Complicated silicosis due to engineered stone: High metabolic activity in positron emission tomography and systemic inflammation years after exposure cessation

Complicated silicosis due to engineered stone: High metabolic activity in positron emission tomography and systemic inflammation years after exposure cessation | 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 Complicated silicosis due to engineered stone: High metabolic activity in positron emission tomography and systemic inflammation years after exposure cessation León-Jiménez Antonio, Rodríguez-Rubio Corona Julio, Jiménez-Gómez Gema, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5879579/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract Engineered stone silicosis is an interstitial lung disease that progresses rapidly causing, in many cases, respiratory insufficiency and death. Metabolic activity in lungs and adenopathies and its relationships with systemic inflammation are unknown. Patients with complicated silicosis were enrolled. All had worked for at least 5 years in finishing and installing engineered stone and had ceased exposure for at least 7 years. Clinical data, positron emission tomography/computed tomography using 18 F-fluorodeoxyglucose ( 18 F-FDG PET/CT), respiratory function tests and blood samples were collected. Patients’ mean age was 44 ± 5.4 years. The average exposure duration was 10.94 ± 3.2. Years from cessation of exposure was 11.6 ± 1.6. The average maximum standardized uptake value (SUVmax) of large opacities was 6.32 ± 3. All patients presented hypermetabolic mediastinal lymphadenopathies and 88.2% also extrathoracic lymphadenopathies. SUV max of large opacities was correlated with Fibrinogen (ρ = 0.717, P = 0.001), lymphocyte-to-monocyte ratio (ρ = -0.506, P = 0.038), systemic inflammatory response index (ρ = 0.559, P = 0.02) and CD4 + NKT cells. Large lung opacities and lymphadenopathies showed high metabolic activity even years after silica exposure ended. The relationships between metabolic activity and some inflammatory factors open a pathway for exploring new therapeutic targets. Health sciences/Medical research/Biomarkers/Predictive markers Health sciences/Medical research/Biomarkers/Prognostic markers Health sciences/Diseases/Respiratory tract diseases Silicosis Engineered stone Systemic inflammatory indices Lymphocyte subsets Positron emission tomography Introduction Engineered stone silicosis is a serious occupational health issue with a high number of cases in many countries, such as Australia, Spain, Israel and the United States [ 1 – 3 ], among others, due to the emergence of a new material used in bathroom and kitchen countertops. This high incidence has even resulted in the prohibition of this material in Australia and proposals for its gradual ban in other European countries [ 4 ]. This engineered stone (ES), commonly known as artificial stone, quartz or silica agglomerates, is characterized by a high content of micronized crystalline silica (over 80% quartz and/or cristobalite), along with resins and metals [ 5 , 6 ]. Compared with natural stones, artificial silica agglomerates induce a more aggressive form of silicosis with higher mortality [ 7 ], and the disease can progress rapidly even after exposure has ceased [ 8 ]. Silicosis is a progressive interstitial lung disease with no specific treatment except for lung transplantation in its final stages. Despite numerous studies, most of which have been conducted in animal models and cell cultures [ 9 ], the underlying mechanisms and cellular processes involved in disease progression remain largely unknown. Since activated inflammatory cells consume glucose for energy and increase glucose transporter expression [ 10 ], positron emission tomography/computed tomography (PET/CT) with 18 F-fluorodeoxyglucose ( 18 F-FDG) may provide new insights into the role of biomarkers and inflammatory cells involved in the progression of silicosis in patients. The aim of our study was to assess the metabolic activity of lung lesions and lymph nodes in patients with complicated silicosis due to ES and to investigate their relationships with specific biomarkers, systemic inflammatory indices, and lymphocyte subpopulations in peripheral blood. Results Silicotic conglomerates and metabolic activity Radiological evaluation by HRCT classified patients according to the ICOERD classification, with five patients categorized as PMF category A, six as category B, and six as category C. All patients presented bilateral large opacities except for one patient who had a unilateral large opacity. The silicotic conglomerates (large opacities) with the highest uptake were mostly located in the upper lobes. From each lung, the region with the highest SUVmax was selected. The average SUVmax of these pulmonary opacities was 6.32 ± 3.04, with limits of 13.73 and 2.27. The values for each patient and the diameter of each opacity measured in millimeters (mm) by HRCT are shown in Table 1 . Table 1 Relationships between radiological opacities in the lung and their corresponding metabolic activity measured by SUVmax. PATIENT ILO/ICOERD CATEGORY DIAMETER RIGHT LUNG OPACITY DIAMETER LEFT LUNG OPACITY AVERAGE DIAMETER PULMONARY OPACITIES LOCATION SUVmax RIGHT LUNG OPACITY SUVmax LEFT LUNG OPACITY AVERAGE SUVmax PULMONARY OPACITIES 1 B/B 41 28 34 RUL-LUL 6.08 7.71 6.9 2 B/B 37 25 31 RUL-LUL 8.5 10.79 9.95 3 C/C 60 24 42 RLL-LLL 5.17 5.01 5.09 4 B/C 51 36 43.5 RLL-LLL 9.4 8.84 9.12 5 B/B 34 30 32 RUL-LUL 4.55 3.98 4.27 6 B/B 31 18 24.5 RUL-LUL 6.14 5.38 5.76 7 C/C 37 43 40 RUL-LUL 15.42 12.04 13.73 8 C/C 41.5 30 35.7 RLL-LLL 9.51 9.18 9.35 9 C/C 28 57 42.5 RLL-LUL 8.72 9.96 9.34 10 A/A NLO 14.5 14.5 NLO-LUL NLO 3.58 3.58 11 C/C 31.5 21 26.2 RUL-LUL 5.07 3.88 4.48 12 A/A 15.5 11 13.2 RUL-LUL 3.28 2.01 2.65 13 A/A 18.5 15 16.75 RUL-LUL 5.6 4 4.80 14 1–1 q-r/A 12 10.5 11.25 RUL-LUL 2.66 1.87 2.27 15 A/B 17 27 22 RUL-LUL 6.11 7.69 6.90 16 A/B 26 16.5 21.25 RUL-LUL 6.16 5.25 5.71 17 A/A 25.5 11 18.25 RUL-LUL 4.71 3.15 3.93 Note: RUL: right upper lobe; LUL: left upper lobe; RLL: right lower lobe; LLL: left lower lobe; NLO: no large opacity. All patients also had scattered micronodules throughout both lung fields, predominantly in the upper lobes, with calcifications in the pulmonary opacities and affected lymph nodes to varying degrees. SUVmax and adenopathies All patients presented mediastinal lymphadenopathy with high metabolic activity. Additionally, 88.2% of patients had hypermetabolic lymphadenopathy in other extrathoracic areas (supraclavicular, lower thoracic, and/or abdominal regions). The average SUVmax of the lymphadenopathies was 6.22 ± 1.56, with limits of 10.81 and 4.10, respectively (Table 2 ). A significant positive correlation was observed between the average SUVmax of the lymphadenopathies and