Surface temperature characteristics of patients with malignant lymphoma based on infrared thermal imaging technology

Surface temperature characteristics of patients with malignant lymphoma based on infrared thermal imaging technology | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Surface temperature characteristics of patients with malignant lymphoma based on infrared thermal imaging technology Junfan Wu, Qiuran Jia, dongyun li, Wenzheng Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4517867/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective: To evaluate the difference in body surface temperature characteristics between patients with malignant lymphoma and healthy people by infrared thermography, and to find the independent risk factors for malignant lymphoma in temperature characteristics. Methods: The infrared heat maps of 40 patients with malignant lymphoma and 40 healthy people who were admitted to Dongzhimen Hospital from December 2022 to December 2023 were collected, and the temperature characteristics of the target area were measured and analyzed. Results: The average temperature of hands and feet in the malignant lymphoma group was higher than that in the normal control group. The average temperature of the abdomen, spine and back of the control group was lower than that of the normal control group (P < 0.05). The homogeneity of the neck and abdomen was worse than that of the normal control group (P < 0.05). The symmetry of the anterior, neck and clavicle region was worse than that of the normal control group (P < 0.05). Lower abdominal homogeneity and neck symmetry were independent risk factors for the diagnosis of malignant lymphoma (P<0.05). Conclusion: Infrared thermal imaging is beneficial to the screening of malignant lymphoma and the evaluation of therapeutic effect. Biological sciences/Biological techniques/Imaging Biological sciences/Cancer/Haematological cancer Biological sciences/Cancer/Haematological cancer/Lymphoma Malignant lymphoma Infrared thermal imaging technology Body surface temperature Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Introduction The incidence and disease burden of malignant lymphoma in China are increasing year by year. A study in 2022 [1] showed that the disability-adjusted life year number increased by 100.9% for non-Hodgkin lymphoma over the past 30 years. At the same time, some data [2] showed that the average annual medical cost of malignant lymphoma patients in China was 177,601.74 yuan, which was about 2.53 times of the median annual household income, and the expenditure was not only used for detection and diagnosis [3] , post-diagnosis treatment [4] , but also regular prognosis monitoring [5] . However, chemotherapy-based treatment mode is the first choice for most malignant aggressive lymphomas [6, 7] . Therefore, exploring an accurate, simple and inexpensive monitoring method for patients with malignant lymphoma after chemotherapy can greatly reduce the burden of disease and the economic pressure of patients. At the same time, in the course of chemotherapy treatment, patients usually have changes in their condition [8, 9] , or different degrees of chemotherapy toxicity [10] . The application of infrared thermal imaging technology can be observed and found in time during the treatment, which provides a powerful reference for clinical treatment. Infrared thermal imaging technology is a non-invasive detection technology [11] . It measures the medium- to long-wave infrared radiation emitted by the human body and displays temperature images through a pseudo-color performance mode. Body temperature reflects the external manifestation of physiological and pathological metabolism, which is a process of energy expression. Therefore, measuring body temperature is meaningful for differentiating clinical physiology from pathology. Infrared thermal imaging technology is accurate, simple, and inexpensive. With the progress of science and technology, the detection precision of infrared thermal imaging technology has significantly improved. It has been used in the detection and research of diseases affecting superficial parts such as breast [12] , thyroid [13] , surface muscle [14] , joint [15] , and other superficial parts. Therefore, for most lymphomas distributed on the body surface, infrared thermal imaging is a suitable detection method. During the progression of malignant lymphoma, the lymphoma site of the lesion often experiences changes in thermal metabolism [16, 17] . Therefore, the authors aim to summarize the clinical statistical value by comparing the differences in thermal metabolism characteristics of superficial lymph nodes between malignant lymphoma patients and healthy individuals. This study aims to demonstrate the difference in lymph node metabolism between malignant lymphoma patients and healthy individuals using infrared thermal imaging technology. By monitoring the lymph node status of patients with malignant lymphoma after chemotherapy, the prognosis and recurrence of patients can be tracked. This study adheres to ethical standards formulated by the Ethics Committee of Dongzhimen Hospital, Beijing University of Chinese Medicine. It follows principles of confidentiality and voluntary participation and has been approved by the Ethics committee (ethical approval number: 2023DZMEC-335-01). Information and Methods 1 Sample source: a total of 40 patients with malignant lymphoma were collected from the headquarters and International Department of Hematology and Oncology of Dongzhimen Hospital from December 2022 to December 2023. Forty healthy people who visited the physical examination center of the International Department of Dongzhimen Hospital were selected. 2 Diagnostic criteria: According to the diagnostic criteria of Chinese Society of Clinical Oncology (CSCO) diagnosis and treatment guidelines for malignant lymphoma 2021 [18] : The diagnosis was confirmed by pathology after biopsy, and the location of the lesion was determined by pet/ct or pathology. 3. Inclusion criteria 3.1 The cases included in the standard: ① The patient met the diagnostic criteria for malignant lymphoma; ② Normal body temperature, with axillary temperature ranging between 36.3°C and 37.2°C; ③ patients who can stand on their own and do not need help or other things to stand; ④ The patients were male or female, aged more than 18 years old or less than 80 years old. ⑤ voluntarily agree to participate in the survey, sign the informed consent form, and answer the survey questions truthfully; ⑥ Patients with good compliance. 3.2 Inclusion criteria for control group: ① Non-malignant lymphoma population who underwent physical examination in our hospital during the same period; ② No abnormal lymph nodes detected by imaging examination; ③ no thyroid and other diseases adjacent to lymph node distribution; ④ The body temperature was normal, and the axillary body temperature was between 36.3℃ and 37.2℃; ⑤ Patients were male and female, aged more than 18 years old and less than 80 years old. 4 Exclusion criteria: ①combined with serious primary diseases such as cardiovascular and cerebrovascular diseases, liver and renal insufficiency; ② pregnant and lactating women; ③ patients with endocrine diseases such as thyroid dysfunction; ④ patients with physical stimulation (cupping, scraping, moxibustion, plaster application, etc.) before infrared thermal imaging detection; ⑤ patients with large area of skin damage, scar and infection; ⑥ unable to cooperate, such as complicated with neurological or mental disorders, or unwilling to cooperate; ⑦ In infrared thermography analysis, it was found that excessively large or sagging breasts can affect the measurement of the anterior chest temperature, and a large beer belly can affect the measurement of the lower abdominal temperature. 5 Observation indicators and methods 5.1 Observation and Documentation of Infrared Thermography Characteristics : The HIR-2000A infrared thermograph, produced by Beijing Yuetian Optoelectronic Technology Co., Ltd., was utilized in this study. A single operator, who had undergone specialized training in the use of the infrared thermograph, conducted all measurements. Subjects were positioned within the infrared thermograph, maintaining an ambient temperature of 20–24°C and a humidity level of 40–60%. The imaging environment was controlled to eliminate interference from wind, strong light, and other potential disturbances, with heating and cooling sources kept at a distance. Subjects were instructed to avoid consuming large quantities of water and to refrain from eating spicy or cold foods within three hours prior to imaging. They were fully exposed, free of perspiration, with their hair tied up and body relaxed [19] . Imaging was performed after subjects had removed their clothing and rested quietly for 15 minutes [20、21、23] . Infrared thermographic images were acquired following the standard operating procedures of the infrared thermograph. Anatomical posture images of the subjects were collected, including: one image of the upper front torso, one of the lower front torso, one of the upper back torso, one of the lower back torso, one of the axillae in an anterior position, one of the right neck in a supine position, and one of the left neck in a supine position (as illustrated in Fig. 1). A database was created using Microsoft Excel to systematically input patient data, including average infrared temperature readings (neck, anterior chest, abdomen, lower abdomen, spine, lower back, palms of both hands, and dorsum of both feet), uniformity values (neck, anterior chest, abdomen, lower abdomen, back), and symmetry values (anterior torso, left and right neck, left and right subclavian regions, left and right inguinal regions). Data analysis for the average temperature, uniformity, and symmetry of each region in the infrared thermographic images was conducted using an image recognition and processing software developed by Professor Wenxue Hong's team at the Institute of Biomedical Engineering, Yanshan University. This software computed the uniformity level, average temperature, and symmetry level of the target regions in the infrared thermographic images. Higher uniformity values indicated better uniformity, whereas lower values indicated poorer uniformity. Symmetry values closer to 1 represented higher symmetry, while values approaching 0 indicated lower symmetry. 