conclusion. Applying clearing and tonifying method by targeting the lung and spleen was the most frequently used therapy in the treatment of NSCLC. This study offered a glimpse of unique views of traditional Chinese medicine on NSCLC and may benefit the treatment of NSCLC.

Study on the syndrome characteristics and classification model of non-small cell lung cancer based on tongue and pulse data (Preprint)

2021 ◽  
Author(s):  
Yulin Shi ◽  
Jiayi Liu ◽  
Xiaojuan Hu ◽  
Liping Tu ◽  
Ji Cui ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Deficiency Syndrome ◽  
Small Cell ◽  
Classification Model ◽  
Syndrome Differentiation ◽  
Small Cell Lung ◽  
Yin Deficiency ◽  
Significant Difference

BACKGROUND Lung cancer is a common malignant tumor that affects people's health seriously. Traditional Chinese medicine (TCM) is one of the effective methods for the treatment of advanced lung cancer, accurate TCM syndrome differentiation is essential to treatment. When the symptoms are not obvious, the traditional symptom-based syndrome differentiation cannot be carried out. There is a close relationship between syndrom and index of western medicine, the combination of micro index and macro symptom can assist syndrome differentiation effectively. OBJECTIVE To explore the characteristics of tongue and pulse data of non-small cell lung cancer (NSCLC) with Qi deficiency syndrome and Yin deficiency syndrome, and to establish syndromes classification model based on tongue and pulse data by using machine learning method, and to evaluate the feasibility of the model. METHODS Tongue and pulse data of non-small cell lung cancer (NSCLC) patients with Qi deficiency syndrome (n=163), patients with Yin deficiency syndrome (n=174) and healthy controls (n=185) were collected by using intelligent Tongue and Face Diagnosis Analysis-1 instrument and Pulse Diagnosis Analysis-1 instrument, respectively. The characteristics of tongue and pulse data were analyzed, the correlation analysis was also made on tongue and pulse data. And four machine learning methods, namely Random Forest, Logistic Regression, Support Vector Machine and Neural Network, were used to establish the classification models based on symptoms, tongue & pulse data, and symptoms & tongue & pulse data, respectively. RESULTS Significant difference indexes of tongue diagnosis between Qi deficiency syndrome and Yin deficiency syndrome were TB-a, TB-S, TB-Cr, TC-a, TC-S, TC-Cr, perAll and the tongue coating texture indexes including TC-Con, TC-ASM, TC-MEAN, and TC-ENT. Significant difference indexes of pulse diagnosis were t4 and t5. The classification performance of each model based on different data sets was as follows: model of tongue & pulse data <model of symptom < model of symptom & tongue & pulse data. The Neural Network model had a better classification performance for the symptom & tongue & pulse data, with an area under the ROC curve and accuracy rate were 0.9401 and 0.8806. CONCLUSIONS This study explored the characteristics of tongue and pulse data of NSCLC with Qi deficiency syndrome and Yin deficiency syndrome, and established syndromes classification model. It was feasible to use tongue and pulse data as one of the objective diagnostic indexes in Qi deficiency syndrome and Yin deficiency syndrome of NSCLC. CLINICALTRIAL Trial registration number: ChiCTR1900026008; ChiCTR-IOR-15006502 Date of registration: Jun. 04th, 2015 URL of trial registry record: http://www.chictr.org.cn/showprojen.aspx?proj=11119; http://www.chictr.org.cn/edit.aspx?pid=38828&htm=4 (This is a retrospective registration)

scholarly journals Data Mining Cytokines Profile for Prognosis in Advanced Non-Small Cell Lung Cancer

2014 ◽  
Vol 25 ◽  
pp. iv67
Author(s):  
L. Barrera ◽  
O. Arrieta ◽  
E. Montes-Servín ◽  
L.A. Ramírez-Tirado ◽  
J.L. Bañales-Mendez ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Data Mining ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Small Cell ◽  
Small Cell Lung

scholarly journals Applied research on serum protein fingerprints for prediction of Qi deficiency syndrome and phlegm and blood stasis in patients with non-small cell lung cancer

2012 ◽  
Vol 32 (3) ◽  
pp. 350-354 ◽  
Author(s):  
Zhizhen Liu ◽  
Zongyang Yu ◽  
Xuenong OuYang ◽  
Jian Du ◽  
Xiaopeng Lan ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Serum Protein ◽  
Cell Lung Cancer ◽  
Applied Research ◽  
Blood Stasis ◽  
Deficiency Syndrome ◽  
Small Cell ◽  
Small Cell Lung

scholarly journals A New Method for Syndrome Classification of Non-Small-Cell Lung Cancer Based on Data of Tongue and Pulse with Machine Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Yu-lin Shi ◽  
Jia-yi Liu ◽  
Xiao-juan Hu ◽  
Li-ping Tu ◽  
Ji Cui ◽  
...  
Keyword(s):  
Machine Learning ◽  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Deficiency Syndrome ◽  
Small Cell ◽  
Small Cell Lung ◽  
Yin Deficiency ◽  
Significant Difference ◽  
Syndrome Classification