the pulmonary opacities (ρ = 0.511, P = 0.036). Table 2 Metabolic activity of thoracic and extrathoracic adenopathies measured by SUVmax. PATIENT SUPRACLAVICULAR ADENOPATHY RIGHT H-M ADENOPATHY LEFT H-M ADENOPATHY SUBCARINAL ADENOPATHY LOWER THORACIC ADENOPATHY ABDOMINAL ADENOPATHY AVERAGE ADENOPATHIES 1 5.68 7.37 7.04 6.28 3.3 5.93 2 13.35 9.07 12.75 8.08 10.81 3 3.34 8.2 4.81 8.2 3.3 9.68 6.26 4 4.07 3.24 2.89 2.89 7.43 4.10 5 3.91 4.21 5.8 4.17 7.26 7.13 5.41 6 3.21 15.81 3.34 3.97 3.89 4.03 5.71 7 3.62 8.49 7.38 10.91 6.55 7.39 8 5.83 5.51 7.32 6.22 9 5.77 5.73 6.07 5.86 10 4.46 3.76 4.94 6.58 4.94 11 7.85 8.78 6.01 8.35 6.89 13.95 8.64 12 4.65 5.38 5.75 6.86 4.04 5.34 13 4.71 8.54 5.08 4.58 10.59 6.70 14 5.24 5.24 5.9 5.46 15 3.02 10.75 6.42 7.15 5.33 5.1 6.30 16 4.31 6.02 5.59 7.26 4.08 6.07 5.56 17 5.97 6.03 3.9 4.42 5.08 Note: H-M: hilar-mediastinal. Blank spaces: no significant adenopathies. Relationships with occupational and other variables We attempted to identify factors that might be associated with increased pulmonary uptake. We found no correlation between the SUVmax and the years of exposure (ρ = 0.091, P = NS), the time elapsed since cessation of the activity (ρ = 0.288, P = NS), the number of years from the start of exposure to the diagnosis of silicosis or PMF, or smoking history. However, we did find a correlation between metabolic activity and opacity size (ρ = 0.747, P = 0.001), as well as a positive correlation between the ICOERD classification and the SUVmax (ρ = 0.697, P = 0.002). In patients with ICOERD category A, the SUVmax was 3.4 ± 1.0; for those with category B it was 6.5 ± 1.8; and for those with category C it was 8.5 ± 3.4. Similarly, a significantly positive correlation between the SUVmax and the ILO classification was observed (ρ = 0.626, P = 0.007). With respect to pulmonary function tests, the SUVmax was significantly and inversely correlated with bronchial obstruction indices such as the percentage of FEV 1 (ρ = -0.562, P = 0.019) and the FEV 1 /FVC ratio (ρ = -0.565, P = 0.018), but not with the percentage of DLCOc (ρ = -0.283, P = 0.348) or FVC (ρ = -0.468, P = 0.058), although the latter was borderline significant. Linear regression analysis of all the significant variables revealed that the best model included only the size of both pulmonary opacities as a covariate. SUVmax and biomarkers We also explored the relationships between the SUVmax and several biomarkers and inflammatory indices (Table 3 ). Among the biomarkers analyzed, only fibrinogen was significantly correlated with the SUVmax of both the pulmonary conglomerates (ρ = 0.717, P = 0.001) and the lymphadenopathies (ρ = 0.593, P = 0.012). Table 3 Relationships between the SUVmax and biomarkers and inflammatory indices. Biomarkers and Inflammatory index Mean ± SD AVERAGE SUVmax LARGE OPACITIES (ρ, P values) AVERAGE SUVmax ADENOPATHIES (ρ, P values) LDH 259.24 ± 89.58 ρ 0.074, P 0.779 ρ 0.118, P 0.653 Fibrinogen 363.18 ± 83.19 ρ 0.717, P 0.001 ρ 0.593, P 0.012 ACE 95.66 ± 46.66 ρ 0.389, P 0.123 ρ 0.404, P 0.107 Leucocytes (10 3 ) 6,37 ± 1,46 ρ 0.048, P 0.855 ρ 0.012, P 0.963 Platelets (10 3 ) 245,47 ± 39,36 ρ 0.013, P 0.959 ρ 0.255, P 0.323 Neutrophils (%) 64.62 ± 9.18 ρ 0.338, P 0.184 ρ 0.368, P 0.147 Monocytes (%) 10.75 ± 3.36 ρ -0.086, P 0.743 ρ -0.006, P 0.981 Lymphocytes (%) 22.09 ± 6.66 ρ -0.484, P 0.049 ρ -0.498, P 0.042 LMR 2.23 ± 0.79 ρ -0.506, P 0.038 ρ -0.482, P 0.050 SIRI 2.36 ± 2 ρ 0.559, P 0.020 ρ 0.436, P 0.080 AISI 621.21 ± 679.18 ρ 0.470, P 0.057 ρ 0.498, P 0.042 SII 919.01 ± 820.98 ρ 0.390, P 0.122 ρ 0.407, P 0.105 NLR 3.57 ± 2.43 ρ 0.438, P 0.079 ρ 0.424, P 0.090 PLR 197.90 ± 85 ρ 0.085, P 0.747 ρ 0.284, P 0.269 Note: LDH: lactate dehydrogenase; ACE: angiotensin converting enzyme. Regarding blood leukocyte cells, the percentage of lymphocytes showed a negative correlation with both pulmonary metabolic activity (ρ = -0.484, P = 0.049) and lymphadenopathy metabolic activity (ρ = -0.498, P = 0.042). In terms of systemic inflammatory indices, the SUVmax of pulmonary conglomerates was correlated with the LMR and SIRI (ρ = -0.506, P = 0.038 and ρ = 0.559, P = 0.02, respectively). The SUVmax of the lymphadenopathies was again correlated with the LMR (ρ = -0.482, P = 0.05) and the AISI (ρ = 0.498, P = 0.042). Metabolic activity and lymphocyte subsets Upon further analysis of specific lymphocyte subsets, the results revealed significant correlations with the SUVmax (Table 4 ). Notably, there was a marked negative correlation between total CD3 + cells, CD8 + cells and CD8 + NKT cells and lymphadenopathies, such that increased metabolic activity was associated with a decrease in these populations. However, only CD4 + NKT cells and the SUVmax in pulmonary opacities were significantly correlated. Additionally, when the B cell lineage was analyzed, both memory B cells and plasma cells were significantly correlated with the SUV in the lymphadenopathies, although not in the lung opacities. Table 4 Correlations between lymphocyte subsets and the SUVmax of large opacities and adenopathies. Lymphocyte subsets (%) * AVERAGE SUVmax LARGE OPACITIES (ρ, P values) CD4 + NKT ρ -0.611, P 0.012 AVERAGE SUVmax ADENOPATHIES (ρ, P values) CD3 + ρ -0.522, P 0.038 CD3 + CD8 + ρ -0.720, P 0.002 CD8 + NKT ρ -0.517, P 0.034 CD20 + CD19 + CD38 ++ CD27 − ρ 0.542, P 0.025 CD19 + CD38 ++ CD27 + ρ 0.522, P 0.032 Note: Only lymphocyte subsets with statistically significant correlations are shown, the rest of the subsets are shown in supplementary Table S2. Discussion Publications describing PET findings in silicosis are, in general, isolated cases detected in the context of suspected lung cancer [ 11 , 12 ]. To our knowledge, this is the first prospective, systematic study that describes the distribution and intensity of lung lesions and lymphadenopathy in patients with silicosis due to ES and seeks to explore the relationships between biomarkers and cellular populations and PET/CT metabolic activity with the aim of better understanding the mechanisms of the disease. One of the characteristics detected in our series was intense metabolic activity, despite an average of more than 11 years since exposure had ceased, not only in lung lesions but also in mediastinal and even extrathoracic lymphadenopathy. Thus, 70% of our patients had hypermetabolic lymphadenopathy in the supraclavicular and abdominal regions. This contrasts with the series of six patients described by Reichert et al. with classical silicosis, where only one patient had mediastinal lymphadenopathy and none of the six patients had hypermetabolic extrathoracic lymphadenopathy [ 13 ]. The greater aggressiveness of this type of silicosis [ 7 ] and differences in the composition and morphology between natural and artificial compounds [ 14 ] could explain these findings, although this remains