5.2 Statistical methods: The statistical analysis of the study data was performed using SPSS version 25.0, with a research data database established in Microsoft Excel. Differences in categorical data were compared using the chi-square test. For the comparison of continuous data between groups, normality tests and homogeneity of variance tests were conducted first. Based on the test results, the independent samples t-test was employed if P < 0.05, indicating statistical significance. If the data did not follow a normal distribution or the variances were not equal, the Wilcoxon Mann-Whitney U test was used instead. Normally distributed continuous data were expressed as mean ± standard deviation (x ± s), while non-normally distributed or heterogeneous variance data were expressed as median (P25, P75). GraphPad Prism software was used to create violin scatter plots to describe the differences between the malignant lymphoma group and the normal control group, aiming to identify infrared thermography characteristics of malignant lymphoma patients. Based on these findings, multivariate binary logistic regression analysis was applied to distinguish independent risk factors between malignant lymphoma patients and healthy individuals. The receiver operating characteristic (ROC) curve and Youden index were utilized to determine the cut-off value, evaluating the diagnostic performance of the indicators. The optimal Youden index was calculated as the sum of the maximum sensitivity and the maximum specificity minus one, and the corresponding infrared thermography indicator level at this Youden index was taken as the cut-off value. Results 1. General Information 1.1 Gender Statistics A total of 40 patients were included in the malignant lymphoma group, with 18 males accounting for 45% and 22 females accounting for 55%. In the normal control group of 40 individuals, there were 15 males, representing 37.5%, and 25 females, representing 62.5%. The chi-square test yielded a P-value greater than 0.05, indicating that there was no statistically significant difference in gender composition between the malignant lymphoma group and the normal control group. Figure 2 . 1.2 Age statistics chart comparing the gender composition of two population groups Among the 40 malignant lymphoma patients included in the study, the oldest was 77 years old, the youngest was 18 years old, and the average age was 50.15 ± 15.57 years. In the healthy individuals of the normal control group, the oldest was 66 years old, the youngest was 26 years old, and the average age was 45.13 ± 8.67 years. The age distribution data of both groups passed the normality test (P > 0.05), indicating a normal distribution. Furthermore, the chi-square test showed no statistically significant difference in age between the two groups (P > 0.05). Detailed age distributions for both groups can be found in Figs. 3 and 4 . 1.3 Course statistics of malignant lymphoma patients Among the 40 patients with malignant lymphoma, the longest course of disease was 164 months, the shortest was 1 month, the normal distribution test P < 0.05, did not meet the normal distribution, the average course of disease was 33.25 ± 34.50 months, the median was 22(9.00,39.75) months. The specific disease course distribution is shown in Fig. 5 . 1.4 Classification statistics of malignant lymphoma patients The patients in the malignant lymphoma group were divided into non-Hodgkin's lymphoma and Hodgkin's lymphoma according to the World Health Organization (WTO) lymphoma classification criteria. Among them, 37 cases (92.5%) were non-Hodgkin's lymphoma, 33 cases (82.5%) were B-cell lymphoma, 3 cases (7.5%) were T-cell lymphoma, and 1 case was NKT cell lymphoma. There were 3 cases of Hodgkin's lymphoma (7.5%). The specific distribution is shown in Fig. 6 . 2 Infrared thermal imaging characteristics of patients with malignant lymphoma There was no significant difference in gender and age between the normal control group and the malignant lymphoma group (P > 0.05). See Table 1 for details. Table 1 Sex and age of normal control group and normal control group Group Number of cases(n) Age (years) Male: Female Normal control group 40 45.13 ± 8.67 1:1.67 Malignant lymphoma group 40 50.15 ± 15.57 1:1.22 2.1 Difference of average temperature in target area between malignant lymphoma patients and normal controls group The average temperature of infrared thermal imaging in the malignant lymphoma group was higher than that in the normal control group in 5 measured target areas (neck, left hand heart, right hand heart, left foot, right foot), and lower than that in the normal control group in 5 measured target areas (anterior chest, abdomen, lower abdomen, spine, back waist). There was no significant difference in three target areas (neck, anterior chest and abdomen) (P > 0.05). Results The average surface temperature in the four target areas (left hand heart, right hand heart, left foot and right foot) of malignant lymphoma group was significantly higher than that of the normal control group (P < 0.05), that is, the average temperature of the left hand heart, right hand heart, left foot and right foot of malignant lymphoma group was higher than that of the normal control group. The mean temperature in the malignant lymphoma group was significantly lower than that in the normal control group in the three target areas (abdomen, middle spine and back waist) (P < 0.05). That is, the average temperature of the abdomen, middle spine and back of the malignant lymphoma group was lower than that of the normal control group. See Fig. 7 , Fig. 8 , Fig. 9 and Table 2 for details. Table 2 Average temperature levels in target areas of normal control group and malignant lymphoma group Target area Normal control group (N = 40) Malignant lymphoma group (N = 40) P-value Mean neck temperature 33.07(32.76,33.39) 33.57(32.67,33.84) 0.137 Mean chest temperature 32.5 ± 1.01 32.3 ± 1.17 0.468 Mean abdominal temperature 32.99(32.16,33.80)** 32.09(31.53,33.09)** 0.005 Mean temperature of the lower abdomen 32.46(31.49,33.19) 32.25(31.70,33.13) 0.7733 Mean spine temperature 33.18(32.65,34.12)** 32.17(31.66,33.22)** 0.001 Average temperature of the back waist 32.76(32.12,33.42)** 32.0(31.26,32.62)** 0.001 Mean heart temperature of the left hand 31.18(30.45,31.91)** 32.52(31.80,33.42)** < 0.001 Mean heart temperature of the right hand 31.41(30.60,32.35)** 32.81(31.87,33.55)** < 0.001 Mean temperature of the left foot 29.4 ± 1.82** 31.6 ± 1.72** < 0.001 Mean temperature of the right foot 29.65(28.11,31.21)** 32.07(30.75,32.78)** < 0.001 2 Differences in homogeneity of target region between malignant lymphoma patients and normal controls group The homogeneity of infrared thermography in the malignant lymphoma group was lower than that in the normal control group in 4 target regions (neck, anterior chest, abdomen, lower abdomen), and higher than that in 1 target region (posterior back). There was no significant difference in 3 target regions (anterior chest, abdomen, posterior back) (P > 0.05). Results The surface homogeneity of the malignant lymphoma group was significantly lower than that of the normal control group in the two target areas (neck and lower abdomen) (P < 0.05). See Table 3 and Fig. 10 for details. Table 3 Homogeneity level of target area in normal control group, malignant lymphoma group Target area Normal control group (N = 40) Malignant lymphoma group (N = 40) P-value Homogeneity of neck 4.09(3.74,5.25)** 3.52(2.54,4.43)** 0.006 Homogeneity of chest 5.56 ± 1.95 5.18 ± 1.83 0.381 Homogeneity of abdomen 5.02(3.20,6.68) 4.20(3.11,6.10) 0.422 Homogeneity of the lower abdomen 5.32 ± 4.97* 3.68 ± 1.31* 0.015 Homogeneity of the back 5.64(4.48,8.37) 6.48(4.90,7.47) 0.683 2.3 Differences in the symmetry of the target region between malignant lymphoma patients and normal controls group The symmetry of infrared thermography in the malignant lymphoma group was lower than that in the normal control group in four target regions (anterior, neck, clavicle region and inguinal region), and there was no significant difference in one target region (inguinal region) (P > 0.05). Results The surface symmetry of the malignant lymphoma group was significantly worse than that of the normal control group in the three target regions (anterior, neck and clavicle region) (P < 0.05). See Table 4 and Fig. 11 for details. Table 4 Symmetry level of target region in normal control group, malignant lymphoma group Target area Normal control group (N = 40) Malignant lymphoma group (N = 40) P-value Symmetry of the front body 0.68 ± 0.06** 0.63 ± 0.06** < 0.001 Symmetry of the neck 0.77 ± 0.07** 0.69 ± 0.10** < 0.001 Symmetry of the clavicle 0.74 ± 0.067* 0.70 ± 0.09* 0.015 Symmetry of the groin 0.82(0.75,0.85) 0.78(0.75,0.83) 0.206 2.4 Infrared thermal imaging features of precursors of patients with malignant lymphoma Non-parametric test was used to analyze the difference between the mean temperature of the abdomen and the mean temperature of the chest and the small abdomen in the normal control group. The results showed that there was no significant difference between the mean temperature of the abdomen and the mean temperature of the chest and the small abdomen (P > 0.05). Non-parametric test was used to analyze the difference between the mean temperature of the abdomen and the mean temperature of the chest and the small abdomen in the malignant lymphoma group. The results showed that there was no significant difference between the mean temperature of the abdomen and the mean temperature of the chest and the small abdomen (P > 0.05). See Tables 5 and 6 for