Diagnostic predictive model for distinguishing intravascular large B-cell lymphoma among patients with fever of unknown origin

Diagnostic predictive model for distinguishing intravascular large B-cell lymphoma among patients with fever of unknown origin | 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 Research Article Diagnostic predictive model for distinguishing intravascular large B-cell lymphoma among patients with fever of unknown origin Chao Chen, Yiao Di, Zhe Zhuang, Zepeng Li, Congwei Jia, Ximin Shi, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4441204/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 Intravascular large B-cell lymphoma (IVLBCL) is relatively rare. Due to the lack of distinctive symptoms, the diagnosis is difficult. Fever of unknown origin (FUO) is the most common symptom. This study retrospectively analyzed IVLBCL patients and FUO patients between February 2015 and October 2023. Diagnostic predictive models were constructed using multivariable logistic regression in the training cohort including 42 IVLBCL patients and 45 FUO patients. Model 1 consisted of peripheral edema, hypoxemia, neurological symptoms, hemophagocytic lymphohistiocytosis, and interstitial lung abnormalities on CT, exhibited good diagnostic efficiency (AUC = 0.928). Model 2 consisted of peripheral edema, hypoxemia, neurological symptoms and Interleukin-10/Interleukin-6 concentration ratio, showed a higher diagnostic ability than Model 1 (AUC = 0.982, p = 0.023). The diagnostic abilities of models were validated internally in leave-one-out validation and externally in the validation cohort. As some medical institutions may lack the capability to test for interleukin concentration, Model 1 can be used to help the diagnosis of IVLBCL in these institutions. Based on Model 1 and Model 2, we established and validated two integer-based scoring systems for clinical practice. Most patients (58%) were histologically diagnosed by random skin biopsy, and the positive rate of random skin biopsy was 79.5%. Intravascular large B-cell lymphoma logistic regression fever of unknown origin Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Intravascular large B-cell lymphoma (IVLBCL) is a relatively rare and distinct type of extranodal diffuse large B-cell lymphoma. IVLBCL is characterized by the predominant growth of large cells within the lumen of different-sized blood vessels (particularly capillaries)[1]. Because neoplastic cells of IVLBCL usually do not circulate in the peripheral blood, do not form an extravascular tumor mass[2, 3], and due to the near-ubiquitous presence of its neoplastic cells, its clinical presentation is highly heterogeneous. In Asia, fever of unknown origin (FUO) is the most common systemic symptom (87–100%) and is typically the primary symptom during the initial medical consolations[4–6]. Other symptoms of IVLBCL are based on the involvement sites. The common involvement sites of IVLBCL are CNS, skin and lung. Therefore, heterogenous neurological symptoms, different kinds of skin lesions and pulmonary symptoms are frequent[7, 8]. Furthermore, some patients also displayed typical clinical hemophagocytic syndrome and presented with peripheral edema[9–11]. Therefore, the diagnosis of IVLBCL is often tricky and requires histological confirmation. In two retrospective studies conducted in Japan and Western countries with review periods exceeding ten years, the proportion of patients diagnosed with IVLBCL through autopsy was 16% and 24%, respectively[12, 13]. This indicates the diagnostic challenge of IVLBCL. IVLBCL is an aggressive disease, delayed diagnosis could result in terrible outcomes. We should find a new method to identify IVLBCL early and establish an improved diagnostic procedure for earlier intervention. We have previously reported that IL-10 was a valuable biomarker for early diagnosis for IVLBCL[14]. Because of the fact that the majority of IVLBCL patients present with FUO as their initial symptom, this study aimed to construct diagnostic prediction models capable of identifying IVLBCL from FUO and analyze the different biopsy methods employed in the patients. Through this study, we aimed to summarize a practical and accurate diagnostic procedure for IVLBCL. Methods Patients The local ethical review boards approved this retrospective cohort study, conducted at Peking Union Medical College Hospital between February 2015 and October 2023. All patients were informed, and the procedure was performed under the Declaration of Helsinki. This study included two cohorts: a modling cohort and a validation cohort, comprising 60 IVLBCL patients and 66 FUO patients. The training cohort consisted of 42 IVLBCL patients and 45 FUO patients diagnosed between February 2015 and November 2021. The validation cohort consisted of 18 IVLBCL patients and 21 FUO patients diagnosed between November 2021 and October 2023. Eligibility criteria for inclusion of IVLBCL patients within the study were: (i) had clinical presentation of FUO; (ii) histologically confirmed IVLBCL; (iii) aged 18–79 years. Eligibility criteria for inclusion of FUO patients within the study were: (i) unexplained fever persisted more than 3 weeks; (ii) aged 18–79 years; (iii) excluded of IVLBCL diagnosis. Patient characteristics, including neurological symptoms, and laboratory data, including hematological, biochemical, and immunological findings were collected from electronic medical records. Imaging reports of baseline chest computed tomography before treatment were reviewed. Pathological data of all biopsied specimens from skin, lung, and bone marrow of IVLBCL patients were also collected. The dermatology department performed random skin biopsy and specimens were usually obtained from 3 separate, fat-containing areas of the skin. In the characteristics, peripheral edema was defined as abnormal accumulation of fluid in the peripheral tissues, typically in the extremities. Hypoxemia was defined as PaO 2 below 60mmHg at the room air. Skin rash was characterized by redness, itching, inflammation, blistering, or other alterations in the skin. HLH was diagnosed according to HLH-2004 Diagnostic Criteria[15]. Iinterstitial lung abnormality (ILA) is characterized by CT scan findings that include ground-glass or reticular abnormalities, lung distortion, traction bronchiectasis, honeycombing, and non-emphysematous cysts[16]. Statistical analysis Figure 1 illustrates a flowchart outlining the study. Data were analyzed with the Statistical Package for Social Sciences, version 27.0 (SPSS Inc., Chicago, IL, USA). Mann-Whitney U test was used to compare median values of data that were not normally distributed within the training cohort. Chi-square test was used to compare the categorical variable. The area under the receiver operating characteristic (ROC) curve was calculated to assess the predictive value of IL-10/IL-6 concentration ratio. The multivariate logistic regression analysis was used to construct diagnostic Model 1 and Model 2 for diagnosis of IVLBCL from FUO. The Delong test was used to compare diagnostic models and IL-10/IL-6 concentration ratio with the highest area under the curve (AUC). The leave-one-out (LOO) analysis was performed to internal validate the diagnostic performance within the training cohort in which models were generated. The analysis was performed for Model 1 and 2 and IL-10/IL-6 concentration ratios. Model 1, Model 2 and IL-10/IL-6 ratio were applied to the validation cohort to examine the diagnostic ability of the models generated from the training cohort. Results Clinical characteristics Table 1 showed the training cohort's baseline characteristics containing 42 IVLBCL patients and 45 FUO patients. The median age (58.5 vs 56, p = 0.13) and gender distribution (male, 54.8% vs 51.1%, p = 0.727) were comparable between patients with IVLBCL and FUO. The occurrence of peripheral edema, hypoxemia, neurological symptoms, and hemophagocytic lymphohistiocytosis was observed to be significantly higher in patients with IVLBCL than those with FUO. The most frequent neurological symptoms of IVLBCL patients were extremity weakness (11, 26.2%) and cognitive impairment (6, 14.3%). Additionally, the CT scans revealed a higher incidence of