unresolved. On the other hand, the intense metabolic activity of lung lesions can cause confusion with neoplastic processes such as lung cancer [ 15 ], and hypermetabolic mediastinal and extrathoracic lymphadenopathy may lead to an overestimation of cancer staging. With respect to biomarkers, we selected those in which we had found differences between patients with simple and complicated silicosis in a previous study [ 16 ]. Among them, fibrinogen showed a highly significant correlation with the SUVmax of lung lesions. Fibrinogen is converted into fibrin, which induces cytokine expression and leukocyte recruitment [ 17 ] and can be deposited in lung tissue, serving as a platform for inflammatory cells and fibroblasts in processes such as idiopathic pulmonary fibrosis [ 18 , 19 ]. Additionally, fibrin deposition is greater in the lungs of patients with idiopathic pulmonary fibrosis (IPF) than in those of healthy volunteers using a fibrin-specific PET [ 20 ]. Elevated fibrinogen levels were detected in our previous series of patients with simple silicosis, and even higher levels were detected in patients with complicated silicosis [ 16 ]. Our current research shows a strong correlation between fibrinogen and metabolic activity in complicated silicosis, further supporting a possible role of the coagulation system in the development of progressive massive fibrosis. We found no correlation between the SUVmax and exposure time or the time since exposure ended, but we did find a strong positive correlation between the size of the lung lesion and its metabolic activity. The progression rate is related to the silicon (Si) content in the nodules; the higher the Si content is, the faster the progression, even after exposure has ended [ 21 ]. Alveolar macrophages phagocytose silica particles, causing lysosomal damage and activation of the NLRP3 inflammasome, which leads to the release of multiple inflammatory and proinflammatory cytokines. These, along with ROS and RNS intermediaries, drive the cycle of apoptotic cell death and fibrosis [ 22 ]. Silica is released into the extracellular space, and along with cell death components, it results in a progressive increase in inflammatory and fibrogenic cells, leading to increased metabolic activity and the size of the silicotic conglomerate. Unraveling the cells involved could be of great importance in identifying therapeutic targets to help disrupt this vicious cycle. The relationships between specific blood cells and various outcomes of interstitial diseases are becoming increasingly evident and are a subject of investigation, as they may serve as accessible and easy-to-obtain biomarkers with prognostic value or even provide clues for potential therapeutic targets. For example, Kreuter [ 23 ] reported a negative correlation between the progression of IPF, hospitalizations, mortality and the number of lymphocytes. Achaiah et al. reported that neutrophils in the blood and lymphopenia are prognostic indicators of IPF progression [ 24 ]. In our study, the percentage of lymphocytes was negatively correlated with both pulmonary metabolic activity (r = -0.484, P = 0.049) and lymphadenopathy (r = -0.498, P = 0.042), indicating that an increased SUVmax was associated with fewer lymphocytes. Significant relationships were also found with certain inflammatory indices (LMR, SIRI, and AISI), which involve other blood cells, such as monocytes, neutrophils, and platelets, but whose numerator or denominator is the lymphocyte count. A decrease in lymphocytes and changes in these inflammatory indices have been detected in patients with complicated silicosis compared with those with simple silicosis and healthy controls [ 16 ]. Decreased lymphocytes and altered LMR were also detected among silica-exposed individuals without disease and patients with silicosis [ 25 ]. The relationship between decreased lymphocytes and the intensity of metabolic activity in both pulmonary opacities and lymphadenopathy is an interesting finding that has not been previously described. In the different lymphocyte subgroups, a decrease in CD4 + natural killer T (NKT) cells was significantly correlated with increased metabolic activity in pulmonary opacities, whereas a decrease in CD3 + and CD8 + NKT cells was correlated with increased metabolic activity in lymphadenopathy. Davis et al., in an experimental model of silicosis, observed the accumulation of lymphocytes in nodules and lymphadenopathy. The lymphocytes found were predominantly CD4 + T cells, but numerous CD8 + T cells, NKT cells, and CD4-γδ-TCR + T cells were also present [ 26 ]. NKT cells are considered to be at the frontier between innate and adaptive immunity, playing both protective and pathogenic roles, and they have been reported to contribute to various diseases, such as autoimmune diseases, infections, and cancer [ 27 , 28 ]. CD4 + NKT cell numbers are reduced in patients with multiple sclerosis, and they improve the disease by directing immune responses toward a Th2 response [ 29 ]. Expansion of CD8 + NKT cells has been reported in chronic immune activation, such as sarcoidosis [ 30 ], and CD8 + NKT cells are considered the most efficient transactivators of CD8 + T cells [ 31 ]. This could explain why we observed a negative correlation between CD8 + T cells or CD8 + NKT cells and the SUVmax of lymphadenopathy. Finally, we observed that memory B cells and plasmablasts or plasma cells were positively correlated with the SUVmax of lymphadenopathy. Complex relationships between B cells and NKT cells have been described, which could explain the observed relationships between the percentages of these subpopulations and the SUVmax values of large opacities and lymphadenopathy [ 32 , 33 ]. The hypothesis is that silica-activated macrophages produce interleukins that attract and activate lymphocytes, which in turn attract and activate more macrophages [ 26 ]. In this way, some lymphocyte subsets could play different roles in the inflammatory activity, unbalancing the global response and favoring the progression of the disease. One limitation of the study is the lack of knowledge regarding the inhaled silica load, and correlating this with exposure time may be inaccurate, as some of our workers experienced intense exposure over short periods of time [ 34 ]. Another limitation is the sample size, although to our knowledge, this is the largest prospective series on silicosis and the only one related to ES. Despite this limitation, we have found very interesting findings that may help improve our understanding of the disease mechanisms and the anatomical distribution of hypermetabolic lymphadenopathy. Recently, it has been suggested that lung function using FVC is not a sensitive measure of progression in most types of pneumoconiosis [ 35 ], thus PET/CT could be an alternative for