details. Table 5 Temperature difference between abdomen and chest by infrared thermal imaging Group Mean abdominal temperature Average temperature of the anterior chest P-value Normal control group 32.49(31.66,33.34) 32.60(31.68,33.31) 0.802 Malignant lymphoma group 32.09(31.53,33.09) 32.36(31.75,33.35) 0.468 Table 6 Infrared thermal imaging abdominal and small abdominal temperature differences Group Mean abdominal temperature Average temperature of the anterior chest P-value Normal control group 32.49(31.66,33.34) 32.46(31.49,33.19) 0.612 Malignant lymphoma group 32.09(31.53,33.09) 32.25(31.71,33.13) 0.416 3 Risk factor analysis used to distinguish patients with malignant lymphoma from healthy individuals The indexes with P value < 0.05 at baseline were included in binary multivariate Logistic regression. After excluding collinearity, the indexes included abdominal average temperature, spinal average temperature, posterior lumbar average temperature, left hand heart average temperature, right hand heart average temperature, left foot average temperature, right foot average temperature, neck uniformity, small abdomen uniformity, anterior symmetry, neck symmetry, and clavicle symmetry. The results showed that the homogeneity of lower abdomen (1.795, 1.170–2.753) and the symmetry of neck were independent risk factors for the diagnosis of malignant lymphoma (P < 0.05). See Table 7 . The ROC curve of the uniformity of the abdomen and the symmetry of the neck showed that the AUC of the uniformity of the abdomen in the diagnosis of malignant lymphoma was 0.600, and the cut-off value was 6.38. The AUC of neck symmetry in the diagnosis of malignant lymphoma was 0.764, the sensitivity was 0.725, the specificity was 0.275, and the cut-off value was 0.745. See Tables 7 , Figs. 12 and 13 for details. Table 7 Multivariate logistic regression analysis of independent risk factors for malignant lymphoma Characteristic of temperature OR 95%CI P-value Mean abdominal temperature 0.586 0.169–2.003 0.400 Mean spine temperature 2.826 0.412–19.368 0.290 Average temperature of the back waist 1.105 0.155–7.879 0.920 Mean heart temperature of the left hand 0.403 0.069–2.343 0.312 Mean heart temperature of the right hand 1.789 0.346–9.254 0.488 Mean temperature of the left foot 0.644 0.189–2.199 0.482 Mean temperature of the right foot 1.007 0.312–3.247 0.991 Homogeneity of neck 0.761 0.413–1.403 0.381 Homogeneity of the lower abdomen 1.795** 1.170–2.753** 0.007 Symmetry of the front body - - 0.301 Symmetry of the neck - - 0.048 Symmetry of the clavicle - - 0.774 Discussion Changes in energy metabolism and temperature are accompanied by every physiological and pathological process of the human body, and every physiological and pathological process is accompanied by changes in the structure and function of the body system. Functional changes often precede structural changes [24,25] , and observing changes in body temperature is a way for us to monitor the body function [26] . This study finally found that based on infrared thermal imaging technology, small abdominal homogeneity and neck symmetry are independent risk factors to distinguish patients with malignant lymphoma from healthy people. The cervical lymph nodes included submandibular lymph nodes, superficial lateral lymph nodes, deep lateral lymph nodes, and supraclavicular lymph nodes. lymph nodes in the lower abdomen mainly radiate from inguinal lymph nodes, including external iliac lymph nodes, internal iliac lymph nodes, and common iliac lymph nodes. When lymph nodes are invaded by cancer cells, their proliferation and differentiation will change. v-myc avian myelocytomatosis viral oncogene homolog (MYC) is a gene involved in cell proliferation, and its dysregulation can be found in malignant lymphomas [27] . The cellular environment of malignant lymphoma is conducive to promoting glucose uptake by tumor cells [28] . In the case of hypoxia, anaerobic glycolysis will be stimulated, but some cancer cells can absorb nutrients and oxygen from the blood supply. Therefore, aerobic glycolysis will also be increased, leading to increased local metabolism, increased heat production and increased temperature. Theoretically, all lymph nodes or lymphatic vessels invaded by cancer cells will show hypermetabolism. However, because infrared thermal imaging detects the temperature of the body surface and adjacent surface tissues, it is not sensitive to the temperature of deep layers and is sensitive to the abnormal metabolism of superficial lymph nodes. At the same time, the neck and abdomen are the superficial locations of lymph nodes. Therefore, the abnormal temperature metabolism of the neck and lower abdomen is relatively clear, but it also indicates that the surface temperature change of malignant lymphoma has its own characteristics. We can screen malignant lymphoma by detecting the uniformity of the temperature of the lower abdomen and the symmetry of the temperature of the neck. However, further studies are needed to confirm the underlying mechanisms. Declarations AUTHORA CONTRIBUTIONS Study desigen: Wu Junfan, Jia Qiuran, Li Dongyun, Zhang Wenzheng; Study Investigator: Wu Junfan, Jia Qiuran; Contributed patients or study materials: Wu Junfan, Jia Qiuran; Collection and assembly of data: Wu Junfan; Analysed the data: Wu Junfan, Jia Qiuran; Data interpretation: Wu Junfan, Jia Qiuran; Critical review and revision of this manuscript and approval of the manuscript for submission: All authors. AFFILIATIONS 1 Dongzhimen Hospital, Beijing University of Traditional Chinese Medicine, Beijing, CHINA; 2 Beijing Haidian Hospital, Beijing, CHINA; 3 Health Management Center, International Department, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, CHINA ACNOWLEDGEMENTS Study desigen: Wu Junfan, Jia Qiuran, Li Dongyun, Zhang Wenzheng; Study Investigator: Wu Junfan, Jia Qiuran; Contributed patients or study materials: Wu Junfan, Jia Qiuran; Collection and assembly of data: Wu Junfan; Analysed the data: Wu Junfan, Jia Qiuran; Data interpretation: Wu Junfan, Jia Qiuran; Critical review and revision of this manuscript and approval of the manuscript for submission: All authors. FUNDING INFORMATION Graduate research project of Beijing University of Chinese Medicine (90011461220441) : Evaluation of lymph node function in patients with malignant lymphoma based on infrared thermal imaging technology CONFLICT OF INTERECT STATEMENT None of the authors has a relevant conflict of interest. DATA AVAILABILITY STATEMENT N/A ETHICS STATEMENT This study followed the ethical standards formulated by the Ethics Committee of Dongzhimen Hospital, Beijing University of Chinese Medicine, followed the principles of confidentiality and voluntary, and has been approved by the Ethics committee (ethical approval number: 2023DZMEC-335-01). PATIENT CONSENT STATEMENT The study was performed according to the Helsinki Declaration. Patient provided written informed consent. CONTACT PERSON Anyone have related questions about this study, or want to get the original data can directly link to the first author: 20210931428 @bucm.edu.cn ORCID Junfan Wu: https://orcid.org/0009-0006-2856-3771 Qiuran Jia: https://orcid.org/0009-0005-9733-8511 Wenzheng Zhang: https://orcid.org/0000-0001-6188-7977 References Liu W, Liu J, Song Y,et al.Burden of lymphoma in China, 1990-2019: an analysis of the global burden of diseases, injuries, and risk factors study 2019. Aging (Albany NY). 2022 Apr 10;14(7):3175-3190. Wang Fangxu, Tao Libo, Dong Dong et al. Disease burden of patients with Hodgkin lymphoma in China: a study based on an online questionnaire survey [J]. China Medical Insurance,2020,No.136(01):60-64. (in Chinese) Phillips EH, Iype R, Wirth A. PET-guided treatment for personalised therapy of Hodgkin lymphoma and aggressive non-Hodgkin lymphoma. Br J Radiol. 2021 Nov 1;94(1127):20210576. Wolfson JA, Bhatia S, Ginsberg JP,et al. 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Research methodology for in vivo measurements of resting energy expenditure, daily body temperature, metabolic heat and non-viral tissue-specific gene therapy in baboons. Res Vet Sci. 2020 Dec;133:136-145. Korać, P, Dotlić S, Matulić M, et al. Role of MYC in B Cell Lymphomagenesis. Genes 2017 , 8 , 115. Kirsch BJ, Chang SJ, Betenbaugh MJ,et al. Non-Hodgkin Lymphoma Metabolism. Adv Exp Med Biol. 2021;1311:103-116. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4517867","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":319167067,"identity":"7a82d193-5ea6-4614-b8b4-4543e9efb50b","order_by":0,"name":"Junfan Wu","email":"","orcid":"","institution":"Beijing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Junfan","middleName":"","lastName":"Wu","suffix":""},{"id":319167068,"identity":"44263a49-ef7e-4d50-a723-b75d18f5c56e","order_by":1,"name":"Qiuran Jia","email":"","orcid":"","institution":"Beijing Haidian Hospital","correspondingAuthor":false,"prefix":"","firstName":"Qiuran","middleName":"","lastName":"Jia","suffix":""},{"id":319167069,"identity":"6a96a479-0bbb-43a0-89af-4140f252bfbf","order_by":2,"name":"dongyun li","email":"","orcid":"","institution":"Dongzhimen Hospital Affiliated to Beijing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"dongyun","middleName":"","lastName":"li","suffix":""},{"id":319167070,"identity":"862494ee-3fea-40a4-ad91-40ff82960ce9","order_by":3,"name":"Wenzheng Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuklEQVRIie3QIQ7CMBTG8TZNOtOhS0roFbZwAY7yFsTU/OQWks7sAJ3hGATZKUxvgJmaZm6CBLAIsodD9Ke/v3iPkCD4T9E8PZ5bHjUO36y7iu1WwgM+UXHFspPcJ7i5tvmQdheeG0mAzOV5OaEWACYvCqNqR1t/W06YAtdbLguzccCoQSRcZfVR8CTnEhJcItSBsdgA4BOpR06td6l5P7lH3aLb60jupdO6afphLhHJB/fjPgiCIPjmBcjzOngjIW2VAAAAAElFTkSuQmCC","orcid":"","institution":"Beijing Traditional Chinese Medicine visualization joint Innovation Center","correspondingAuthor":true,"prefix":"","firstName":"Wenzheng","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2024-06-02 