ILA in IVLBCL patients. And the assessment of serum IL-10 concentration as well as the IL-10/IL-6 concentration ratio in IVLBCL patients was significantly greater than those in FUO patients. In these 45 FUO patients, 14 were finally diagnosed as infectious disease, 13 were connective tissue disease, 10 were hematological disease, 5 were tumor and 3 were other disease. Table 1 Baseline characteristics of patients in the training cohort. Variables IVLBCL (n = 42) FUO (n = 45) p value Age, median (range), years 58.5 (32 to 76) 56 (21 to 73) 0.13 Sex, n (%) Male 23 (54.8%) 24 (51.1%) 0.727 Female 19 (45.2%) 23 (48.9%) Peripheral edema, n (%) 27 (64.3%) 4 (8.9%) < 0.001 Hypoxemia, n (%) 33 (78.6%) 5 (11.1%) < 0.001 Skin rash, n (%) 20 (47.6%) 16 (34.0%) 0.193 Neurological symptoms, n (%) 23 (54.8%) 8 (17.8%) < 0.001 Extremity weakness 11 (26.2%) 2 (4.4%) Cognitive impairment 6 (14.3%) 0 Paraesthesia 4 (9.5%) 3 (6.7%) Slurred speech 3 (7.1%) 1 (2.2%) Disorder of consciousness 3 (7.1%) 5 (11.1%) Epilepsy 3 (7.1%) 0 Visual disturbances 1 (2.4%) 0 Hearing impairment 1 (2.4%) 0 Hemophagocytic lymphohistiocytosis, n (%) 17 (40.5%) 5 (11.1%) 0.001 CT showed ILA, n (%) 27 (64.3%) 12 (26.7%) < 0.001 IL-10 concentration, median (range), pg/mL 182 ( 1000) 0 (< 5 to 208) < 0.001 IL-10/IL-6, median (range) 11.5 (0 to 103.1) 0 (0 to 2.3) < 0.001 Diagnostic Model Generation We constructed two diagnostic models for IVLBCL based on multivariate logistic regression analysis (Table 2 ). Model 1 select five commonly observed clinical parameters as explanatory variables, including peripheral edema, hypoxemia, neurological symptoms, HLH, and ILA. In the predictive logistic Model 1, significant factors affecting the diagnostic yield were peripheral edema (vs. negative) (odds ratio [OR] = 9.093; 95% CI = 1.960-51.105; p = 0.007), hypoxemia (vs. negative) (OR = 17.371; 95% CI = 3.725-108.101; p < 0.001) and neurological symptoms (vs. negative) (OR = 4.748; 95% CI = 1.128–22.154; p = 0.037). Univariate ROC analysis indicated that the IL-10/IL-6 concentration ratio showed a high differentiating diagnostic value in training cohort with a cut-off value of 0.400 (AUC = 0.975, sensitivity = 0.976, specificity = 0.915). Model 2 incorporates serum IL-10 concentration along with commonly observable clinical symptoms—peripheral edema, hypoxemia and neurological symptoms—as four explanatory variables. In Model 2, significant factor was IL-10/IL-6 concentration ratio (OR = 6.798; 95% CI = 2.013–51.698; p = 0.020) (Table 2 ). The ROC AUC of Model 1 and Model 2 for estimating the diagnostic efficiency were 0.928 and 0.982, respectively (Table 3 ) (Fig. 2 ). The DeLong test showed that the AUC of the Model 2 was better than Model 1 ( p = 0.023). Table 2 Model 1 and Model 2 generated by multivariate logistic regression analysis. Model 1 select five clinical parameters including peripheral edema, hypoxemia, neurological symptoms, HLH, and ILA. Model 2 incorporates serum IL-10 concentration with three clinical parameters including peripheral edema, hypoxemia and neurological symptoms. Variables Coefficient OR (95% CI) p value Model 1 Intercept -2.726 0.065 (0.017–0.192) < 0.001 Peripheral edema 2.208 9.093 (1.960-51.105) 0.007 Hypoxemia 2.855 17.371 (3.725-108.101) < 0.001 Neurological symptoms 1.558 4.748 (1.128–22.154) 0.037 HLH 0.604 1.828 (0.270-12.588) 0.532 CT showed ILA 0.165 1.180 (0.205–5.870) 0.843 Model 2 Intercept -5.353 0.005 (0.000-0.046) < 0.001 Peripheral edema 2.192 8.955 (0.620-176.411) 0.106 Hypoxemia 1.967 7.140 (0.454-162.013) 0.156 Neurological symptoms 2.027 7.588 (0.571–199.940) 0.143 IL-10 / IL-6 1.917 6.798 (2.013–51.698) 0.020 Table 3 Differential diagnostic efficiency of IL-10/IL-6 concentration ratio, Model 1 and Model 2. AUC was generated by ROC analysis. AUC, area under curve; ROC, receiver operating characteristic. Parameter Cut-off value Std AUC (95%CI) Sensitivity Specificity IL-10/IL-6 0.400 0.016 0.975 (0.944-1.000) 0.976 0.915 Model 1 0.553 0.029 0.928 (0.871–0.985) 0.857 0.915 Model 2 0.652 0.018 0.982 (0.947-1.000) 0.976 1.000 Internal validation of LR models Internal validation of IL-10/IL-6 ROC analysis, Model 1 and Model 2 was evaluated using the Leave-One-Out cross-validation. The results of LOO analysis indicate that the mean square error of IL-10/IL-6, Model 1 and Model 2 were 0.068, 0.121, 0.032, respectively. Furthermore, the mean AUC of IL-10/IL-6, Model 1 and Model 2 were 0.976, 0.888 and 0.981, respectively. External validation of LR models The external validation of IL-10/IL-6 ROC analysis, Model 1, and Model 2 was conducted by applying them to the independent validation cohort including 18 patients with IVLBCL and 21 patients with FUO (Fig. 3 ). Using the IL-10/IL-6 model yielded an AUC of 0.930 in the validation cohort, with no significant difference compared to the AUC obtained in the training cohort ( p = 0.345). Additionally, at a cut-off value of 0.400, it demonstrated a sensitivity of 0.944 and a specificity of 0.714. When using Model 1, the AUC was 0.909, with no significant difference from the AUC in the training cohort ( p = 0.716). At a cut-off of 0.553, the sensitivity was 0.778, and the specificity was 0.857. Using Model 2, the AUC was 0.956, with no significant difference from the AUC in the training cohort ( p = 0.462). At a cut-off of 0.652, the sensitivity was 0.944, and the specificity was 0.857. Integer-Based Scoring System To create the integer-based scoring system for clinical practice, we calculated the coefficient of each variable as a simple integer ratio from Model 1 and Model 2 above. The coefficients of each variable in Model 1 and Model 2 are listed in Fig. 3 a and Fig. 3 b. At the same time, the relations between the integer-based scores and predictive diagnostic yields are presented in Fig. 3 c and Fig. 3 d. The ROC AUC of the integer-based scoring system of Model 1 and Model 2 for estimating the diagnostic yield in the training cohort were 0.919 and 0.938, respectively (Fig. 4 E). In the validation cohort, the ROC AUC of the integer-based scoring system of Model 1 and Model 2 were 0.896 and 0.955, respectively (Fig. 4 F). Biopsy confirmation All 60 patients with IVLBCL in training cohort and validation cohort were diagnosed through a random skin biopsy, bone marrow biopsy and/or TBLB (Table 4 ). Most patients received bone marrow biopsy, but the positive rate was only 42.1%. Forty-four patients received random skin biopsy and the positive rate was 79.5%. Only 8 patients underwent TBLB, all of whom presented with hypoxemia and exhibited ILA on CT scans, and all these 8 patients were diagnosed with IVLBCL through TBLB. Among patients diagnosed with IVLBCL by Model 1 and Model 2, the positivity rates of random skin biopsy were 80.0% and 81.0%, respectively. Table 4 The pathological diagnosis of IVLBCL. Biopsy type Random skin biopsy Bone Marrow Biopsy TBLB Patients receive biopsy, n (% of all patients) 44 (73.3%) 57 (95.0%) 8 (13.3%) Patients diagnosed by biopsy, n (% of all patients) 35 (58.3%) 24 (40.0%) 8 (13.3%) Positive rate 79.5% 42.1% 100% Discussion Due to the absence of circulating neoplastic cells on peripheral blood smear[17] and the lack of any specific signs and symptoms[9], the IVLBCL diagnosis is highly challenging. FUO is the most common systemic symptom and is typically the primary symptom during the initial medical consultation[4, 18]. Therefore, in our study we established two diagnostic models to identify IVLBCL from FUO quickly and accurately. Besides, we demonstrated that IL-10/IL-6 concentration ratio could improve the ability of diagnosis of IVLBCL. Model 1 was established based on the presence of peripheral edema, hypoxemia, neurological symptoms, HLH, and ILA in CT. These variables were easily observable and detectable. In training cohort, hypoxemia, ILA in CT, peripheral edema, HLH and neurological symptoms, such as extremity weakness and cognitive impairment, were significantly more frequent in IVLBCL patients than FUO patients. These findings were consistent with the pathological physiology of IVLBCL[9]. However, there was no significant difference in the incidence of skin rash between IVLBCL and FUO patients. Due to the significant differences in these easily