evaluating the efficacy of drugs in this disease. Conclusions In conclusion, ES silicosis produces intense metabolic activity not only in the lungs but also in thoracic and extrathoracic lymphadenopathies, and this activity persists even years after exposure has ceased. Considering that silica is a Group 1 carcinogen and the potential coexistence of lung cancer, understanding the topographic distribution of hypermetabolic lymphadenopathy related to silica exposure is important to avoid overstaging neoplastic processes through PET. Additionally, the strong relationships found between metabolic activity and fibrinogen open a pathway for exploring new drugs not yet tested in this disease, such as monoclonal antibodies antagonizing factor XIIa, which are currently being tested in idiopathic pulmonary fibrosis (CSL312 Safety, Pharmacokinetics, and Pharmacodynamics in Idiopathic Pulmonary Fibrosis. ClinicalTrials.gov ID NCT05130970). Finally, the relationships found between different lymphocyte subsets and metabolic activity open a path for further research in silicosis, of which this study could constitute the first step. Methods Study population Seventeen patients who were diagnosed with silicosis and progressive massive fibrosis (PMF) caused by ES were included in the study. These patients are part of a cohort being monitored at Puerta del Mar University Hospital (Cádiz, Spain). All patients were diagnosed with silicosis based on their history of exposure to ES and radiological findings, chest radiography and high-resolution computed tomography (HRCT), and some were further confirmed through lung or lymph node biopsy. The study was approved by the Research Ethics Committee of the province of Cádiz and the Spanish Agency of Medicines and Medical Devices (Eudra CT 2021-002701-94, date 13/08/2021). All research was performed following the Declaration of Helsinki, in accordance with relevant guidelines/regulations and informed consent was obtained from all participants. The inclusion criteria were as follows: male patients aged 18 to 65 years, with at least five years of exposure to ES, and diagnosed with silicosis and PMF. The exclusion criteria were as follows: active smokers, other diseases affecting silicosis progression (cancer, HIV, hepatitis, liver or renal failure), active infectious disease and immunosuppressive, immunomodulatory, antifibrotic or biological therapies. Only patients taking prednisone at doses of 20 mg/day or lower were included. The first patient was enrolled in November 2021, and the last patient was enrolled in September 2022. Eleven patients were diagnosed with ES silicosis between 2010 and 2011 and subsequently ceased exposure. The remaining patients were diagnosed later, although exposure had ceased years earlier. At the time of diagnosis, 11 patients had simple silicosis, while 6 were diagnosed with complicated silicosis PMF category A. The sociodemographic characteristics of the patients at the time of their inclusion in the study are described in Table 5 . All patients had worked for small companies involved in cutting, polishing and installing kitchen and bathroom countertops. Table 5 Sociodemographic and labor data of the participants and pulmonary function values Mean ± SD Age (years) 44 ± 5.4 Starting Exposure Age 21.65 ± 5.11 Duration of Exposure (years) 10.94 ± 3.21 Years from start exposure to diagnosis of silicosis. 13.0 ± 4.24 Years from start exposure to diagnosis of PMF. 16.9 ± 3.6 Years from cessation of exposure to blood extraction and PET/CT (study) 11.6 ± 1.6 Never-smoker* 9 (53%) Ex-smoker* 8 (47%) Pack-years 7.13 ± 4.85 FVC (mL) 3914 ± 968 FVC (%) 79.1 ± 17.3 FEV 1 (mL) 2796 ± 899 FEV 1 (%) 70.8 ± 21.7 FEV 1 /FVC 0.69 ± 0.10 DLCOc (mmol/min/kPa) 8.62 ± 1.55 DLCOc (%) 85.3 ± 14.8 Note: FEV 1 : Forced expiratory volume in the first second, forced vital capacity (FVC); DLCOc: Diffusing capacity of lung for carbon monoxide. *Number of cases (percentage). A comprehensive clinical interview, respiratory function tests, blood sampling, and a PET/CT scan were performed on all patients, all on the same day. Respiratory function tests were carried out by trained personnel using an EasyOne Pro system (ndd Medizintechnik AG, Zurich, Switzerland), following international guidelines [ 36 , 37 ]. The radiological classification of silicosis was based on the International Labor Organization (ILO) classification for chest radiographs [ 38 ]. The CT, integrated into the PET/CT scans, was performed using 2 mm slices in the thoracic region, and the classification of images was done using the International Classification of HRCT for Occupational and Environmental Respiratory Diseases (ICOERD) [ 39 ]. Large opacities were defined when the mean diameter, measured in two perpendicular axes, exceeded 1 cm. Three experts independently interpreted the chest X-rays and HRCT scans, using the ICOERD classification to categorize large opacities as A, B, or C. Acquisition of PET/CT studies and image analysis PET/CT scans were performed using a Biograph mCT 20 Excel hybrid scanner (Siemens®) with time-of-flight (TOF) technology, following a minimum 6-hour fasting period and relative rest the day before the scan. Fifty-five to sixty-five minutes after the administration of 18 F-FDG, a low-dose CT scan (Somatom Definition AS 20) was acquired with CARE Dose 4D software, 80 KW, 20 slices per rotation, and 2 mm thickness, without oral or intravenous contrast administration. PET scans were performed from the vertex to the proximal third of the lower extremities, with the arms positioned overhead. Images were obtained with and without attenuation correction, and iterative reconstruction was performed using 6 iterations, 21 segments, and a Gaussian filter of 500 mm in the axial, coronal, and sagittal planes. The 18 F-FDG dose was calculated based on patient weight (MBq/kg), ranging between 259 and 381 MBq. The acquisition time varied between 2 and 35 minutes per bed position, and blood glucose levels did not exceed 200 mg/dL in any case. PET/CT scans were performed under consistent conditions of dose, injection-to-acquisition time, and reconstruction parameters. The PET/CT findings were classified as pulmonary, hilar-mediastinal (H-M) and extrathoracic. Lesions were considered pathological when 18 F-FDG uptake exceeded that of the thoracic descending aorta. All hypermetabolic large opacities in the pulmonary parenchyma were evaluated, and the opacity with the highest uptake was selected from each lung. For thoracic or extrathoracic lymph nodes, the largest and most metabolically active nodes were highlighted. All studies were reviewed independently by two experienced nuclear medicine physicians, and a third expert opinion was requested in cases of doubt. For quantitative PET/CT analysis, volumes of interest (VOI) were manually