Mining intrinsic information of convalescent patients after suffering coronavirus disease 2019 in Wuhan

doi:10.19852/j.cnki.jtcm.20220225.001

Journal of Traditional Chinese Medicine ›› 2022, Vol. 42 ›› Issue (2): 279-288.DOI: 10.19852/j.cnki.jtcm.20220225.001

• Research Articles • Previous Articles Next Articles

Mining intrinsic information of convalescent patients after suffering coronavirus disease 2019 in Wuhan

YAN Shixing¹, Lü Yi², LIU Ziqing³, REN Meng², HE Haiyang¹, XIAO Li⁵, GUO Feng¹, PENG Miao², LI Xiaoxia¹, WANG Yong⁴, XU Xi², YANG Tao⁶, SHAO Zuoyu², HUANG Jingjing², XIAO Mingzhong²()

1 Department of TCM Data Intelligence, Shanghai Daosh Medical Technology Co., Ltd., Shanghai 201200, China
2 Hepatic Disease Institute, Hubei Key Laboratory of Theoretical and Applied Research of Liver and Kidney in Traditional Chinese Medicine, Hubei Provincial Hospital of Traditional Chinese Medicine, Wuhan 430061, China; Affiliated Hospital of Hubei University of Chinese Medicine, Wuhan 430061, China; Hubei Academy of Traditional Chinese Medicine, Wuhan 430074, China
3 Electronical Medical Records and Information Management Center, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai 200437, China
4 Chinese Medicine Development research Center, Shanghai Literature Institute of TCM, Shanghai 200025, China
5 College of Acupuncture and Massage, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
6 College of Artificial Intelligence and Information Technology, Institute of Information and Technology Nanjing University of Chinese Medicine, Nanjing 210023, China

Received:2021-07-07 Accepted:2021-10-29 Online:2022-02-25 Published:2022-02-25
Contact: XIAO Mingzhong
About author:Dr. XIAO Mingzhong, Hepatic Disease Institute, Hubei Key Laboratory of Theoretical and Applied Research of Liver and Kidney in Traditional Chinese Medicine, Hubei Provincial Hospital of Traditional Chinese Medicine, Wuhan 430061, China; Affifiliated Hospital of Hubei University of Chinese Medicine, Wuhan 430061, China; Hubei Academy of Traditional Chinese Medicine, Wuhan 430074, China. xmz0001@sohu.com, Telephone: +86-18908640865
Supported by:
National key research and development plan-Clinical Evaluation of TCM Intervention in COVID-19 Recovery(2020YFC0845000);Clinical study on the prevention and treatment of COVID-19 with integrated Chinese and Western Medicine(2020YFC0841600);National Administration of Traditional Chinese Medicine-TCM Emergency Response Project for COVID-19(2020ZYLCYJ04)

Abstract

Abstract:

OBJECTIVE: To summarize the potential characteristics of convalescent patients with coronavirus disease 2019 (COVID-19) in China based on emerging clinical tongue data and guide the treatment and recovery of COVID-19 patients from the perspective of Traditional Chinese Medicine tongue diagnosis.

METHODS: In this study, we developed and validated radiomics-based and lab-based methods as a novel approach to provide individualized pretreatment evaluation by analyzing different features to mine the orderliness behind tongue data of convalescent patients. In addition, this study analyzed the tongue features of convalescent patients from clinical tongue qualitative values, including thick and thin, fur, peeling, fat and lean, tooth marks and cracked, and greasy and putrid fur.

RESULTS: We included 2164 tongue images in total (34% from day 0, 35.4% from day 14 and 30.6% from day 28) from convalescent patients. The significance results are shown as follows. Firstly, as the recovery time prolongs, the L average values of tongue and coat decrease from 60.21 to 57.18 and from 60.06 to 57.03 respectively. Secondly, the decrease of abnormality rate of tongue coat, included greasy tongue fur, putrid fur, teeth-mark, thick-thin fur, are of significant statistical difference (P < 0.05). Thirdly, the average value of gray-level co-occurrence matrices increases from 0.173 to 0.194, the average value of entropy increases from 0.606 to 0.665, the average value of inverse difference normalized decrease from 0.981 to 0.979, and the average value of dissimilarity decrease from 0.1576 to 0.1828. The details of other radiomics features are describe in results section.

CONCLUSIONS: Our experiment shows that patients in different recovery periods have a relationship with quantitative values of tongue images, including L color space of the tongue and coat radiomics features analysis. This relationship can help clinical doctors master the recovery and health of patients as soon as possible and improve their understanding of the potential mechanisms underlying the dynamic changes and mechanisms underlying COVID-19.