observable and detectable clinical variables, Model 1 was established. Model 1 exhibited good ability to diagnose IVLBCL (AUC = 0.975, sensitivity = 0.976 and specificity = 0.915). The internal and external validity of Model 1 also demonstrated its diagnostic capability (AUC = 0.888 and 0.909, respectively). Based on this, we proposed that using Model 1 for the assessment of patients at their initial visit for FUO is a convenient and effective preliminary diagnostic procedure for IVLBCL. A challenge to the diagnosis of IVLBCL is its lack of specific tumor markers. We previously reported that in IVLBCL, serum IL-10 levels were significantly higher than those in FUO, connective tissue disease, non-lymphomatous hematologic diseases, and DLBCL patients[14]. Therefore, we have demonstrated that serum IL-10 is a valuable biomarker for early diagnosis. In this study, IL-10/IL-6 concentration ratio in IVLBCL patients was significantly higher than that in FUO patients, which was similar to previous study. The ROC analysis indicated that IL-10/IL-6 concentration ratio showed excellent ability for diagnosing IVLBCL (AUC = 0.975). The internal and external validity also demonstrated its diagnostic capability (AUC = 0.976 and 0.930, respectively). However, IL-10/IL-6 concentration ratio did not significantly improve diagnostic performance compared with Model 1. Therefore, we established Model 2 incorporating the IL-10/IL-6 concentration ratio and three variables—hypoxemia, peripheral edema, and neurological symptoms—each exhibiting OR values significantly greater than 1 in Model 1. Model 2 also achieve very excellent diagnostic efficacy (AUC = 0.985) and was validated internally and externally (AUC = 0.981 and 0.956, respectively). The Delong test demonstrated that Model 2 could significantly improve the differential diagnostic ability compared to Model 1. Therefore, we proposed that for FUO patients suspected of having IVLBCL after applying Model 1, conducting IL-10 and IL-6 concentration testing and utilizing Model 2 for prediction were beneficial for the diagnosis of IVLBCL. As some medical institutions may lack the capability to test for interleukin-10 and interleukin-6, Model 1 can also be used for the diagnosis of IVLBCL in these institutions. In this study, among 60 patients with IVLBCL, patients were diagnosed by random skin biopsy (58%), bone marrow biopsy (40%) and TBLB (13.3%). This finding differed from some studies reported before. Most patients were diagnosed by bone marrow biopsy (67%) in the Murase series and by skin biopsy from positive skin lesions (38%) in the Ferreri series[6, 19]. Recently, randon skin biopsy was increasingly utilized. In the Kosei series, random skin biopsy was used as the main diagnostic method (69%), comparable to our study. In our study, the positive rate of random skin biopsy, bone marrow biopsy and TBLB were 79.5%, 42.1% and 100%, respectively. Random skin biopsy has been reported to be useful for diagnosis of IVLBCL and is now considered a standard diagnostic procedure for patients with suspected IVLBCL in Japan[4, 5, 18]. Random skin biopsy also achieved an excellent positive rate in our series and served as the main diagnostic method, which demonstrated that suspected IVLBCL should be consider to perform random skin biopsy. However, there are no clear recommendations on when to perform random skin biopsies on patients, as existing studies have not provided definitive guidance. The diagnostic models constructed in this study can help in determining the timing for random skin biopsy. When the IL-10/IL-6 ratio is elevated and models 1 and 2 yield positive diagnoses, random skin biopsies should be promptly performed for histopathological confirmation of IVLBCL. Although bone marrow biopsy achieved a relatively low positive rate (40%), it was easy to perform, minimally invasive, and the specimens are readily obtainable. 95% of our patients performed bone marrow biopsy and 40% of our 60 patients were diagnosed with it. TBLB had a positive rate of 100%, but only 8 patients performed this procedure due to the higher complication rate. We suggested that TBLB could be performed in patients with significant lung involvement. According to the findings in this study, we proposed a diagnostic procedure for IVLBCL as following (Fig. 5 ) in our institution. As the initial step, we used Model 1 including significant and easily observable clinical features to early identify IVLBCL from FUO patients. When patients were suspected by Model 1, we measured the IL-10/IL-6 concentration ratio and conducted further validation using Model 2 as the second step in the diagnostic procedure. Following a positive prediction by Model 2, we proceed the biopsy, primarily random skin biopsy, for the final diagnosis. These two models offered a more efficient and practical diagnostic procedure to identify IVLBCL in FUO patients and we have constructed a more convenient integer-based scoring system for clinical use. Our study has several limitations. was a retrospective, single institutional design study that didn’t use any novel molecular technique to help with diagnosis. Furthermore, we only externally validated the models on the temporal scale, not the spatial scale, by not externally validating the models in other centers. To completely overcome these limitations, prospective and multicenter analyses will be needed. In conclusion, the Model 1 and Model 2 performed well in the diagnosis of IVLBCL. Using these two models, the new diagnostic procedure for IVLBCL, could assist physicians and patients to better diagnose IVLBCL. With the assistance of Model 1 and Model 2, actively conducting blind random skin biopsies can lead to a rapid diagnosis of IVLBCL. Declarations Conflict of interest: The authors have no competing financial interests or other conflicts of interest to disclose. Sources of funding: Funding: This study was funded by the National High Level Hospital Clinical Research Funding 2022-PUMCH-A-192, 2022-PUMCH-B-029. Data availability: The data that support the findings of this study are available from the corresponding author upon reasonable request. Ethics approval: This study was carried out in accordance with the principles of good clinical practice and the Declaration of Helsinki, and was approved by the Institutional Review Board of Peking Union Medical College Hospital. Informed consent: All patients provided written informed consent prior to enrolment. Permission to reproduce material from other sources: The article didn't reproduce material from other sources. Author Contribution YZ and CC wrote the first draft of the manuscript. Daobin Zhou, WZ, Danqing Zhao, WW, ZZ, XL, CJ, XS participated in the diagnosis and treatment of the patient, and providing follow-up. ZL and YD acquired and analysed clinical data. All authors contributed to the article and approved the submitted version. Acknowledgements: We thank all the clinicians for providing care and management to the patients. References Nakamura S: Intravascular large B-cell lymphoma . World Health Organization Classification of Tumours, WHO Classification of Tumours of Haematopoietic and Lymphoid Tissues 2008:252-253. Ponzoni M, Ferreri AJ, Campo E, Facchetti F, Mazzucchelli L, Yoshino T, Murase T, Pileri SA, Doglioni C, Zucca E et al : Definition, diagnosis, and management of intravascular large B-cell lymphoma: proposals and perspectives from an international consensus meeting. J Clin Oncol 2007, 25(21):3168-3173. 