Objective. To explore the data characteristics of tongue and pulse of non-small-cell lung cancer with Qi deficiency syndrome and Yin deficiency syndrome, establish syndrome classification model based on data of tongue and pulse by using machine learning methods, and evaluate the feasibility of syndrome classification based on data of tongue and pulse. Methods. We collected tongue and pulse of non-small-cell lung cancer patients with Qi deficiency syndrome ( n = 163 ), patients with Yin deficiency syndrome ( n = 174 ), and healthy controls ( n = 185 ) using intelligent tongue diagnosis analysis instrument and pulse diagnosis analysis instrument, respectively. We described the characteristics and examined the correlation of data of tongue and pulse. Four machine learning methods, namely, random forest, logistic regression, support vector machine, and neural network, were used to establish the classification models based on symptom, tongue and pulse, and symptom and tongue and pulse, respectively. Results. Significant difference indices of tongue diagnosis between Qi deficiency syndrome and Yin deficiency syndrome were TB-a, TB-S, TB-Cr, TC-a, TC-S, TC-Cr, perAll, and the tongue coating texture indices including TC-CON, TC-ASM, TC-MEAN, and TC-ENT. Significant difference indices of pulse diagnosis were t4 and t5. The classification performance of each model based on different datasets was as follows: tongue and pulse < symptom < symptom and tongue and pulse. The neural network model had a better classification performance for symptom and tongue and pulse datasets, with an area under the ROC curves and accuracy rate which were 0.9401 and 0.8806. Conclusions. It was feasible to use tongue data and pulse data as one of the objective diagnostic basis in Qi deficiency syndrome and Yin deficiency syndrome of non-small-cell lung cancer.

scholarly journals Study on medication rules of modern chinese herbal medicine in the treatment of non-small cell lung cancer based on data mining

2020 ◽  
Vol 6 (1) ◽  
pp. 83
Author(s):  
Jie Li ◽  
Li-Ting Liu ◽  
Cui-Yun Zhao ◽  
Tong Wu ◽  
Zi-Yang Yu ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Data Mining ◽  
Herbal Medicine ◽  
Small Cell Lung Cancer ◽  
Chinese Herbal Medicine ◽  
Cell Lung Cancer ◽  
Small Cell ◽  
Small Cell Lung ◽  
Chinese Herbal ◽  
Modern Chinese

scholarly journals Cytokine profile determined by data-mining analysis set into clusters of non-small-cell lung cancer patients according to prognosis

2015 ◽  
Vol 26 (2) ◽  
pp. 428-435 ◽  
Author(s):  
L. Barrera ◽  
E. Montes-Servín ◽  
A. Barrera ◽  
L.A. Ramírez-Tirado ◽  
F. Salinas-Parra ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Data Mining ◽  
Small Cell Lung Cancer ◽  
Cancer Patients ◽  
Cell Lung Cancer ◽  
Small Cell ◽  
Cytokine Profile ◽  
Small Cell Lung ◽  
Lung Cancer Patients ◽  
Data Mining Analysis

scholarly journals Integrated DIA proteomics and lipidomics analysis on non-small cell lung cancer patients with TCM syndromes

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Song Cang ◽  
Ran Liu ◽  
Wei Jin ◽  
Qi Tang ◽  
Wanjun Li ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Chinese Medicine ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Small Cell ◽  
Diagnosis And Treatment ◽  
Sphingolipid Metabolism ◽  
Small Cell Lung ◽  
Yin Deficiency ◽  
Nsclc Patients

Abstract Background Lung cancer remains the leading cause of mortality from malignant tumors, non-small cell lung cancer (NSCLC) accounts for the majority of lung cancer cases, and individualized diagnosis and treatment is an effective trend. The individual characteristics of different traditional Chinese medicine (TCM) syndromes of NSCLC patients may be revealed by highly specific molecular profiles. Methods In this study, 10 NSCLC patients with Qi deficiency and Yin deficiency (QDYD) syndrome and 10 patients with Qi deficiency of lung-spleen (QDLS) syndrome in TNM stage III-IV as well as 10 healthy volunteers were enrolled. Aiming at the varied syndromes of NSCLC patients with “Yin deficiency” as the main difference, a proteomics research based on data-independent acquisition (DIA) was developed. Of the dysregulated proteins in NSCLC patients, lipid metabolism was significantly enriched. Thereafter, nontargeted lipidomics research based on UPLC-Q-TOF/MS was performed in 16 patients, with 8 individuals randomly selected from each syndrome group. Furthermore, the considerably different characteristics between the syndromes and pathological mechanisms of NSCLC were screened by statistical and biological integrations of proteomics and lipidomics and the differential metabolic pathways of the two similar syndromes were further explored. Besides, lipids biomarkers were verified by a clinically used anticancer Chinese medicine, and the level of key differential proteins in the two syndromes was also validated using ELISA. Results The results showed that glycerophospholipid metabolism, sphingolipid metabolism, glycolipid metabolism, and primary bile acid biosynthesis were altered in NSCLC patients and that glycerophospholipid metabolism was significantly changed between the two syndromes in lipidomics analysis. Among the proteins and lipids, ALDOC and lysophosphatidylcholine (LPCs) were revealed to have a strong relationship by statistical and biological integration analysis, and could effectively distinguish QDLS and QDYD syndromes. Notably, the patients with different syndromes had the most typical metabolic patterns in glycerophospholipid metabolism and glycolysis, reflecting the differences in the syndromes dominated by “Yin deficiency”. Conclusions ALDOC and LPCs could be employed for the differentiation of NSCLC patients with QDLS and QDYD syndromes, and “Yin deficiency” might be associated with glycerophospholipid metabolism and glycolysis pathway. The results provided a theoretical basis for “Syndrome differentiation” in TCM diagnosis. Moreover, the developed integrated strategy could also provide a reference for individualized diagnosis and treatment of other diseases.

Small cell lung cancer

2001 ◽  
Vol 37 ◽  
pp. 61-63
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Small Cell ◽  
Small Cell Lung

Non-small cell lung cancer

2001 ◽  
Vol 37 ◽  
pp. 47-61
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Small Cell ◽  
Small Cell Lung
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