17:10:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4517867/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4517867/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":59607376,"identity":"33b8586e-fc7b-40cd-bb6e-abc2012d8d79","added_by":"auto","created_at":"2024-07-03 19:05:41","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":56312,"visible":true,"origin":"","legend":"\u003cp\u003eThe target area of the picture\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4517867/v1/0e02c2f77917ea3fc458fb6a.jpg"},{"id":59607375,"identity":"1aa0a477-43b8-4788-9d64-1e14d5901bc2","added_by":"auto","created_at":"2024-07-03 19:05:41","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":15300,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4517867/v1/995485edac6b525da9209ce1.jpg"},{"id":59608698,"identity":"f8e6b8d4-f365-4cc3-beb6-1ebc3848edc9","added_by":"auto","created_at":"2024-07-03 19:13:41","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":21593,"visible":true,"origin":"","legend":"\u003cp\u003eAge distribution of malignant lymphoma group\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4517867/v1/9de6fc7baf0ecc530f853b55.jpg"},{"id":59607382,"identity":"42c335f2-3432-4228-b1a8-05efab4c881b","added_by":"auto","created_at":"2024-07-03 19:05:41","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":22096,"visible":true,"origin":"","legend":"\u003cp\u003eage distribution of normal control group\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4517867/v1/298b66167631ea839845afbd.jpg"},{"id":59607385,"identity":"3d7bf33d-c7e4-4b79-979e-f9c0ce912889","added_by":"auto","created_at":"2024-07-03 19:05:41","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":17633,"visible":true,"origin":"","legend":"\u003cp\u003eCourse distribution of malignant lymphoma group\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4517867/v1/11b372a6b3283c7621dd5078.jpg"},{"id":59607383,"identity":"1da5550f-e870-4b10-a7ae-5e74860d91e8","added_by":"auto","created_at":"2024-07-03 19:05:41","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":30195,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of malignant lymphoma components\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4517867/v1/0d72f5ae8cc197263a3b3e66.jpg"},{"id":59608699,"identity":"90fc1864-a337-4b94-a9fa-1cdf24311b8c","added_by":"auto","created_at":"2024-07-03 19:13:41","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":73907,"visible":true,"origin":"","legend":"\u003cp\u003eInfrared thermography of normal subjects\u003c/p\u003e","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4517867/v1/56a7584ef5160c2bfffc473c.jpg"},{"id":59607387,"identity":"7666a83c-89d2-48ad-b9a1-43eee94f8bc8","added_by":"auto","created_at":"2024-07-03 19:05:42","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":61265,"visible":true,"origin":"","legend":"\u003cp\u003eInfrared thermography of patients with malignant lymphoma\u003c/p\u003e","description":"","filename":"8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4517867/v1/dae1ddde226593737cf60a28.jpg"},{"id":59607380,"identity":"e6edd948-783a-4551-b5a7-833817e248bb","added_by":"auto","created_at":"2024-07-03 19:05:41","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":63035,"visible":true,"origin":"","legend":"\u003cp\u003eViolin plot of temperature difference between malignant lymphoma group and normal control group\u003c/p\u003e","description":"","filename":"9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4517867/v1/d68b762763925c5aba2397d1.jpg"},{"id":59608702,"identity":"8653593f-d380-4f06-bdbd-f1339c5d1f6c","added_by":"auto","created_at":"2024-07-03 19:13:42","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":25121,"visible":true,"origin":"","legend":"\u003cp\u003eViolin plot of homogeneity difference between malignant lymphoma group and normal control group\u003c/p\u003e","description":"","filename":"10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4517867/v1/ee5224fe473efdedfc750ff1.jpg"},{"id":59608701,"identity":"5bb650fc-d803-4724-a47d-65d2e99190c5","added_by":"auto","created_at":"2024-07-03 19:13:41","extension":"jpg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":31502,"visible":true,"origin":"","legend":"\u003cp\u003eViolin plot of symmetry difference between malignant lymphoma group and normal control group\u003c/p\u003e","description":"","filename":"11.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4517867/v1/8fdc44175fca63479a1bc24b.jpg"},{"id":59607388,"identity":"e99f2c9c-89aa-4489-8515-e289c3b97aa7","added_by":"auto","created_at":"2024-07-03 19:05:42","extension":"jpg","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":26863,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve for the diagnosis of malignant lymphoma by neck symmetry\u003c/p\u003e","description":"","filename":"12.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4517867/v1/bacb8e3c39ff7011d93560c2.jpg"},{"id":59607379,"identity":"73bf9d42-97c8-449d-9219-cdd903b314e8","added_by":"auto","created_at":"2024-07-03 19:05:41","extension":"jpg","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":25586,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve for the diagnosis of malignant lymphoma by homogeneity of the lower abdomen\u003c/p\u003e","description":"","filename":"13.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4517867/v1/885c8f448f982eb4f8bdf857.jpg"},{"id":79761050,"identity":"e7080304-6dfb-487a-afd0-a09d4eee4bfa","added_by":"auto","created_at":"2025-04-02 11:17:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1171829,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4517867/v1/5e6b87f4-ee8b-479f-b29f-4bc054658ebe.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Surface temperature characteristics of patients with malignant lymphoma based on infrared thermal imaging technology","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe incidence and disease burden of malignant lymphoma in China are increasing year by year. A study in 2022 \u003csup\u003e[1]\u003c/sup\u003e showed that the disability-adjusted life year number increased by 100.9% for non-Hodgkin lymphoma over the past 30 years. At the same time, some data \u003csup\u003e[2]\u003c/sup\u003e showed that the average annual medical cost of malignant lymphoma patients in China was 177,601.74 yuan, which was about 2.53 times of the median annual household income, and the expenditure was not only used for detection and diagnosis \u003csup\u003e[3]\u003c/sup\u003e, post-diagnosis treatment \u003csup\u003e[4]\u003c/sup\u003e, but also regular prognosis monitoring \u003csup\u003e[5]\u003c/sup\u003e. However, chemotherapy-based treatment mode is the first choice for most malignant aggressive lymphomas \u003csup\u003e[6, 7]\u003c/sup\u003e. Therefore, exploring an accurate, simple and inexpensive monitoring method for patients with malignant lymphoma after chemotherapy can greatly reduce the burden of disease and the economic pressure of patients. At the same time, in the course of chemotherapy treatment, patients usually have changes in their condition \u003csup\u003e[8, 9]\u003c/sup\u003e, or different degrees of chemotherapy toxicity \u003csup\u003e[10]\u003c/sup\u003e. The application of infrared thermal imaging technology can be observed and found in time during the treatment, which provides a powerful reference for clinical treatment.\u003c/p\u003e \u003cp\u003eInfrared thermal imaging technology is a non-invasive detection technology \u003csup\u003e[11]\u003c/sup\u003e. It measures the medium- to long-wave infrared radiation emitted by the human body and displays temperature images through a pseudo-color performance mode. Body temperature reflects the external manifestation of physiological and pathological metabolism, which is a process of energy expression. Therefore, measuring body temperature is meaningful for differentiating clinical physiology from pathology. Infrared thermal imaging technology is accurate, simple, and inexpensive. With the progress of science and technology, the detection precision of infrared thermal imaging technology has significantly improved. It has been used in the detection and research of diseases affecting superficial parts such as breast \u003csup\u003e[12]\u003c/sup\u003e, thyroid \u003csup\u003e[13]\u003c/sup\u003e, surface muscle \u003csup\u003e[14]\u003c/sup\u003e, joint \u003csup\u003e[15]\u003c/sup\u003e, and other superficial parts. Therefore, for most lymphomas distributed on the body surface, infrared thermal imaging is a suitable detection method. During the progression of malignant lymphoma, the lymphoma site of the lesion often experiences changes in thermal metabolism \u003csup\u003e[16, 17]\u003c/sup\u003e. Therefore, the authors aim to summarize the clinical statistical value by comparing the differences in thermal metabolism characteristics of superficial lymph nodes between malignant lymphoma patients and healthy individuals. This study aims to demonstrate the difference in lymph node metabolism between malignant lymphoma patients and healthy individuals using infrared thermal imaging technology. By monitoring the lymph node status of patients with malignant lymphoma after chemotherapy, the prognosis and recurrence of patients can be tracked. This study adheres to ethical standards formulated by the Ethics Committee of Dongzhimen Hospital, Beijing University of Chinese Medicine. It follows principles of confidentiality and voluntary participation and has been approved by the Ethics committee (ethical approval number: 2023DZMEC-335-01).\u003c/p\u003e"},{"header":"Information and Methods","content":"\u003cp\u003e1 Sample source: a total of 40 patients with malignant lymphoma were collected from the headquarters and International Department of Hematology and Oncology of Dongzhimen Hospital from December 2022 to December 2023. Forty healthy people who visited the physical examination center of the International Department of Dongzhimen Hospital were selected.