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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-4441204","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":309695933,"identity":"23ed670a-0890-4080-83db-59772b83745e","order_by":0,"name":"Chao Chen","email":"","orcid":"","institution":"Peking Union Medical College Hospital","correspondingAuthor":false,"prefix":"","firstName":"Chao","middleName":"","lastName":"Chen","suffix":""},{"id":309695934,"identity":"8fbc0850-2146-4687-a8e6-9c8e284176e0","order_by":1,"name":"Yiao Di","email":"","orcid":"","institution":"Peking Union Medical College 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Hospital","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Wang","suffix":""},{"id":309695941,"identity":"2f73cdbe-4c3e-49d2-84a8-9876bad387d0","order_by":8,"name":"Wei Zhang","email":"","orcid":"","institution":"Peking Union Medical College Hospital","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Zhang","suffix":""},{"id":309695942,"identity":"1f4c3f8d-1415-4b07-8c78-4e955cef41c6","order_by":9,"name":"Daobin Zhou","email":"","orcid":"","institution":"Peking Union Medical College Hospital","correspondingAuthor":false,"prefix":"","firstName":"Daobin","middleName":"","lastName":"Zhou","suffix":""},{"id":309695943,"identity":"3e69f750-0859-47fd-bc05-fba6e91dd79d","order_by":10,"name":"Yan Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+0lEQVRIiWNgGAWjYDACCSjNxsx8/MOHChCTh0gtfOxtaYwzzpCiRY7njBozbxsRWuRnNz97+LXNLo9NIoftMe+8O4lr288eYPi5A7cWxjnHzI1l25KL2SRyjxvO3fYscduZvATG3jO4tTBLJJhJS7YxJ7ZJ5CVIvN12OHHbDR4DZsY23FrYJNK/AbXUA7XkGEjwziFCC49Ejpnkx7bDiW08Z8wkeRuI0CIhkVMmzXDueGIbe1uy4Yxjz4y3nckxONiLR4v8jPRtkj/KqhPnNzMffPCh5o7stuNnDB/8xKMFHAS8bHD2ASQSD2D88QdNyygYBaNgFIwCZAAA2RBXkCpbM1wAAAAASUVORK5CYII=","orcid":"","institution":"Peking Union Medical College Hospital","correspondingAuthor":true,"prefix":"","firstName":"Yan","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2024-05-18 13:27:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4441204/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4441204/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":57947790,"identity":"59309e7e-1f7a-4b76-a8d0-953d600f8930","added_by":"auto","created_at":"2024-06-07 20:26:27","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":397078,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlowchart outlining the study\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4441204/v1/d427134f5dd5e3c48d627aae.jpg"},{"id":57947791,"identity":"fc077c86-96a1-45ef-95d7-d9c84eb7247d","added_by":"auto","created_at":"2024-06-07 20:26:27","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":203692,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe ROC curves of IL-10/IL-6, Model 1 and Model 2 applying in training cohort. The areas under the ROC curves for the ability to differentiate IVLBCL from FUO for\u003c/strong\u003e Model 1, IL-10/IL-6, and Model 2 \u003cstrong\u003ewere 0.928, 0.975 and 0.982, respectively.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4441204/v1/18e3d25f889f61ad26e6cea1.jpg"},{"id":57947795,"identity":"f83a86bc-10f5-464f-a3aa-09a3f43ce45a","added_by":"auto","created_at":"2024-06-07 20:26:27","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":195715,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe ROC curves of IL-10/IL-6, Model 1 and Model 2 applying in validation cohort. The areas under the ROC curves for the ability to differentiate IVLBCL from FUO for Model 1, IL-10/IL-6, and Model 2 were 0.909, 0.930 and 0.956, respectively.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4441204/v1/54eacdd702961ab778b81287.jpg"},{"id":57947794,"identity":"9c7ae5d4-d4f1-4580-b901-e3164e48740f","added_by":"auto","created_at":"2024-06-07 20:26:27","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":373999,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eInteger-based scoring systems generated from Model 1 and Model 2.\u003c/strong\u003e (A and B) The score of each factor is multiplied by the number of units that the parameter has and the sum of these scores is calculated as the total score. (C and D) The relations between the integer-based scores and the predictive diagnostic yields are presented. (E) \u003cstrong\u003eThe ROC curves of integer-based scoring systems applying in training cohort. (F) The ROC curves of integer-based scoring systems applying in validation cohort.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Onlinefloatimage4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4441204/v1/a3401b4b7d5a7f5cdc93fb9f.jpg"},{"id":57947793,"identity":"7dbce451-6ab1-4e95-b463-f21cc948b21c","added_by":"auto","created_at":"2024-06-07 20:26:27","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":199592,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDiagnostic procedure for IVLBCL. Firstly, Model 1 was used to early identify IVLBCL from FUO patients. Secondly, examining IL-10 and IL-6 concentration, and validate using Model 2. Following a positive prediction by Model 2, biopsy, including random skin biopsy, bone marrow biopsy and TBLB, was processed for the final diagnosis.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Onlinefloatimage5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4441204/v1/53d6aa1b2049ac1f91fdf900.jpg"},{"id":57949142,"identity":"cf96adf7-e2ba-4823-89d4-d94b1f5aeef5","added_by":"auto","created_at":"2024-06-07 20:42:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2193486,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4441204/v1/1bbabf18-addd-4942-b031-84c9bca6412b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Diagnostic predictive model for distinguishing intravascular large B-cell lymphoma among patients with fever of unknown origin","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIntravascular large B-cell lymphoma (IVLBCL) is a relatively rare and distinct type of extranodal diffuse large B-cell lymphoma. IVLBCL is characterized by the predominant growth of large cells within the lumen of different-sized blood vessels (particularly capillaries)[1]. Because neoplastic cells of IVLBCL usually do not circulate in the peripheral blood, do not form an extravascular tumor mass[2, 3], and due to the near-ubiquitous presence of its neoplastic cells, its clinical presentation is highly heterogeneous. In Asia, fever of unknown origin (FUO) is the most common systemic symptom (87–100%) and is typically the primary symptom during the initial medical consolations[4–6]. Other symptoms of IVLBCL are based on the involvement sites. The common involvement sites of IVLBCL are CNS, skin and lung. Therefore, heterogenous neurological symptoms, different kinds of skin lesions and pulmonary symptoms are frequent[7, 8]. Furthermore, some patients also displayed typical clinical hemophagocytic syndrome and presented with peripheral edema[9–11]. Therefore, the diagnosis of IVLBCL is often tricky and requires histological confirmation. In two retrospective studies conducted in Japan and Western countries with review periods exceeding ten years, the proportion of patients diagnosed with IVLBCL through autopsy was 16% and 24%, respectively[12, 13]. This indicates the diagnostic challenge of IVLBCL. IVLBCL is an aggressive disease, delayed diagnosis could result in terrible outcomes. We should find a new method to identify IVLBCL early and establish an improved diagnostic procedure for earlier intervention. We have previously reported that IL-10 was a valuable biomarker for early diagnosis for IVLBCL[14].\u003c/p\u003e \u003cp\u003eBecause of the fact that the majority of IVLBCL patients present with FUO as their initial symptom, this study aimed to construct diagnostic prediction models capable of identifying IVLBCL from FUO and analyze the different biopsy methods employed in the patients. Through this study, we aimed to summarize a practical and accurate diagnostic procedure for IVLBCL.\u003c/p\u003e "},{"header":"Methods","content":"\u003cp\u003ePatients\u003c/p\u003e\u003cp\u003e The local ethical review boards approved this retrospective cohort study, conducted at Peking Union Medical College Hospital between February 2015 and October 2023. All patients were informed, and the procedure was performed under the Declaration of Helsinki. This study included two cohorts: a modling cohort and a validation cohort, comprising 60 IVLBCL patients and 66 FUO patients. The training cohort consisted of 42 IVLBCL patients and 45 FUO patients diagnosed between February 2015 and November 2021. The validation cohort consisted of 18 IVLBCL patients and 21 FUO patients diagnosed between November 2021 and October 2023. Eligibility criteria for inclusion of IVLBCL patients within the study were: (i) had clinical presentation of FUO; (ii) histologically confirmed IVLBCL; (iii) aged 18–79 years. Eligibility criteria for inclusion of FUO patients within the study were: (i) unexplained fever persisted more than 3 weeks; (ii) aged 18–79 years; (iii) excluded of IVLBCL diagnosis.