Key words: COVID-19, tongue inspection, quantitative values, dynamic change mechanisms, radiomics features analysis

YAN Shixing, Lü Yi, LIU Ziqing, REN Meng, HE Haiyang, XIAO Li, GUO Feng, PENG Miao, LI Xiaoxia, WANG Yong, XU Xi, YANG Tao, SHAO Zuoyu, HUANG Jingjing, XIAO Mingzhong. Mining intrinsic information of convalescent patients after suffering coronavirus disease 2019 in Wuhan[J]. Journal of Traditional Chinese Medicine, 2022, 42(2): 279-288.

Figures/Tables 9

References 32.

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Item	Radiomic feature
First order	10 percentile
	Energy feature
	Interquartile range
	Maximum feature
	Mean absolute deviation
	Mean feature
	Median feature
	Minimum feature
	Range feature V
	Robust mean absolute deviation
	Standard deviation feature
	Total energy feature
GLCM	Autocorrelation
	Contrast
	Entropy
	Idn
	Cluster prominence
	Dissimilarity
	Joint energy
GLRM	Gray-level non uniformity
	Long-run emphasis
	Low gray-level run
	Short-run emphasis
GLSZM	Small area emphasis
	Small area low gray
	Zone percent
NGTDM	Busyness
	Coarseness
	Complexity
	Contrast
	Strength

Item	Radiomic feature
First order	10 percentile
	Energy feature
	Interquartile range
	Maximum feature
	Mean absolute deviation
	Mean feature
	Median feature
	Minimum feature
	Range feature V
	Robust mean absolute deviation
	Standard deviation feature
	Total energy feature
GLCM	Autocorrelation
	Contrast
	Entropy
	Idn
	Cluster prominence
	Dissimilarity
	Joint energy
GLRM	Gray-level non uniformity
	Long-run emphasis
	Low gray-level run
	Short-run emphasis
GLSZM	Small area emphasis
	Small area low gray
	Zone percent
NGTDM	Busyness
	Coarseness
	Complexity
	Contrast
	Strength

Tongue feature	Feature	Day 0 (n = 737)				Day 28 (n = 659)		P value
Tongue feature	Feature	Numbers	Rate (%)	Numbers	Rate (%)	Numbers	Rate (%)	P value
Greasy fur	YES	355	48.17	384	50.00	243	36.87	< 0.01
Greasy fur	NO	382	51.83	384	50.00	416	63.13	< 0.01
putrid fur	YES	50	6.78	48	6.25	17	2.58	< 0.01
putrid fur	NO	687	93.22	720	93.75	642	97.42	< 0.01
Peeling	YES	114	15.47	148	19.27	130	19.73	0.070
Peeling	NO	623	84.53	620	80.73	529	80.27	0.070
Teeth-mark	YES	187	25.37	241	32.70	91	13.81	< 0.01
Teeth-mark	NO	550	74.63	527	71.51	568	86.19	< 0.01
Crack	YES	173	23.47	182	23.70	145	22.00	0.6517
Crack	NO	564	76.53	586	76.30	514	78.00	0.6517
Thick-thin	Thick	570	77.34	624	81.25	471	71.47	< 0.01
Thick-thin	Thin	167	22.66	144	18.75	188	28.53	< 0.01
Fat-lean	Fat	268	36.36	255	33.20	229	34.75	0.33
	Lean	2	0.27	2	0.26	2	0.30
	Moderate	467	63.37	511	66.54	428	64.95

Tongue feature	Feature	Day 0 (n = 737)				Day 28 (n = 659)		P value
Tongue feature	Feature	Numbers	Rate (%)	Numbers	Rate (%)	Numbers	Rate (%)	P value
Greasy fur	YES	355	48.17	384	50.00	243	36.87	< 0.01
Greasy fur	NO	382	51.83	384	50.00	416	63.13	< 0.01
putrid fur	YES	50	6.78	48	6.25	17	2.58	< 0.01
putrid fur	NO	687	93.22	720	93.75	642	97.42	< 0.01
Peeling	YES	114	15.47	148	19.27	130	19.73	0.070
Peeling	NO	623	84.53	620	80.73	529	80.27	0.070
Teeth-mark	YES	187	25.37	241	32.70	91	13.81	< 0.01
Teeth-mark	NO	550	74.63	527	71.51	568	86.19	< 0.01
Crack	YES	173	23.47	182	23.70	145	22.00	0.6517
Crack	NO	564	76.53	586	76.30	514	78.00	0.6517
Thick-thin	Thick	570	77.34	624	81.25	471	71.47	< 0.01
Thick-thin	Thin	167	22.66	144	18.75	188	28.53	< 0.01
Fat-lean	Fat	268	36.36	255	33.20	229	34.75	0.33
	Lean	2	0.27	2	0.26	2	0.30
	Moderate	467	63.37	511	66.54	428	64.95

Recovery day	Mean
	Tongue					Coat
	L	A	B		L		A	B
0	60.21	12.24	6.4	60.06			13.92	6.59
14	59.48	13.69	8.29	59.78			15.22	8.39
28	57.18	11.83	3.53	57.03			13.89	3.84

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Figures/Tables 9

References 32.