\u003c/p\u003e\n\u003cp\u003e2 Diagnostic criteria: According to the diagnostic criteria of Chinese Society of Clinical Oncology (CSCO) diagnosis and treatment guidelines for malignant lymphoma 2021 [18] : The diagnosis was confirmed by pathology after biopsy, and the location of the lesion was determined by pet/ct or pathology.\u003c/p\u003e\n\u003cp\u003e3. Inclusion criteria\u003c/p\u003e\n\u003cp\u003e3.1 The cases included in the standard: ① The patient met the diagnostic criteria for malignant lymphoma; ② Normal body temperature, with axillary temperature ranging between 36.3\u0026deg;C and 37.2\u0026deg;C; ③ patients who can stand on their own and do not need help or other things to stand; ④ The patients were male or female, aged more than 18 years old or less than 80 years old. ⑤ voluntarily agree to participate in the survey, sign the informed consent form, and answer the survey questions truthfully; ⑥ Patients with good compliance.\u003c/p\u003e\n\u003cp\u003e3.2 Inclusion criteria for control group: ① Non-malignant lymphoma population who underwent physical examination in our hospital during the same period; ② No abnormal lymph nodes detected by imaging examination; ③ no thyroid and other diseases adjacent to lymph node distribution; ④ The body temperature was normal, and the axillary body temperature was between 36.3℃ and 37.2℃; ⑤ Patients were male and female, aged more than 18 years old and less than 80 years old.\u003c/p\u003e\n\u003cp\u003e4 Exclusion criteria: ①combined with serious primary diseases such as cardiovascular and cerebrovascular diseases, liver and renal insufficiency; ② pregnant and lactating women; ③ patients with endocrine diseases such as thyroid dysfunction; ④ patients with physical stimulation (cupping, scraping, moxibustion, plaster application, etc.) before infrared thermal imaging detection; ⑤ patients with large area of skin damage, scar and infection; ⑥ unable to cooperate, such as complicated with neurological or mental disorders, or unwilling to cooperate; ⑦ In infrared thermography analysis, it was found that excessively large or sagging breasts can affect the measurement of the anterior chest temperature, and a large beer belly can affect the measurement of the lower abdominal temperature.\u003c/p\u003e\n\u003cp\u003e5 Observation indicators and methods\u003c/p\u003e\n\u003cp\u003e5.1 Observation and Documentation of Infrared Thermography Characteristics : The HIR-2000A infrared thermograph, produced by Beijing Yuetian Optoelectronic Technology Co., Ltd., was utilized in this study. A single operator, who had undergone specialized training in the use of the infrared thermograph, conducted all measurements. Subjects were positioned within the infrared thermograph, maintaining an ambient temperature of 20\u0026ndash;24\u0026deg;C and a humidity level of 40\u0026ndash;60%. The imaging environment was controlled to eliminate interference from wind, strong light, and other potential disturbances, with heating and cooling sources kept at a distance. Subjects were instructed to avoid consuming large quantities of water and to refrain from eating spicy or cold foods within three hours prior to imaging. They were fully exposed, free of perspiration, with their hair tied up and body relaxed\u003csup\u003e[19]\u003c/sup\u003e. Imaging was performed after subjects had removed their clothing and rested quietly for 15 minutes\u003csup\u003e[20、21、23]\u003c/sup\u003e. Infrared thermographic images were acquired following the standard operating procedures of the infrared thermograph. Anatomical posture images of the subjects were collected, including: one image of the upper front torso, one of the lower front torso, one of the upper back torso, one of the lower back torso, one of the axillae in an anterior position, one of the right neck in a supine position, and one of the left neck in a supine position (as illustrated in Fig.\u0026nbsp;1). A database was created using Microsoft Excel to systematically input patient data, including average infrared temperature readings (neck, anterior chest, abdomen, lower abdomen, spine, lower back, palms of both hands, and dorsum of both feet), uniformity values (neck, anterior chest, abdomen, lower abdomen, back), and symmetry values (anterior torso, left and right neck, left and right subclavian regions, left and right inguinal regions). Data analysis for the average temperature, uniformity, and symmetry of each region in the infrared thermographic images was conducted using an image recognition and processing software developed by Professor Wenxue Hong\u0026apos;s team at the Institute of Biomedical Engineering, Yanshan University. This software computed the uniformity level, average temperature, and symmetry level of the target regions in the infrared thermographic images. Higher uniformity values indicated better uniformity, whereas lower values indicated poorer uniformity. Symmetry values closer to 1 represented higher symmetry, while values approaching 0 indicated lower symmetry.\u003c/p\u003e\n\u003cp\u003e5.2 Statistical methods: The statistical analysis of the study data was performed using SPSS version 25.0, with a research data database established in Microsoft Excel. Differences in categorical data were compared using the chi-square test. For the comparison of continuous data between groups, normality tests and homogeneity of variance tests were conducted first. Based on the test results, the independent samples t-test was employed if P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, indicating statistical significance. If the data did not follow a normal distribution or the variances were not equal, the Wilcoxon Mann-Whitney U test was used instead. Normally distributed continuous data were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (x\u0026thinsp;\u0026plusmn;\u0026thinsp;s), while non-normally distributed or heterogeneous variance data were expressed as median (P25, P75). GraphPad Prism software was used to create violin scatter plots to describe the differences between the malignant lymphoma group and the normal control group, aiming to identify infrared thermography characteristics of malignant lymphoma patients. Based on these findings, multivariate binary logistic regression analysis was applied to distinguish independent risk factors between malignant lymphoma patients and healthy individuals. The receiver operating characteristic (ROC) curve and Youden index were utilized to determine the cut-off value, evaluating the diagnostic performance of the indicators. The optimal Youden index was calculated as the sum of the maximum sensitivity and the maximum specificity minus one, and the corresponding infrared thermography indicator level at this Youden index was taken as the cut-off value.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cspan\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e1. General Information\u003c/p\u003e\u003cspan\u003e\n \u003cp\u003e1.1 Gender Statistics\u003c/p\u003e\n\u003c/span\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eA total of 40 patients were included in the malignant lymphoma group, with 18 males accounting for 45% and 22 females accounting for 55%. In the normal control group of 40 individuals, there were 15 males, representing 37.5%, and 25 females, representing 62.5%. The chi-square test yielded a P-value greater than 0.05, indicating that there was no statistically significant difference in gender composition between the malignant lymphoma group and the normal control group. Figure \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003e1.2 Age statistics chart comparing the gender composition of two population groups\u003c/p\u003e\n\u003cp\u003eAmong the 40 malignant lymphoma patients included in the study, the oldest was 77 years old, the youngest was 18 years old, and the average age was 50.15 ± 15.57 years. In the healthy individuals of the normal control group, the oldest was 66 years old, the youngest was 26 years old, and the average age was 45.13 ± 8.67 years. The age distribution data of both groups passed the normality test (P \u0026gt; 0.05), indicating a normal distribution. Furthermore, the chi-square test showed no statistically significant difference in age between the two groups (P \u0026gt; 0.05). Detailed age distributions for both groups can be found in Figs. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003e1.3 Course statistics of malignant lymphoma patients\u003c/p\u003e\n\u003cp\u003eAmong the 40 patients with malignant lymphoma, the longest course of disease was 164 months, the shortest was 1 month, the normal distribution test P \u0026lt; 0.05, did not meet the normal distribution, the average course of disease was 33.25 ± 34.50 months, the median was 22(9.00,39.75) months. The specific disease course distribution is shown in Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003e1.4 Classification statistics of malignant lymphoma patients\u003c/p\u003e\n\u003cp\u003eThe patients in the malignant lymphoma group were divided into non-Hodgkin's lymphoma and Hodgkin's lymphoma according to the World Health Organization (WTO) lymphoma classification criteria. Among them, 37 cases (92.5%) were non-Hodgkin's lymphoma, 33 cases (82.5%) were B-cell lymphoma, 3 cases (7.5%) were T-cell lymphoma, and 1 case was NKT cell lymphoma. There were 3 cases of Hodgkin's lymphoma (7.5%). The specific distribution is shown in Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003e2 Infrared thermal imaging characteristics of patients with malignant lymphoma\u003c/p\u003e\n\u003cp\u003eThere was no significant difference in gender and age between the normal control group and the malignant lymphoma group (P \u0026gt; 0.05). See Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e for details.