\u003c/p\u003e\u003cp\u003ePatient characteristics, including neurological symptoms, and laboratory data, including hematological, biochemical, and immunological findings were collected from electronic medical records. Imaging reports of baseline chest computed tomography before treatment were reviewed. Pathological data of all biopsied specimens from skin, lung, and bone marrow of IVLBCL patients were also collected. The dermatology department performed random skin biopsy and specimens were usually obtained from 3 separate, fat-containing areas of the skin. In the characteristics, peripheral edema was defined as abnormal accumulation of fluid in the peripheral tissues, typically in the extremities. Hypoxemia was defined as PaO\u003csub\u003e2\u003c/sub\u003e below 60mmHg at the room air. Skin rash was characterized by redness, itching, inflammation, blistering, or other alterations in the skin. HLH was diagnosed according to HLH-2004 Diagnostic Criteria[15]. Iinterstitial lung abnormality (ILA) is characterized by CT scan findings that include ground-glass or reticular abnormalities, lung distortion, traction bronchiectasis, honeycombing, and non-emphysematous cysts[16].\u003c/p\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates a flowchart outlining the study. Data were analyzed with the Statistical Package for Social Sciences, version 27.0 (SPSS Inc., Chicago, IL, USA). Mann-Whitney U test was used to compare median values of data that were not normally distributed within the training cohort. Chi-square test was used to compare the categorical variable. The area under the receiver operating characteristic (ROC) curve was calculated to assess the predictive value of IL-10/IL-6 concentration ratio. The multivariate logistic regression analysis was used to construct diagnostic Model 1 and Model 2 for diagnosis of IVLBCL from FUO. The Delong test was used to compare diagnostic models and IL-10/IL-6 concentration ratio with the highest area under the curve (AUC). The leave-one-out (LOO) analysis was performed to internal validate the diagnostic performance within the training cohort in which models were generated. The analysis was performed for Model 1 and 2 and IL-10/IL-6 concentration ratios. Model 1, Model 2 and IL-10/IL-6 ratio were applied to the validation cohort to examine the diagnostic ability of the models generated from the training cohort.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eClinical characteristics\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e showed the training cohort's baseline characteristics containing 42 IVLBCL patients and 45 FUO patients. The median age (58.5 vs 56, \u003cem\u003ep\u003c/em\u003e = 0.13) and gender distribution (male, 54.8% vs 51.1%, \u003cem\u003ep\u003c/em\u003e = 0.727) were comparable between patients with IVLBCL and FUO. The occurrence of peripheral edema, hypoxemia, neurological symptoms, and hemophagocytic lymphohistiocytosis was observed to be significantly higher in patients with IVLBCL than those with FUO. The most frequent neurological symptoms of IVLBCL patients were extremity weakness (11, 26.2%) and cognitive impairment (6, 14.3%). Additionally, the CT scans revealed a higher incidence of ILA in IVLBCL patients. And the assessment of serum IL-10 concentration as well as the IL-10/IL-6 concentration ratio in IVLBCL patients was significantly greater than those in FUO patients. In these 45 FUO patients, 14 were finally diagnosed as infectious disease, 13 were connective tissue disease, 10 were hematological disease, 5 were tumor and 3 were other disease.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of patients in the training cohort.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIVLBCL\u003c/p\u003e \u003cp\u003e(n = 42)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFUO\u003c/p\u003e \u003cp\u003e(n = 45)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, median (range), years\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58.5 (32 to 76)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56 (21 to 73)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex, n (%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 (54.8%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (51.1%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.727\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19 (45.2%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (48.9%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeripheral edema, n (%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27 (64.3%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (8.9%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypoxemia, n (%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33 (78.6%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (11.1%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSkin rash, n (%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (47.6%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (34.0%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.193\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeurological symptoms, n (%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 (54.8%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (17.8%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExtremity weakness\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (26.2%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (4.4%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCognitive impairment\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (14.3%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParaesthesia\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (9.5%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (6.7%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSlurred speech\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (7.1%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (2.2%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisorder of consciousness\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (7.1%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (11.1%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEpilepsy\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (7.1%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVisual disturbances\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (2.4%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHearing impairment\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (2.4%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemophagocytic lymphohistiocytosis, n (%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (40.5%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (11.1%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCT showed ILA, n (%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27 (64.3%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (26.7%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL-10 concentration, median (range), pg/mL\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e182 (\u0026lt; 5 to \u0026gt; 1000)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (\u0026lt; 5 to 208)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL-10/IL-6, median (range)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.5 (0 to 103.1)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0 to 2.3)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003eDiagnostic Model Generation\u003c/p\u003e\u003cp\u003eWe constructed two diagnostic models for IVLBCL based on multivariate logistic regression analysis (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Model 1 select five commonly observed clinical parameters as explanatory variables, including peripheral edema, hypoxemia, neurological symptoms, HLH, and ILA. In the predictive logistic Model 1, significant factors affecting the diagnostic yield were peripheral edema (vs. negative) (odds ratio [OR] = 9.093; 95% CI = 1.960-51.105; \u003cem\u003ep\u003c/em\u003e = 0.007), hypoxemia (vs. negative) (OR = 17.371; 95% CI = 3.725-108.101; \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001) and neurological symptoms (vs. negative) (OR = 4.748; 95% CI = 1.128–22.154; \u003cem\u003ep\u003c/em\u003e = 0.037). Univariate ROC analysis indicated that the IL-10/IL-6 concentration ratio showed a high differentiating diagnostic value in training cohort with a cut-off value of 0.400 (AUC = 0.975, sensitivity = 0.976, specificity = 0.915). Model 2 incorporates serum IL-10 concentration along with commonly observable clinical symptoms—peripheral edema, hypoxemia and neurological symptoms—as four explanatory variables. In Model 2, significant factor was IL-10/IL-6 concentration ratio (OR = 6.798; 95% CI = 2.013–51.698; \u003cem\u003ep\u003c/em\u003e = 0.020) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The ROC AUC of Model 1 and Model 2 for estimating the diagnostic efficiency were 0.928 and 0.982, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The DeLong test showed that the AUC of the Model 2 was better than Model 1 (\u003cem\u003ep\u003c/em\u003e = 0.023).