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Radiomics feature	Recovery day
Radiomics feature	0 d	14 d	28 d
CoatGLCMAutocorrelation	30.910	28.230	30.960
CoatGLCMContrast	0.173	0.174	0.194
CoatGLCMEntropy	0.606	0.600	0.665
CoatGLCMIdn	0.981	0.980	0.979
CoatGLCMClusterProminence	117.570	90.660	100.910
CoatGLCMDissimilarity	0.158	0.159	0.183
CoatGLCMJointEnergy	0.316	0.356	0.262
CoatGLRMGrayLevelNonUniformity	1913	2895	1130
CoatGLRMLongRunEmphasis	346.100	371.410	129.580
CoatGLRMLowGrayLevelRun	0.069	0.081	0.063
CoatGLRMShortRunEmphasis	0.289	0.293	0.312
CoatGLSZMSmallAreaEmphasis	0.315	0.310	0.331
CoatGLSZMSmallAreaLowGray	0.024	0.028	0.024
CoatGLSZMZonePercent	0.014	0.015	0.017
CoatNGTDMBusyness	54.000	86.000	25.000
CoatNGTDMCoarseness	0.001	0.002	0.001
CoatNGTDMComplexity	4.291	3.706	4.901
CoatNGTDMContrast	0.006	0.005	0.007
CoatNGTDMStrength	0.041	0.045	0.043
CoatFirstOrder10Percentile	148.730	145.950	143.300
CoatFirstOrderEnergyFeature	1.599	1.470	1.789
CoatFirstOrderInterquartileRange	10.276	8.998	12.194
CoatFirstOrderMaximumFeature	151.220	147.880	146.410
CoatFirstOrderMeanAbsoluteDeviation	20.820	18.690	22.900
CoatFirstOrderMeanFeature	24.150	21.180	28.880
CoatFirstOrderMedianFeature	15.260	13.450	17.520
CoatFirstOrderMinimumFeature	204.250	199.160	202.150
CoatFirstOrderRangeFeatureV	150.450	147.340	145.330
CoatFirstOrder RobustMeanAbsoluteDeviation	502.160	396.940	576.670
CoatFirstOrderStandardDeviationFeature	0.406	0.440	0.351
CoatFirstOrderTotalEnergyFeature	32.400	37.390	28.680

Radiomics feature	P-value (Wilcoxon Test)
Radiomics feature	0 vs 14	14 vs 28	0 vs 28
CoatGLCMAutocorrelation	0.010	0.005	0.882
CoatGLCMContrast	0.962	0.001	0.000
CoatGLCMEntropy	0.820	0.000	0.000
CoatGLCMIdn	0.176	0.048	0.001
CoatGLCMClusterProminence	0.000	0.000	0.000
CoatGLCMDissimilarity	0.000	0.000	0.401
CoatGLCMJointEnergy	0.864	0.000	0.000
CoatGLRMGrayLevelNonUniformity	0.000	0.000	0.000
CoatGLRMLongRunEmphasis	0.644	0.000	0.000
CoatGLRMLowGrayLevelRun	0.029	0.009	0.258
CoatGLRMShortRunEmphasis	0.727	0.002	0.001
CoatGLSZMSmallAreaEmphasis	0.109	0.000	0.000
CoatGLSZMSmallAreaLowGray	0.038	0.241	0.678
CoatGLSZMZonePercent	0.410	0.003	0.000
CoatNGTDMBusyness	0.000	0.000	0.000
CoatNGTDMCoarseness	0.013	0.000	0.000
CoatNGTDMComplexity	0.007	0.000	0.005
CoatNGTDMContrast	0.084	0.000	0.000
CoatNGTDMStrength	0.000	0.000	0.000
CoatFirstOrder10Percentile	0.133	0.060	0.003
CoatFirstOrderEnergyFeature	0.000	0.000	0.000
CoatFirstOrderInterquartileRange	0.000	0.000	0.000
CoatFirstOrderMaximumFeature	0.081	0.283	0.013
CoatFirstOrderMeanAbsoluteDeviation	0.000	0.000	0.000
CoatFirstOrderMeanFeature	0.000	0.000	0.000
CoatFirstOrderMedianFeature	0.000	0.000	0.000
CoatFirstOrderMinimumFeature	0.000	0.030	0.063
CoatFirstOrderRangeFeatureV	0.084	0.146	0.004
CoatFirstOrder RobustMeanAbsoluteDeviation	0.000	0.000	0.000
CoatFirstOrderStandardDeviationFeature	0.000	0.000	0.000
CoatFirstOrderTotalEnergyFeature	0.400	0.000	0.007

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