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSex and age of normal control group and normal control group\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eNumber of cases(n)\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eMale: Female\u003c/p\u003e\n \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eNormal control group\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e45.13 ± 8.67\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e1:1.67\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eMalignant lymphoma group\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e50.15 ± 15.57\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e1:1.22\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e2.1 Difference of average temperature in target area between malignant lymphoma patients and normal controls group\u003c/p\u003e\n\u003cp\u003eThe average temperature of infrared thermal imaging in the malignant lymphoma group was higher than that in the normal control group in 5 measured target areas (neck, left hand heart, right hand heart, left foot, right foot), and lower than that in the normal control group in 5 measured target areas (anterior chest, abdomen, lower abdomen, spine, back waist). There was no significant difference in three target areas (neck, anterior chest and abdomen) (P \u0026gt; 0.05). Results The average surface temperature in the four target areas (left hand heart, right hand heart, left foot and right foot) of malignant lymphoma group was significantly higher than that of the normal control group (P \u0026lt; 0.05), that is, the average temperature of the left hand heart, right hand heart, left foot and right foot of malignant lymphoma group was higher than that of the normal control group. The mean temperature in the malignant lymphoma group was significantly lower than that in the normal control group in the three target areas (abdomen, middle spine and back waist) (P \u0026lt; 0.05). That is, the average temperature of the abdomen, middle spine and back of the malignant lymphoma group was lower than that of the normal control group. See Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e, Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e, Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e and Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e for details.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAverage temperature levels in target areas of normal control group and malignant lymphoma group\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eTarget area\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eNormal control group (N = 40)\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eMalignant lymphoma group (N = 40)\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eMean neck temperature\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e33.07(32.76,33.39)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e33.57(32.67,33.84)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.137\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eMean chest temperature\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e32.5 ± 1.01\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e32.3 ± 1.17\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.468\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eMean abdominal temperature\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e32.99(32.16,33.80)**\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e32.09(31.53,33.09)**\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eMean temperature of the lower abdomen\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e32.46(31.49,33.19)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e32.25(31.70,33.13)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.7733\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eMean spine temperature\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e33.18(32.65,34.12)**\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e32.17(31.66,33.22)**\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eAverage temperature of the back waist\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e32.76(32.12,33.42)**\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e32.0(31.26,32.62)**\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eMean heart temperature of the left hand\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e31.18(30.45,31.91)**\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e32.52(31.80,33.42)**\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eMean heart temperature of the right hand\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e31.41(30.60,32.35)**\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e32.81(31.87,33.55)**\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eMean temperature of the left foot\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e29.4 ± 1.82**\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e31.6 ± 1.72**\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eMean temperature of the right foot\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e29.65(28.11,31.21)**\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e32.07(30.75,32.78)**\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e2 Differences in homogeneity of target region between malignant lymphoma patients and normal controls group\u003c/p\u003e\n\u003cp\u003eThe homogeneity of infrared thermography in the malignant lymphoma group was lower than that in the normal control group in 4 target regions (neck, anterior chest, abdomen, lower abdomen), and higher than that in 1 target region (posterior back). There was no significant difference in 3 target regions (anterior chest, abdomen, posterior back) (P \u0026gt; 0.05). Results The surface homogeneity of the malignant lymphoma group was significantly lower than that of the normal control group in the two target areas (neck and lower abdomen) (P \u0026lt; 0.05). See Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e for details.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eHomogeneity level of target area in normal control group, malignant lymphoma group\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eTarget area\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eNormal control group (N = 40)\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eMalignant lymphoma group (N = 40)\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eHomogeneity of neck\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e4.09(3.74,5.25)**\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e3.52(2.54,4.43)**\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eHomogeneity of chest\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e5.56 ± 1.95\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e5.18 ± 1.83\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.381\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eHomogeneity of abdomen\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e5.02(3.20,6.68)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e4.20(3.11,6.10)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.422\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eHomogeneity of the lower abdomen\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e5.32 ± 4.97*\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e3.68 ± 1.31*\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eHomogeneity of the back\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e5.64(4.48,8.37)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e6.48(4.90,7.47)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.683\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e2.3 Differences in the symmetry of the target region between malignant lymphoma patients and normal controls group\u003c/p\u003e\n\u003cp\u003eThe symmetry of infrared thermography in the malignant lymphoma group was lower than that in the normal control group in four target regions (anterior, neck, clavicle region and inguinal region), and there was no significant difference in one target region (inguinal region) (P \u0026gt; 0.05). Results The surface symmetry of the malignant lymphoma group was significantly worse than that of the normal control group in the three target regions (anterior, neck and clavicle region) (P \u0026lt; 0.05). See Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003e for details.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSymmetry level of target region in normal control group, malignant lymphoma group\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eTarget area\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eNormal control group (N = 40)\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eMalignant lymphoma group (N = 40)\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eSymmetry of the front body\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.68 ± 0.06**\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.63 ± 0.06**\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eSymmetry of the neck\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.77 ± 0.07**\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.69 ± 0.10**\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eSymmetry of the clavicle\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.74 ± 0.067*\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.70 ± 0.09*\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eSymmetry of the groin\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.82(0.75,0.85)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.78(0.75,0.83)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.206\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e2.4 Infrared thermal imaging features of precursors of patients with malignant lymphoma\u003c/p\u003e\n\u003cp\u003eNon-parametric test was used to analyze the difference between the mean temperature of the abdomen and the mean temperature of the chest and the small abdomen in the normal control group. The results showed that there was no significant difference between the mean temperature of the abdomen and the mean temperature of the chest and the small abdomen (P \u0026gt; 0.05). Non-parametric test was used to analyze the difference between the mean temperature of the abdomen and the mean temperature of the chest and the small abdomen in the malignant lymphoma group. The results showed that there was no significant difference between the mean temperature of the abdomen and the mean temperature of the chest and the small abdomen (P \u0026gt; 0.05). See Tables \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e and\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e for details.