\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eModel 1 and Model 2 generated by multivariate logistic regression analysis. Model 1 select five clinical parameters including peripheral edema, hypoxemia, neurological symptoms, HLH, and ILA. Model 2 incorporates serum IL-10 concentration with three clinical parameters including peripheral edema, hypoxemia and neurological symptoms.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel 1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-2.726\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.065 (0.017–0.192)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeripheral edema\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.208\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.093 (1.960-51.105)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypoxemia\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.855\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.371 (3.725-108.101)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeurological symptoms\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.558\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.748 (1.128–22.154)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHLH\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.604\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.828 (0.270-12.588)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.532\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCT showed ILA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.165\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.180 (0.205–5.870)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.843\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel 2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-5.353\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.005 (0.000-0.046)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeripheral edema\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.192\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.955 (0.620-176.411)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.106\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypoxemia\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.967\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.140 (0.454-162.013)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.156\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeurological symptoms\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.027\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.588 (0.571–199.940)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.143\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL-10 / IL-6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.917\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.798 (2.013–51.698)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDifferential diagnostic efficiency of IL-10/IL-6 concentration ratio, Model 1 and Model 2. AUC was generated by ROC analysis. AUC, area under curve; ROC, receiver operating characteristic.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCut-off value\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAUC (95%CI)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL-10/IL-6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.400\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.975 (0.944-1.000)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.976\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.915\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.553\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.928 (0.871–0.985)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.857\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.915\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.652\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.982 (0.947-1.000)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.976\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003eInternal validation of LR models\u003c/p\u003e\u003cp\u003eInternal validation of IL-10/IL-6 ROC analysis, Model 1 and Model 2 was evaluated using the Leave-One-Out cross-validation. The results of LOO analysis indicate that the mean square error of IL-10/IL-6, Model 1 and Model 2 were 0.068, 0.121, 0.032, respectively. Furthermore, the mean AUC of IL-10/IL-6, Model 1 and Model 2 were 0.976, 0.888 and 0.981, respectively.\u003c/p\u003e\u003cp\u003eExternal validation of LR models\u003c/p\u003e\u003cp\u003eThe external validation of IL-10/IL-6 ROC analysis, Model 1, and Model 2 was conducted by applying them to the independent validation cohort including 18 patients with IVLBCL and 21 patients with FUO (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Using the IL-10/IL-6 model yielded an AUC of 0.930 in the validation cohort, with no significant difference compared to the AUC obtained in the training cohort (\u003cem\u003ep\u003c/em\u003e = 0.345). Additionally, at a cut-off value of 0.400, it demonstrated a sensitivity of 0.944 and a specificity of 0.714. When using Model 1, the AUC was 0.909, with no significant difference from the AUC in the training cohort (\u003cem\u003ep\u003c/em\u003e = 0.716). At a cut-off of 0.553, the sensitivity was 0.778, and the specificity was 0.857. Using Model 2, the AUC was 0.956, with no significant difference from the AUC in the training cohort (\u003cem\u003ep\u003c/em\u003e = 0.462). At a cut-off of 0.652, the sensitivity was 0.944, and the specificity was 0.857.\u003c/p\u003e\u003cp\u003eInteger-Based Scoring System\u003c/p\u003e\u003cp\u003eTo create the integer-based scoring system for clinical practice, we calculated the coefficient of each variable as a simple integer ratio from Model 1 and Model 2 above. The coefficients of each variable in Model 1 and Model 2 are listed in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb. At the same time, the relations between the integer-based scores and predictive diagnostic yields are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed. The ROC AUC of the integer-based scoring system of Model 1 and Model 2 for estimating the diagnostic yield in the training cohort were 0.919 and 0.938, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). In the validation cohort, the ROC AUC of the integer-based scoring system of Model 1 and Model 2 were 0.896 and 0.955, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF).\u003c/p\u003e\u003cp\u003eBiopsy confirmation\u003c/p\u003e\u003cp\u003eAll 60 patients with IVLBCL in training cohort and validation cohort were diagnosed through a random skin biopsy, bone marrow biopsy and/or TBLB (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Most patients received bone marrow biopsy, but the positive rate was only 42.1%. Forty-four patients received random skin biopsy and the positive rate was 79.5%. Only 8 patients underwent TBLB, all of whom presented with hypoxemia and exhibited ILA on CT scans, and all these 8 patients were diagnosed with IVLBCL through TBLB. Among patients diagnosed with IVLBCL by Model 1 and Model 2, the positivity rates of random skin biopsy were 80.0% and 81.0%, respectively.\u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe pathological diagnosis of IVLBCL.