defined, and the maximum standard uptake values (SUVmax) were obtained from all regions [ 40 ]. Analysis of Systemic Inflammatory Indices and Lymphocyte Populations Blood samples were collected after fasting and immediately processed for biochemical and hematological analysis. Standard hematological parameters were automatically analyzed using an XN-1000 analyzer (Sysmex, Germany). The following inflammatory indices were calculated: neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), neutrophil × platelet-to-lymphocyte ratio (systemic immune-inflammation index or SII), neutrophil × monocyte-to-lymphocyte ratio (systemic inflammation response index or SIRI), and neutrophil × monocyte × platelet-to-lymphocyte ratio (aggregate index of systemic inflammation or AISI). A detailed immune lymphocyte profile was performed on 150 µL of fresh peripheral blood using cell surface immunostaining with the corresponding fluorochrome-conjugated antibodies for 15 minutes in the dark (supplementary Table S1 ). After treating the blood with 2 ml of lysing solution (Becton Dickinson; San Jose, CA, USA) for 5 minutes in the dark, the samples were analyzed by flow cytometry, and the results are reported as percentages of total lymphocytes. For intracellular staining, after surface staining, the cells were fixed and permeabilized with the Cytofix/Cytoperm Kit (Becton Dickinson). Then, intracellular fluorochrome-conjugated antibodies (anti-GATA3, anti-RORγT and anti-t-BET) were added, and the samples were incubated for 30 minutes in the dark, washed, centrifuged, and analyzed by flow cytometry. The lymphocyte subsets analyzed are shown in supplementary Table S2. Statistical analysis Results are expressed as mean and standard deviation (SD) or number of cases and percentages. Initially, the normal or non-normal distribution of each dataset was assessed using the Kolmogorov-Smirnov test. Bivariate techniques were used to analyze variable relationships (Spearman’s correlation test). Significant relationships were further analyzed using simple regression analysis. A significance level of P < 0.05 was adopted for all tests. Declarations Author Contributions Conception and design of the study: A.L.J., A.C.C., J.R.R.C. and M.P.V. Data collection, research and data analysis: A.L.J., A.C.C., J.R.R.C. and G.J.G. Subject recruitment, data collection and analyzed images: A.L.J., G.J.G., A.H.M., J.R.R.C., M.L.P.F.R., M.A.C.S. and M.P.V. Original draft, review and editing: A.L.J., A.C.C. and J.R.R.C. Critical revision of the manuscript: All authors. Funding acquisition: A.L.J. and A.C.C. All the authors have read and approved the final version of the submitted manuscript. Availability of data and materials The data are not publicly available due to privacy or ethical restrictions. The data that support the findings of this study are available on request from the corresponding author for researchers who meet the criteria for confidential data access as stipulated by participant informed consent and the Institutional Research Ethics Committee of the province of Cadiz, Spain. Competing interests The authors declare that they have no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results. Funding of the research This research was funded by the Programa Estatal de Generación de Conocimiento y Fortalecimiento del Sistema Español de I+D+i, financed by the Instituto de Salud Carlos III and Fondo Europeo de Desarrollo Regional (FEDER) 2014–2020, grant number PI19/01064, to A.C.C and A.L.J. References Hoy, R. F. et al. Current global perspectives on silicosis-Convergence of old and newly emergent hazards. Respirology 27 , 387–398. 10.1111/resp.14242 (2022). Fazio, J. C. et al. Silicosis Among Immigrant Engineered Stone (Quartz) Countertop Fabrication Workers in California. JAMA Intern. Med. 183 , 991–998. 10.1001/jamainternmed.2023.3295 (2023). Martinez Gonzalez, C. et al. Silicosis in Artificial Quartz Conglomerate Workers. Arch. Bronconeumol. (Engl Ed) . 55 , 459–464. 10.1016/j.arbres.2019.01.017 (2019). Kromhout, H., van Tongeren, M. & Cherrie, J. W. Should engineered stone products be banned? Occup. Environ. Med. 81 , 329–330. 10.1136/oemed-2024-109708 (2024). Leon-Jimenez, A. New Etiological Agents of Silicosis. Arch. Bronconeumol. 59 , 479–480. 10.1016/j.arbres.2023.03.003 (2023). Ramkissoon, C. et al. Understanding the pathogenesis of engineered stone-associated silicosis: The effect of particle chemistry on the lung cell response. Respirology 29 , 217–227. 10.1111/resp.14625 (2024). Wu, N., Xue, C., Yu, S. & Ye, Q. Artificial stone-associated silicosis in China: A prospective comparison with natural stone-associated silicosis. Respirology 25 , 518–524. 10.1111/resp.13744 (2020). Leon-Jimenez, A. et al. Artificial Stone Silicosis: Rapid Progression Following Exposure Cessation. Chest 158 , 1060–1068. 10.1016/j.chest.2020.03.026 (2020). Li, R., Kang, H. & Chen, S. From Basic Research to Clinical Practice: Considerations for Treatment Drugs for Silicosis. Int. J. Mol. Sci. 24 10.3390/ijms24098333 (2023). Capitanio, S., Nordin, A. J., Noraini, A. R. & Rossetti, C. PET/CT in nononcological lung diseases: current applications and future perspectives. Eur. Respir Rev. 25 , 247–258. 10.1183/16000617.0051-2016 (2016). del Castillo-Otero, D., Hidalgo-Molina, R. R. J. & Conde- Sánchez, A. Pajares -Vinardell M and León- Jiménez A. Positron emission tomography in artificial stone silicosis. J. Lung Pulmonary Respiratory Res. 11 , 37–39. 10.15406/jlprr.2024.11.00316 (2024). Matar, E. et al. Complicated silicosis resulting from occupational exposure to engineered stone products. Med. J. Aust . 206 , 385–386. 10.5694/mja16.00257 (2017). Reichert, M. & Bensadoun, E. S. PET imaging in patients with coal workers pneumoconiosis and suspected malignancy. J. Thorac. Oncol. 4 , 649–651. 10.1097/JTO.0b013e31819d4778 (2009). Ramkissoon, C. et al. Characterisation of dust emissions from machined engineered stones to understand the hazard for accelerated silicosis. Sci. Rep. 12 , 4351. 10.1038/s41598-022-08378-8 (2022). Kang, S. R. & Rho, J. Y. Progressive Massive Fibrosis Mimicking Lung Cancer: Two Case Reports with Potentially Useful CT Features for Differential Diagnosis. J. Korean Soc. Radiol. 83 , 1175–1181. 10.3348/jksr.2021.0185 (2022). Garcia-Nunez, A. et al. Inflammatory indices obtained from routine blood tests show an inflammatory state associated with disease progression in engineered stone silicosis patients. Sci. Rep. 12 , 8211. 10.1038/s41598-022-11926-x (2022). Jennewein, C. et al. Novel aspects of fibrin(ogen) fragments during inflammation. Mol. Med. 17 , 568–573. 10.2119/molmed.2010.00146 (2011). Bargagli, E. et al. Serum analysis of coagulation factors in IPF and NSIP. Inflammation 37, 10–16, (2014). 