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eTemperature difference between abdomen and chest by infrared thermal imaging\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eMean abdominal temperature\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eAverage temperature of the anterior chest\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eNormal control group\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e32.49(31.66,33.34)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e32.60(31.68,33.31)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.802\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eMalignant lymphoma group\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e32.09(31.53,33.09)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e32.36(31.75,33.35)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.468\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eInfrared thermal imaging abdominal and small abdominal temperature differences\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eMean abdominal temperature\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eAverage temperature of the anterior chest\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eNormal control group\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e32.49(31.66,33.34)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e32.46(31.49,33.19)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.612\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eMalignant lymphoma group\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e32.09(31.53,33.09)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e32.25(31.71,33.13)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.416\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e3 Risk factor analysis used to distinguish patients with malignant lymphoma from healthy individuals\u003c/p\u003e\n\u003cp\u003eThe indexes with P value \u0026lt; 0.05 at baseline were included in binary multivariate Logistic regression. After excluding collinearity, the indexes included abdominal average temperature, spinal average temperature, posterior lumbar average temperature, left hand heart average temperature, right hand heart average temperature, left foot average temperature, right foot average temperature, neck uniformity, small abdomen uniformity, anterior symmetry, neck symmetry, and clavicle symmetry. The results showed that the homogeneity of lower abdomen (1.795, 1.170–2.753) and the symmetry of neck were independent risk factors for the diagnosis of malignant lymphoma (P \u0026lt; 0.05). See Table \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e. The ROC curve of the uniformity of the abdomen and the symmetry of the neck showed that the AUC of the uniformity of the abdomen in the diagnosis of malignant lymphoma was 0.600, and the cut-off value was 6.38. The AUC of neck symmetry in the diagnosis of malignant lymphoma was 0.764, the sensitivity was 0.725, the specificity was 0.275, and the cut-off value was 0.745. See Tables \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e, Figs. \u003cspan class=\"InternalRef\"\u003e12\u003c/span\u003e and\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e13\u003c/span\u003e for details.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMultivariate logistic regression analysis of independent risk factors for malignant lymphoma\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eCharacteristic of temperature\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eMean abdominal temperature\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.586\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.169–2.003\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.400\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eMean spine temperature\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e2.826\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.412–19.368\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.290\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eAverage temperature of the back waist\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1.105\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.155–7.879\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.920\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eMean heart temperature of the left hand\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.403\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.069–2.343\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.312\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eMean heart temperature of the right hand\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1.789\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.346–9.254\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.488\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eMean temperature of the left foot\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.644\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.189–2.199\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.482\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eMean temperature of the right foot\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1.007\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.312–3.247\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.991\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eHomogeneity of neck\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.761\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.413–1.403\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.381\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eHomogeneity of the lower abdomen\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1.795**\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1.170–2.753**\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eSymmetry of the front body\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.301\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eSymmetry of the neck\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eSymmetry of the clavicle\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"char\"\u003e\n \u003cp\u003e0.774\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\n\u003c/div\u003e\n\n\n\n"},{"header":"Discussion","content":"\u003cp\u003eChanges in energy metabolism and temperature are accompanied by every physiological and pathological process of the human body, and every physiological and pathological process is accompanied by changes in the structure and function of the body system. Functional changes often precede structural changes \u003csup\u003e[24,25]\u003c/sup\u003e, and observing changes in body temperature is a way for us to monitor the body function \u003csup\u003e[26]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThis study finally found that based on infrared thermal imaging technology, small abdominal homogeneity and neck symmetry are independent risk factors to distinguish patients with malignant lymphoma from healthy people. The cervical lymph nodes included submandibular lymph nodes, superficial lateral lymph nodes, deep lateral lymph nodes, and supraclavicular lymph nodes. lymph nodes in the lower abdomen mainly radiate from inguinal lymph nodes, including external iliac lymph nodes, internal iliac lymph nodes, and common iliac lymph nodes. When lymph nodes are invaded by cancer cells, their proliferation and differentiation will change. v-myc avian myelocytomatosis viral oncogene homolog (MYC) is a gene involved in cell proliferation, and its dysregulation can be found in malignant lymphomas \u003csup\u003e[27]\u003c/sup\u003e. The cellular environment of malignant lymphoma is conducive to promoting glucose uptake by tumor cells \u003csup\u003e[28]\u003c/sup\u003e. In the case of hypoxia, anaerobic glycolysis will be stimulated, but some cancer cells can absorb nutrients and oxygen from the blood supply. Therefore, aerobic glycolysis will also be increased, leading to increased local metabolism, increased heat production and increased temperature.\u003c/p\u003e\u003cp\u003eTheoretically, all lymph nodes or lymphatic vessels invaded by cancer cells will show hypermetabolism. However, because infrared thermal imaging detects the temperature of the body surface and adjacent surface tissues, it is not sensitive to the temperature of deep layers and is sensitive to the abnormal metabolism of superficial lymph nodes. At the same time, the neck and abdomen are the superficial locations of lymph nodes. Therefore, the abnormal temperature metabolism of the neck and lower abdomen is relatively clear, but it also indicates that the surface temperature change of malignant lymphoma has its own characteristics. We can screen malignant lymphoma by detecting the uniformity of the temperature of the lower abdomen and the symmetry of the temperature of the neck. However, further studies are needed to confirm the underlying mechanisms.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAUTHORA CONTRIBUTIONS\u003c/p\u003e\n\u003cp\u003eStudy desigen: Wu Junfan, Jia Qiuran, Li Dongyun, Zhang Wenzheng; Study Investigator: Wu Junfan, Jia Qiuran; Contributed patients or study materials: Wu Junfan, Jia Qiuran; Collection and assembly of data: Wu Junfan; Analysed the data: Wu Junfan, Jia Qiuran; Data interpretation: Wu Junfan, Jia Qiuran; Critical review and revision of this manuscript and approval of the manuscript for submission: All authors.