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiopsy type\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRandom skin biopsy\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBone Marrow Biopsy\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTBLB\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatients receive biopsy, n (% of all patients)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e44 (73.3%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e57 (95.0%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (13.3%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatients diagnosed by biopsy, n (% of all patients)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35 (58.3%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24 (40.0%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (13.3%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive rate\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e79.5%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42.1%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eDue to the absence of circulating neoplastic cells on peripheral blood smear[17] and the lack of any specific signs and symptoms[9], the IVLBCL diagnosis is highly challenging. FUO is the most common systemic symptom and is typically the primary symptom during the initial medical consultation[4, 18]. Therefore, in our study we established two diagnostic models to identify IVLBCL from FUO quickly and accurately. Besides, we demonstrated that IL-10/IL-6 concentration ratio could improve the ability of diagnosis of IVLBCL.\u003c/p\u003e \u003cp\u003eModel 1 was established based on the presence of peripheral edema, hypoxemia, neurological symptoms, HLH, and ILA in CT. These variables were easily observable and detectable. In training cohort, hypoxemia, ILA in CT, peripheral edema, HLH and neurological symptoms, such as extremity weakness and cognitive impairment, were significantly more frequent in IVLBCL patients than FUO patients. These findings were consistent with the pathological physiology of IVLBCL[9]. However, there was no significant difference in the incidence of skin rash between IVLBCL and FUO patients. Due to the significant differences in these easily observable and detectable clinical variables, Model 1 was established. Model 1 exhibited good ability to diagnose IVLBCL (AUC\u0026thinsp;=\u0026thinsp;0.975, sensitivity\u0026thinsp;=\u0026thinsp;0.976 and specificity\u0026thinsp;=\u0026thinsp;0.915). The internal and external validity of Model 1 also demonstrated its diagnostic capability (AUC\u0026thinsp;=\u0026thinsp;0.888 and 0.909, respectively). Based on this, we proposed that using Model 1 for the assessment of patients at their initial visit for FUO is a convenient and effective preliminary diagnostic procedure for IVLBCL.\u003c/p\u003e \u003cp\u003eA challenge to the diagnosis of IVLBCL is its lack of specific tumor markers. We previously reported that in IVLBCL, serum IL-10 levels were significantly higher than those in FUO, connective tissue disease, non-lymphomatous hematologic diseases, and DLBCL patients[14]. Therefore, we have demonstrated that serum IL-10 is a valuable biomarker for early diagnosis. In this study, IL-10/IL-6 concentration ratio in IVLBCL patients was significantly higher than that in FUO patients, which was similar to previous study. The ROC analysis indicated that IL-10/IL-6 concentration ratio showed excellent ability for diagnosing IVLBCL (AUC\u0026thinsp;=\u0026thinsp;0.975). The internal and external validity also demonstrated its diagnostic capability (AUC\u0026thinsp;=\u0026thinsp;0.976 and 0.930, respectively). However, IL-10/IL-6 concentration ratio did not significantly improve diagnostic performance compared with Model 1.\u003c/p\u003e \u003cp\u003eTherefore, we established Model 2 incorporating the IL-10/IL-6 concentration ratio and three variables\u0026mdash;hypoxemia, peripheral edema, and neurological symptoms\u0026mdash;each exhibiting OR values significantly greater than 1 in Model 1. Model 2 also achieve very excellent diagnostic efficacy (AUC\u0026thinsp;=\u0026thinsp;0.985) and was validated internally and externally (AUC\u0026thinsp;=\u0026thinsp;0.981 and 0.956, respectively). The Delong test demonstrated that Model 2 could significantly improve the differential diagnostic ability compared to Model 1. Therefore, we proposed that for FUO patients suspected of having IVLBCL after applying Model 1, conducting IL-10 and IL-6 concentration testing and utilizing Model 2 for prediction were beneficial for the diagnosis of IVLBCL. As some medical institutions may lack the capability to test for interleukin-10 and interleukin-6, Model 1 can also be used for the diagnosis of IVLBCL in these institutions.\u003c/p\u003e \u003cp\u003eIn this study, among 60 patients with IVLBCL, patients were diagnosed by random skin biopsy (58%), bone marrow biopsy (40%) and TBLB (13.3%). This finding differed from some studies reported before. Most patients were diagnosed by bone marrow biopsy (67%) in the Murase series and by skin biopsy from positive skin lesions (38%) in the Ferreri series[6, 19]. Recently, randon skin biopsy was increasingly utilized. In the Kosei series, random skin biopsy was used as the main diagnostic method (69%), comparable to our study. In our study, the positive rate of random skin biopsy, bone marrow biopsy and TBLB were 79.5%, 42.1% and 100%, respectively. Random skin biopsy has been reported to be useful for diagnosis of IVLBCL and is now considered a standard diagnostic procedure for patients with suspected IVLBCL in Japan[4, 5, 18]. Random skin biopsy also achieved an excellent positive rate in our series and served as the main diagnostic method, which demonstrated that suspected IVLBCL should be consider to perform random skin biopsy. However, there are no clear recommendations on when to perform random skin biopsies on patients, as existing studies have not provided definitive guidance. The diagnostic models constructed in this study can help in determining the timing for random skin biopsy. When the IL-10/IL-6 ratio is elevated and models 1 and 2 yield positive diagnoses, random skin biopsies should be promptly performed for histopathological confirmation of IVLBCL. Although bone marrow biopsy achieved a relatively low positive rate (40%), it was easy to perform, minimally invasive, and the specimens are readily obtainable. 95% of our patients performed bone marrow biopsy and 40% of our 60 patients were diagnosed with it. TBLB had a positive rate of 100%, but only 8 patients performed this procedure due to the higher complication rate. We suggested that TBLB could be performed in patients with significant lung involvement.\u003c/p\u003e \u003cp\u003eAccording to the findings in this study, we proposed a diagnostic procedure for IVLBCL as following (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) in our institution. As the initial step, we used Model 1 including significant and easily observable clinical features to early identify IVLBCL from FUO patients. When patients were suspected by Model 1, we measured the IL-10/IL-6 concentration ratio and conducted further validation using Model 2 as the second step in the diagnostic procedure. Following a positive prediction by Model 2, we proceed the biopsy, primarily random skin biopsy, for the final diagnosis. These two models offered a more efficient and practical diagnostic procedure to identify IVLBCL in FUO patients and we have constructed a more convenient integer-based scoring system for clinical use.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOur study has several limitations. was a retrospective, single institutional design study that didn\u0026rsquo;t use any novel molecular technique to help with diagnosis. Furthermore, we only externally validated the models on the temporal scale, not the spatial scale, by not externally validating the models in other centers. To completely overcome these limitations, prospective and multicenter analyses will be needed.\u003c/p\u003e \u003cp\u003eIn conclusion, the Model 1 and Model 2 performed well in the diagnosis of IVLBCL. Using these two models, the new diagnostic procedure for IVLBCL, could assist physicians and patients to better diagnose IVLBCL. With the assistance of Model 1 and Model 2, actively conducting blind random skin biopsies can lead to a rapid diagnosis of IVLBCL.