10.1007/s10753-013-9706-z Schuliga, M., Grainge, C., Westall, G. & Knight, D. The fibrogenic actions of the coagulant and plasminogen activation systems in pulmonary fibrosis. Int. J. Biochem. Cell. Biol. 97 , 108–117. 10.1016/j.biocel.2018.02.016 (2018). Munchel, J. K. et al. Fibrin-Positron Emission Tomography Imaging Reveals Ongoing Lung Injury in Idiopathic Pulmonary Fibrosis. Am. J. Respir Crit. Care Med. 210 , 514–517. 10.1164/rccm.202312-2357LE (2024). Leon-Jimenez, A. et al. Compositional and structural analysis of engineered stones and inorganic particles in silicotic nodules of exposed workers. Part. Fibre Toxicol. 18 10.1186/s12989-021-00434-x (2021). Vanka, K. S. et al. Understanding the pathogenesis of occupational coal and silica dust-associated lung disease. Eur. Respir Rev. 31 10.1183/16000617.0250-2021 (2022). Kreuter, M. et al. Monocyte Count as a Prognostic Biomarker in Patients with Idiopathic Pulmonary Fibrosis. Am. J. Respir Crit. Care Med. 204 , 74–81. 10.1164/rccm.202003-0669OC (2021). Achaiah, A. et al. Increased monocyte level is a risk factor for radiological progression in patients with early fibrotic interstitial lung abnormality. ERJ Open. Res. 8 10.1183/23120541.00226-2022 (2022). Lombardi, E. M. S., Mizutani, R. F., Terra-Filho, M., Ubiratan & de Paula Biomarkers related to silicosis and pulmonary function in individuals exposed to silica. Am. J. Ind. Med. 66 , 984–995. 10.1002/ajim.23528 (2023). Davis, G. S., Holmes, C. E., Pfeiffer, L. M. & Hemenway, D. R. Lymphocytes, lymphokines, and silicosis. J. Environ. Pathol. Toxicol. Oncol. 20 (Suppl 1), 53–65 (2001). Nelson, A., Lukacs, J. D. & Johnston, B. The Current Landscape of NKT Cell Immunotherapy and the Hills Ahead. Cancers (Basel) . 13 10.3390/cancers13205174 (2021). Dhodapkar, M. V., Kumar, V. & Type II NKT Cells and Their Emerging Role in Health and Disease. J. Immunol. 198 , 1015–1021. 10.4049/jimmunol.1601399 (2017). Ahmadi, A. et al. The role of NK and NKT cells in the pathogenesis and improvement of multiple sclerosis following disease-modifying therapies. Health Sci. Rep. 5 , e489. 10.1002/hsr2.489 (2022). Naccache, J. M. et al. Increasing level of CD56 + T-cells in peripheral blood in sarcoidosis. Eur. Respir J. 27 , 654. 10.1183/09031936.06.00129505 (2006). Lin, H., Nieda, M., Rozenkov, V. & Nicol, A. J. Analysis of the effect of different NKT cell subpopulations on the activation of CD4 and CD8 T cells, NK cells, and B cells. Exp. Hematol. 34 , 289–295. 10.1016/j.exphem.2005.12.008 (2006). Doherty, D. G., Melo, A. M., Moreno-Olivera, A. & Solomos, A. C. Activation and Regulation of B Cell Responses by Invariant Natural Killer T Cells. Front. Immunol. 9 , 1360. 10.3389/fimmu.2018.01360 (2018). Leadbetter, E. A. & Karlsson, M. C. I. Invariant natural killer T cells balance B cell immunity. Immunol. Rev. 299 , 93–107. 10.1111/imr.12938 (2021). Perez-Alonso, A., Gonzalez-Dominguez, M. E., Novalbos-Ruiz, J. P., Leon-Jimenez, A. & Cordoba-Dona, J. A. Artificial Stone Silicosis: Accumulation of errors in the resurgence of an occupational disease: A qualitative study. Work 70 , 433–442. 10.3233/WOR-213582 (2021). Thiruvarudchelvan, A., Hart-Brown, L., Bloch, M. & Yates, D. The Nintedanib in Progressive Pneumoconiosis Study (NiPPs): early data from Australia. Eur. Respir. J. 62 , PA3800. 10.1183/13993003.congress-2023.PA3800 (2023). Graham, B. L. et al. Standardization of Spirometry 2019 Update. An Official American Thoracic Society and European Respiratory Society Technical Statement. Am. J. Respir Crit. Care Med. 200 , e70–e88. 10.1164/rccm.201908-1590ST (2019). Graham, B. L. et al. 2017 ERS/ATS standards for single-breath carbon monoxide uptake in the lung. Eur. Respir J. 49 10.1183/13993003.00016-2016 (2017). International Labour Office. Geneva, S. I. L. O (International Labour Office, 2011). Suganuma, N. et al. Reliability of the proposed international classification of high-resolution computed tomography for occupational and environmental respiratory diseases. J. Occup. Health . 51 , 210–222. 10.1539/joh.l8030 (2009). Boellaard, R. et al. FDG PET/CT: EANM procedure guidelines for tumour imaging: version 2.0. Eur. J. Nucl. Med. Mol. Imaging . 42 , 328–354. 10.1007/s00259-014-2961-x (2015). Additional Declarations No competing interests reported. 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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-5879579","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":409652264,"identity":"d883a9fa-bf32-4b77-84dd-382bc90f9170","order_by":0,"name":"León-Jiménez Antonio","email":"","orcid":"","institution":"Hospital Universitario Puerta del Mar","correspondingAuthor":false,"prefix":"","firstName":"León-Jiménez","middleName":"","lastName":"Antonio","suffix":""},{"id":409652265,"identity":"c6475449-04db-491a-b6b5-e3ec46b1d9c2","order_by":1,"name":"Rodríguez-Rubio Corona Julio","email":"","orcid":"","institution":"Hospital Universitario Puerta del Mar","correspondingAuthor":false,"prefix":"","firstName":"Rodríguez-Rubio","middleName":"Corona","lastName":"Julio","suffix":""},{"id":409652269,"identity":"d4e36f2e-7491-47d0-9ceb-6375d95bc3dc","order_by":2,"name":"Jiménez-Gómez Gema","email":"","orcid":"","institution":"Biomedical Research and Innovation Institute of Cadiz","correspondingAuthor":false,"prefix":"","firstName":"Jiménez-Gómez","middleName":"","lastName":"Gema","suffix":""},{"id":409652273,"identity":"b0ecc103-6ccd-484a-8faa-704ce220bcb7","order_by":3,"name":"Piñero Fernández-Reyes María Luisa","email":"","orcid":"","institution":"Hospital Universitario Puerta del Mar","correspondingAuthor":false,"prefix":"","firstName":"Piñero","middleName":"Fernández-Reyes María","lastName":"Luisa","suffix":""},{"id":409652275,"identity":"96473a92-e8c8-473c-af8c-f4a01b9b4a69","order_by":4,"name":"Hidalgo-Molina Antonio","email":"","orcid":"","institution":"Hospital Universitario Puerta del 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Antonio","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA30lEQVRIiWNgGAWjYNACAzjLhhjlzCha0ojVggCHCWvgn91/8HFBgQ0Df/vptAcf/pxP7G8/wPjwBx4tEncOMxvPMEhjkDiTu91wZtvtxBlnEpiNefBZcyOZTZrH4DDQO7nbpHkbbiduYEhgk8anQx6uhf/tNmmeP+cSN/A/YP+Jz2EGcC0SQFt42A4kbpBIYGPA5zDDG8nGxjwGaTwSN96C/JJsPOPGw2ZpfFrkbiQ+fMzzx0aOvz93GzDE7GT7+5MPfsTnMBgAGcsGZTM2EKEBAtgIKxkFo2AUjIIRCQBT4kiRP5bVLwAAAABJRU5ErkJggg==","orcid":"","institution":"University of Cádiz","correspondingAuthor":true,"prefix":"","firstName":"Campos-Caro","middleName":"","lastName":"Antonio","suffix":""}],"badges":[],"createdAt":"2025-01-22 09:38:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5879579/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5879579/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-10562-5","type":"published","date":"2025-07-14T16:05:29+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":87220062,"identity":"c52192c0-2dfc-43a1-b3a7-b6c34a892adb","added_by":"auto","created_at":"2025-07-21 16:11:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1180979,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5879579/v1/e5f3b174-49b4-46f3-b6a0-2063ebe429d2.pdf"},{"id":75303320,"identity":"1cf031a2-d2ed-4833-847f-69ab2d714ebc","added_by":"auto","created_at":"2025-02-03 08:02:32","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":70865,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryfilePETandSilicosis.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5879579/v1/14233a2c56711c077819038e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Complicated silicosis due to engineered stone: High metabolic activity in positron emission tomography and systemic inflammation years after exposure cessation","fulltext":[{"header":"Introduction","content":"\u003cp\u003eEngineered stone silicosis is a serious occupational health issue with a high number of cases in many countries, such as Australia, Spain, Israel and the United States [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], among others, due to the emergence of a new material used in bathroom and kitchen countertops. This high incidence has even resulted in the prohibition of this material in Australia and proposals for its gradual ban in other European countries [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. This engineered stone (ES), commonly known as artificial stone, quartz or silica agglomerates, is characterized by a high content of micronized crystalline silica (over 80% quartz and/or cristobalite), along with resins and metals [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Compared with natural stones, artificial silica agglomerates induce a more aggressive form of silicosis with higher mortality [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], and the disease can progress rapidly even after exposure has ceased [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSilicosis is a progressive interstitial lung disease with no specific treatment except for lung transplantation in its final stages. Despite numerous studies, most of which have been conducted in animal models and cell cultures [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], the underlying mechanisms and cellular processes involved in disease progression remain largely unknown.\u003c/p\u003e \u003cp\u003eSince activated inflammatory cells consume glucose for energy and increase glucose transporter expression [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], positron emission tomography/computed tomography (PET/CT) with \u003csup\u003e18\u003c/sup\u003eF-fluorodeoxyglucose (\u003csup\u003e18\u003c/sup\u003eF-FDG) may provide new insights into the role of biomarkers and inflammatory cells involved in the progression of silicosis in patients.\u003c/p\u003e \u003cp\u003eThe aim of our study was to assess the metabolic activity of lung lesions and lymph nodes in patients with complicated silicosis due to ES and to investigate their relationships with specific biomarkers, systemic inflammatory indices, and lymphocyte subpopulations in peripheral blood.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSilicotic conglomerates and metabolic activity\u003c/h2\u003e \u003cp\u003eRadiological evaluation by HRCT classified patients according to the ICOERD classification, with five patients categorized as PMF category A, six as category B, and six as category C. All patients presented bilateral large opacities except for one patient who had a unilateral large opacity. The silicotic conglomerates (large opacities) with the highest uptake were mostly located in the upper lobes. From each lung, the region with the highest SUVmax was selected. The average SUVmax of these pulmonary opacities was 6.32\u0026thinsp;\u0026plusmn;\u0026thinsp;3.04, with limits of 13.73 and 2.27. The values for each patient and the diameter of each opacity measured in millimeters (mm) by HRCT are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\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\u003eRelationships between radiological opacities in the lung and their corresponding metabolic activity measured by SUVmax.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePATIENT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eILO/ICOERD\u003c/p\u003e \u003cp\u003eCATEGORY\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDIAMETER\u003c/p\u003e \u003cp\u003eRIGHT LUNG\u003c/p\u003e \u003cp\u003eOPACITY\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDIAMETER\u003c/p\u003e \u003cp\u003eLEFT LUNG\u003c/p\u003e \u003cp\u003eOPACITY\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAVERAGE\u003c/p\u003e \u003cp\u003eDIAMETER\u003c/p\u003e \u003cp\u003ePULMONARY OPACITIES\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLOCATION\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSUVmax\u003c/p\u003e \u003cp\u003eRIGHT LUNG OPACITY\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSUVmax\u003c/p\u003e \u003cp\u003eLEFT LUNG\u003c/p\u003e \u003cp\u003eOPACITY\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAVERAGE SUVmax\u003c/p\u003e \u003cp\u003ePULMONARY OPACITIES\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB/B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRUL-LUL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e6.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB/B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRUL-LUL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e9.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC/C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRLL-LLL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e5.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB/C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e 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align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e5.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC/C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRUL-LUL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e12.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e13.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC/C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRLL-LLL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e9.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC/C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e42.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRLL-LUL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e9.