\u003c/p\u003e\n\u003cp\u003eAFFILIATIONS\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eDongzhimen Hospital, Beijing University of Traditional Chinese Medicine, Beijing, CHINA;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e2\u003c/sup\u003eBeijing Haidian Hospital, Beijing, CHINA;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e3\u003c/sup\u003eHealth Management Center, International Department, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, CHINA\u003c/p\u003e\n\u003cp\u003eACNOWLEDGEMENTS\u003c/p\u003e\n\u003cp\u003eStudy desigen: Wu Junfan, Jia Qiuran, Li Dongyun, Zhang Wenzheng; Study Investigator: Wu Junfan, Jia Qiuran; Contributed patients or study materials: Wu Junfan, Jia Qiuran; Collection and assembly of data: Wu Junfan; Analysed the data: Wu Junfan, Jia Qiuran; Data interpretation: Wu Junfan, Jia Qiuran; Critical review and revision of this manuscript and approval of the manuscript for submission: All authors.\u003c/p\u003e\n\u003cp\u003eFUNDING INFORMATION\u003c/p\u003e\n\u003cp\u003eGraduate research project of Beijing University of Chinese Medicine (90011461220441) : Evaluation of lymph node function in patients with malignant lymphoma based on infrared thermal imaging technology\u003c/p\u003e\n\u003cp\u003eCONFLICT OF INTERECT STATEMENT\u003c/p\u003e\n\u003cp\u003eNone of the authors has a relevant conflict of interest.\u003c/p\u003e\n\u003cp\u003eDATA AVAILABILITY STATEMENT\u003c/p\u003e\n\u003cp\u003eN/A\u003c/p\u003e\n\u003cp\u003eETHICS STATEMENT\u003c/p\u003e\n\u003cp\u003eThis study followed the ethical standards formulated by the Ethics Committee of Dongzhimen Hospital, Beijing University of Chinese Medicine, followed the principles of confidentiality and voluntary, and has been approved by the Ethics committee (ethical approval number: 2023DZMEC-335-01).\u003c/p\u003e\n\u003cp\u003ePATIENT CONSENT STATEMENT\u003c/p\u003e\n\u003cp\u003eThe study was performed according to the Helsinki Declaration. Patient provided written informed consent.\u003c/p\u003e\n\u003cp\u003eCONTACT PERSON\u003c/p\u003e\n\u003cp\u003eAnyone have related questions about this study, or want to get the original data can directly link to the first author: 20210931428 @bucm.edu.cn\u003c/p\u003e\n\u003cp\u003eORCID\u003c/p\u003e\n\u003cp\u003eJunfan Wu: https://orcid.org/0009-0006-2856-3771\u003c/p\u003e\n\u003cp\u003eQiuran Jia: https://orcid.org/0009-0005-9733-8511\u003c/p\u003e\n\u003cp\u003eWenzheng Zhang: https://orcid.org/0000-0001-6188-7977\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLiu W, Liu J, Song Y,et al.Burden of lymphoma in China, 1990-2019: an analysis of the global burden of diseases, injuries, and risk factors study 2019. Aging (Albany NY). 2022 Apr 10;14(7):3175-3190.\u003c/li\u003e\n\u003cli\u003eWang Fangxu, Tao Libo, Dong Dong et al. Disease burden of patients with Hodgkin lymphoma in China: a study based on an online questionnaire survey [J]. China Medical Insurance,2020,No.136(01):60-64. (in Chinese) \u003c/li\u003e\n\u003cli\u003ePhillips EH, Iype R, Wirth A. PET-guided treatment for personalised therapy of Hodgkin lymphoma and aggressive non-Hodgkin lymphoma. Br J Radiol. 2021 Nov 1;94(1127):20210576.\u003c/li\u003e\n\u003cli\u003eWolfson JA, Bhatia S, Ginsberg JP,et al. Expenditures in Young Adults with Hodgkin Lymphoma: NCI-Designated Comprehensive Cancer Centers versus Other Sites. Cancer Epidemiol Biomarkers Prev. 2022 Jan;31(1):142-149.\u003c/li\u003e\n\u003cli\u003eAl-Ibraheem A, Anwer F, Juweid ME, et al. Interim FDG-PET/CT for therapy monitoring and prognostication in Hodgkin\u0026apos;s Lymphoma. Sci Rep. 2022 Oct 21;12(1):17702. \u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003e[6]\u003c/strong\u003eLewis WD, Lilly S, Jones KL. Lymphoma: Diagnosis and Treatment. Am Fam Physician. 2020 Jan 1;101(1):34-41. \u003c/li\u003e\n\u003cli\u003eBrice P, de Kerviler E, Friedberg JW. Classical Hodgkin lymphoma. Lancet. 2021 Oct 23;398(10310):1518-1527. \u003c/li\u003e\n\u003cli\u003eWang X, Zhang L, Liu X, et al. Efficacy and Safety of a Pegasparaginase-Based Chemotherapy Regimen vs an L-asparaginase-Based Chemotherapy Regimen for Newly Diagnosed Advanced Extranodal Natural Killer/T-Cell Lymphoma: A Randomized Clinical Trial. JAMA Oncol. 2022 Jul 1;8(7):1035-1041.\u003c/li\u003e\n\u003cli\u003eHou K, Yu Z, Jia Y, et al. Efficacy and safety of ibrutinib in diffuse large B-cell lymphoma: A single-arm meta-analysis. Crit Rev Oncol Hematol. 2020 Aug;152:103010.\u003c/li\u003e\n\u003cli\u003eLo AC, Dieckmann K, Pelz T,et al. Pediatric classical Hodgkin lymphoma. Pediatr Blood Cancer. 2021 May;68 Suppl 2:e28562.\u003c/li\u003e\n\u003cli\u003eTattersall GJ. Infrared thermography: A non-invasive window into thermal physiology. Comp Biochem Physiol A Mol Integr Physiol. 2016 Dec;202:78-98. \u003c/li\u003e\n\u003cli\u003eFraser J. Seeing Infrared: Breast Cancer, Inuit, and the Extractive Colonality of Disease Distributions and Diagnostic Imaging Technologies. Technol Cult. 2021;62(3):709-740. \u003c/li\u003e\n\u003cli\u003eDami\u0026atilde;o CP, Montero JRG, Moran MBH, et al. Application of thermography in the diagnostic investigation of thyroid nodules. Endocr J. 2021 May 28;68(5):573-581. \u003c/li\u003e\n\u003cli\u003ede Almeida ANS, de Souza Ferreira SL, Balata PMM, et al. Thermography in complementary assessments of head and neck muscles: A scoping review. J Oral Rehabil. 2022 Dec;49(12):1188-1196.\u003c/li\u003e\n\u003cli\u003eSchiavon G, Capone G, Frize M, et al. Infrared Thermography for the Evaluation of Inflammatory and Degenerative Joint Diseases: A Systematic Review. Cartilage. 2021 Dec;13(2_suppl):1790S-1801S.\u003c/li\u003e\n\u003cli\u003eJalali S, Ansell SM. The potential role of glycogen metabolism in diffuse large B-cell lymphoma. Leuk Lymphoma. 2020 May;61(5):1028-1036.\u003c/li\u003e\n\u003cli\u003eBeielstein AC, Pallasch CP. Tumor Metabolism as a Regulator of Tumor-Host Interactions in the B-Cell Lymphoma Microenvironment-Fueling Progression and Novel Brakes for Therapy. Int J Mol Sci. 2019 Aug 26;20(17):4158.\u003c/li\u003e\n\u003cli\u003eZhu Jun, Ma Jun, Union for China Lymphoma Investigators of Chinese Society of Clinical Oncology(2021). Chinese Society of Clinical Oncology (CSCO) diagnosis and treatment guidelines for malignant lymphoma 2021 (English version). Chin J Cancer Res, 33(3), 289-301. \u003c/li\u003e\n\u003cli\u003eRaccuglia M, Heyde C, Lloyd A, et al. The use of infrared thermal imaging to measure spatial and temporal sweat retention in clothing. Int J Biometeorol. 2019 Jul;63(7):885-894. \u003c/li\u003e\n\u003cli\u003eTian S W. Research on the effect of Baduanjin on the function of viscera based on infrared thermal imaging [D]. 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Deregulation of enhancer structure, function, and dynamics in acute lymphoblastic leukemia. Trends Immunol. 2021 May;42(5):418-431.\u003c/li\u003e\n\u003cli\u003eFrost PA, Chen S, Rodriguez-Ayala E, et al. Research methodology for in vivo measurements of resting energy expenditure, daily body temperature, metabolic heat and non-viral tissue-specific gene therapy in baboons. Res Vet Sci. 2020 Dec;133:136-145.\u003c/li\u003e\n\u003cli\u003eKorać, P, Dotlić S, Matulić M, et al. Role of MYC in B Cell Lymphomagenesis. \u003cem\u003eGenes\u003c/em\u003e \u003cstrong\u003e2017\u003c/strong\u003e, \u003cem\u003e8\u003c/em\u003e, 115.\u003c/li\u003e\n\u003cli\u003eKirsch BJ, Chang SJ, Betenbaugh MJ,et al. Non-Hodgkin Lymphoma Metabolism. Adv Exp Med Biol. 2021;1311:103-116. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Malignant lymphoma, Infrared thermal imaging technology, Body surface temperature","lastPublishedDoi":"10.21203/rs.3.rs-4517867/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4517867/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective:\u003c/strong\u003e To evaluate the difference in body surface temperature characteristics between patients with malignant lymphoma and healthy people by infrared thermography, and to find the independent risk factors for malignant lymphoma in temperature characteristics.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e The infrared heat maps of 40 patients with malignant lymphoma and 40 healthy people who were admitted to Dongzhimen Hospital from December 2022 to December 2023 were collected, and the temperature characteristics of the target area were measured and analyzed.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e The average temperature of hands and feet in the malignant lymphoma group was higher than that in the normal control group. The average temperature of the abdomen, spine and back of the control group was lower than that of the normal control group (P \u0026lt; 0.05). The homogeneity of the neck and abdomen was worse than that of the normal control group (P \u0026lt; 0.05). The symmetry of the anterior, neck and clavicle region was worse than that of the normal control group (P \u0026lt; 0.05). Lower abdominal homogeneity and neck symmetry were independent risk factors for the diagnosis of malignant lymphoma (P\u0026lt;0.05).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Infrared thermal imaging is beneficial to the screening of malignant lymphoma and the evaluation of therapeutic effect.\u003c/p\u003e","manuscriptTitle":"Surface temperature characteristics of patients with malignant lymphoma based on infrared thermal imaging technology","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-03 19:05:37","doi":"10.21203/rs.3.rs-4517867/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9f3cd70e-84bd-407e-9e49-3ea01444d562","owner":[],"postedDate":"July 3rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":33746829,"name":"Biological sciences/Biological techniques/Imaging"},{"id":33746830,"name":"Biological sciences/Cancer/Haematological cancer"},{"id":33746831,"name":"Biological sciences/Cancer/Haematological cancer/Lymphoma"}],"tags":[],"updatedAt":"2025-04-02T11:08:54+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-03 19:05:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4517867","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4517867","identity":"rs-4517867","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}