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eConflict of interest: The authors have no competing financial interests or other conflicts of interest to disclose.\u003c/p\u003e\n\u003cp\u003eSources of funding:\u0026nbsp;Funding:\u0026nbsp;This study was funded by the National High Level Hospital Clinical Research Funding 2022-PUMCH-A-192, 2022-PUMCH-B-029.\u003c/p\u003e\n\u003cp\u003eData availability: The data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003eEthics approval: This study was carried out in accordance with the principles of good clinical practice and the Declaration of Helsinki, and was approved by the Institutional Review Board of Peking Union Medical College Hospital.\u003c/p\u003e\n\u003cp\u003eInformed consent:\u0026nbsp;All patients provided written informed consent prior to enrolment.\u003c/p\u003e\n\u003cp\u003ePermission to reproduce material from other sources: The article didn\u0026apos;t reproduce material from other sources.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eYZ and CC wrote the first draft of the manuscript. Daobin Zhou, WZ, Danqing Zhao, WW, ZZ, XL, CJ, XS participated in the diagnosis and treatment of the patient, and providing follow-up. ZL and YD acquired and analysed clinical data. All authors contributed to the article and approved the submitted version.\u003c/p\u003e\u003ch2\u003eAcknowledgements:\u003c/h2\u003e \u003cp\u003eWe thank all the clinicians for providing care and management to the patients.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eNakamura S: \u003cstrong\u003eIntravascular large B-cell lymphoma\u003c/strong\u003e. World Health Organization Classification of Tumours, WHO Classification of Tumours of Haematopoietic and Lymphoid Tissues 2008:252-253.\u003c/li\u003e\n\u003cli\u003ePonzoni M, Ferreri AJ, Campo E, Facchetti F, Mazzucchelli L, Yoshino T, Murase T, Pileri SA, Doglioni C, Zucca E\u003cem\u003e et al\u003c/em\u003e: Definition, diagnosis, and management of intravascular large B-cell lymphoma: proposals and perspectives from an international consensus meeting. \u003cem\u003eJ Clin Oncol \u003c/em\u003e2007, 25(21):3168-3173.\u003c/li\u003e\n\u003cli\u003eShimada K, Kinoshita T, Naoe T, Nakamura S: \u003cstrong\u003ePresentation and management of intravascular large B-cell lymphoma\u003c/strong\u003e. \u003cem\u003eThe lancet oncology \u003c/em\u003e2009, \u003cstrong\u003e10\u003c/strong\u003e(9):895-902.\u003c/li\u003e\n\u003cli\u003eMatsue K, Abe Y, Narita K, Kobayashi H, Kitadate A, Takeuchi M, Miura D, Takeuchi K: Diagnosis of intravascular large B cell lymphoma: novel insights into clinicopathological features from 42 patients at a single institution over 20 years. \u003cem\u003eBritish Journal of Haematology \u003c/em\u003e2019, 187(3):328-336.\u003c/li\u003e\n\u003cli\u003eAsada N, Odawara J, Kimura S, Aoki T, Yamakura M, Takeuchi M, Seki R, Tanaka A, Matsue K: \u003cstrong\u003eUse of random skin biopsy for diagnosis of intravascular large B-cell lymphoma\u003c/strong\u003e. \u003cem\u003eMayo Clin Proc \u003c/em\u003e2007, \u003cstrong\u003e82\u003c/strong\u003e(12):1525-1527.\u003c/li\u003e\n\u003cli\u003eMurase T, Yamaguchi M, Suzuki R, Okamoto M, Sato Y, Tamaru J, Kojima M, Miura I, Mori N, Yoshino T\u003cem\u003e et al\u003c/em\u003e: Intravascular large B-cell lymphoma (IVLBCL): a clinicopathologic study of 96 cases with special reference to the immunophenotypic heterogeneity of CD5. \u003cem\u003eBlood \u003c/em\u003e2007, 109(2):478-485.\u003c/li\u003e\n\u003cli\u003eDavis JW, Auerbach A, Crothers BA, Lewin E, Lynch DT, Teschan NJ, Schmieg JJ, III: Intravascular Large B-Cell Lymphoma: A Brief Review of Current and Historic Literature and a Cautionary Tale of Pertinent Pitfalls. \u003cem\u003eArchives of Pathology \u0026amp; 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M, Egeler RM, Filipovich AH, Imashuku S, Ladisch S, McClain K, Webb D, Winiarski J\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eHLH-2004: Diagnostic and therapeutic guidelines for hemophagocytic lymphohistiocytosis\u003c/strong\u003e. \u003cem\u003ePediatr Blood Cancer \u003c/em\u003e2007, \u003cstrong\u003e48\u003c/strong\u003e(2):124-131.\u003c/li\u003e\n\u003cli\u003eHatabu H, Hunninghake GM, Richeldi L, Brown KK, Wells AU, Remy-Jardin M, Verschakelen J, Nicholson AG, Beasley MB, Christiani DC\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eInterstitial lung abnormalities detected incidentally on CT: a Position Paper from the Fleischner Society\u003c/strong\u003e. \u003cem\u003eLancet Respir Med \u003c/em\u003e2020, \u003cstrong\u003e8\u003c/strong\u003e(7):726-737.\u003c/li\u003e\n\u003cli\u003eCerroni L, Massone C, Kutzner H, Mentzel T, Umbert P, Kerl H: Intravascular large T-cell or NK-cell lymphoma: a rare variant of intravascular large cell lymphoma with frequent cytotoxic phenotype and association with Epstein-Barr virus infection. \u003cem\u003eAm J Surg Pathol \u003c/em\u003e2008, 32(6):891-898.\u003c/li\u003e\n\u003cli\u003ePongpudpunth M, Rattanakaemakorn P, Fleischer AB, Jr.: Usefulness of Random Skin Biopsy as a Diagnostic Tool of Intravascular Lymphoma Presenting With Fever of Unknown Origin. \u003cem\u003eAm J Dermatopathol \u003c/em\u003e2015, 37(9):686-690.\u003c/li\u003e\n\u003cli\u003eFerreri AJ, Dognini GP, Campo E, Willemze R, Seymour JF, Bairey O, Martelli M, De Renz AO, Doglioni C, Montalb\u0026aacute;n C\u003cem\u003e et al\u003c/em\u003e: Variations in clinical presentation, frequency of hemophagocytosis and clinical behavior of intravascular lymphoma diagnosed in different geographical regions. \u003cem\u003eHaematologica \u003c/em\u003e2007, 92(4):486-492.\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":"Intravascular large B-cell lymphoma, logistic regression, fever of unknown origin","lastPublishedDoi":"10.21203/rs.3.rs-4441204/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4441204/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIntravascular large B-cell lymphoma (IVLBCL) is relatively rare. Due to the lack of distinctive symptoms, the diagnosis is difficult. Fever of unknown origin (FUO) is the most common symptom. This study retrospectively analyzed IVLBCL patients and FUO patients between February 2015 and October 2023. Diagnostic predictive models were constructed using multivariable logistic regression in the training cohort including 42 IVLBCL patients and 45 FUO patients. Model 1 consisted of peripheral edema, hypoxemia, neurological symptoms, hemophagocytic lymphohistiocytosis, and interstitial lung abnormalities on CT, exhibited good diagnostic efficiency (AUC\u0026thinsp;=\u0026thinsp;0.928). Model 2 consisted of peripheral edema, hypoxemia, neurological symptoms and Interleukin-10/Interleukin-6 concentration ratio, showed a higher diagnostic ability than Model 1 (AUC\u0026thinsp;=\u0026thinsp;0.982, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.023). The diagnostic abilities of models were validated internally in leave-one-out validation and externally in the validation cohort. As some medical institutions may lack the capability to test for interleukin concentration, Model 1 can be used to help the diagnosis of IVLBCL in these institutions. Based on Model 1 and Model 2, we established and validated two integer-based scoring systems for clinical practice. Most patients (58%) were histologically diagnosed by random skin biopsy, and the positive rate of random skin biopsy was 79.5%.\u003c/p\u003e","manuscriptTitle":"Diagnostic predictive model for distinguishing intravascular large B-cell lymphoma among patients with fever of unknown origin","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-07 20:26:22","doi":"10.21203/rs.3.rs-4441204/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":"e314ac23-a539-4d4d-9555-f1b06762fa97","owner":[],"postedDate":"June 7th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-06-07T20:26:25+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-07 20:26:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4441204","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4441204","identity":"rs-4441204","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}