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNLO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNLO-LUL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNLO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC/C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRUL-LUL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRUL-LUL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRUL-LUL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026ndash;1 q-r/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRUL-LUL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA/B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRUL-LUL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e6.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA/B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRUL-LUL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e5.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRUL-LUL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eNote: RUL: right upper lobe; LUL: left upper lobe; RLL: right lower lobe; LLL: left lower lobe; NLO: no large opacity.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAll patients also had scattered micronodules throughout both lung fields, predominantly in the upper lobes, with calcifications in the pulmonary opacities and affected lymph nodes to varying degrees.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSUVmax and adenopathies\u003c/h3\u003e\n\u003cp\u003eAll patients presented mediastinal lymphadenopathy with high metabolic activity. Additionally, 88.2% of patients had hypermetabolic lymphadenopathy in other extrathoracic areas (supraclavicular, lower thoracic, and/or abdominal regions). The average SUVmax of the lymphadenopathies was 6.22\u0026thinsp;\u0026plusmn;\u0026thinsp;1.56, with limits of 10.81 and 4.10, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). A significant positive correlation was observed between the average SUVmax of the lymphadenopathies and the pulmonary opacities (ρ\u0026thinsp;=\u0026thinsp;0.511, P\u0026thinsp;=\u0026thinsp;0.036).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\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\u003eMetabolic activity of thoracic and extrathoracic adenopathies measured by SUVmax.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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 \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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePATIENT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSUPRACLAVICULAR ADENOPATHY\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRIGHT H-M ADENOPATHY\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLEFT H-M ADENOPATHY\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSUBCARINAL ADENOPATHY\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLOWER THORACIC ADENOPATHY\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eABDOMINAL ADENOPATHY\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAVERAGE ADENOPATHIES\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e10.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e13.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eNote: H-M: hilar-mediastinal. Blank spaces: no significant adenopathies.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eRelationships with occupational and other variables\u003c/h3\u003e\n\u003cp\u003eWe attempted to identify factors that might be associated with increased pulmonary uptake. We found no correlation between the SUVmax and the years of exposure (ρ\u0026thinsp;=\u0026thinsp;0.091, P\u0026thinsp;=\u0026thinsp;NS), the time elapsed since cessation of the activity (ρ\u0026thinsp;=\u0026thinsp;0.288, P\u0026thinsp;=\u0026thinsp;NS), the number of years from the start of exposure to the diagnosis of silicosis or PMF, or smoking history. However, we did find a correlation between metabolic activity and opacity size (ρ\u0026thinsp;=\u0026thinsp;0.747, P\u0026thinsp;=\u0026thinsp;0.001), as well as a positive correlation between the ICOERD classification and the SUVmax (ρ\u0026thinsp;=\u0026thinsp;0.697, P\u0026thinsp;=\u0026thinsp;0.002). In patients with ICOERD category A, the SUVmax was 3.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0; for those with category B it was 6.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8; and for those with category C it was 8.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4. Similarly, a significantly positive correlation between the SUVmax and the ILO classification was observed (ρ\u0026thinsp;=\u0026thinsp;0.626, P\u0026thinsp;=\u0026thinsp;0.007).\u003c/p\u003e \u003cp\u003eWith respect to pulmonary function tests, the SUVmax was significantly and inversely correlated with bronchial obstruction indices such as the percentage of FEV\u003csub\u003e1\u003c/sub\u003e (ρ = -0.562, P\u0026thinsp;=\u0026thinsp;0.019) and the FEV\u003csub\u003e1\u003c/sub\u003e/FVC ratio (ρ = -0.565, P\u0026thinsp;=\u0026thinsp;0.018), but not with the percentage of DLCOc (ρ = -0.283, P\u0026thinsp;=\u0026thinsp;0.348) or FVC (ρ = -0.468, P\u0026thinsp;=\u0026thinsp;0.058), although the latter was borderline significant.\u003c/p\u003e \u003cp\u003eLinear regression analysis of all the significant variables revealed that the best model included only the size of both pulmonary opacities as a covariate.\u003c/p\u003e\n\u003ch3\u003eSUVmax and biomarkers\u003c/h3\u003e\n\u003cp\u003eWe also explored the relationships between the SUVmax and several biomarkers and inflammatory indices (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Among the biomarkers analyzed, only fibrinogen was significantly correlated with the SUVmax of both the pulmonary conglomerates (ρ\u0026thinsp;=\u0026thinsp;0.717, P\u0026thinsp;=\u0026thinsp;0.001) and the lymphadenopathies (ρ\u0026thinsp;=\u0026thinsp;0.593, P\u0026thinsp;=\u0026thinsp;0.012).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\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\u003eRelationships between the SUVmax and biomarkers and inflammatory indices.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" 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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiomarkers and Inflammatory index\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAVERAGE SUVmax\u003c/p\u003e \u003cp\u003eLARGE OPACITIES\u003c/p\u003e \u003cp\u003e(ρ, P values)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAVERAGE SUVmax ADENOPATHIES\u003c/p\u003e \u003cp\u003e(ρ, P values)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e259.24\u0026thinsp;\u0026plusmn;\u0026thinsp;89.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eρ 0.074, P 0.779\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eρ 0.118, P 0.653\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFibrinogen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e363.18\u0026thinsp;\u0026plusmn;\u0026thinsp;83.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eρ 0.717, P 0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eρ 0.593, P 0.012\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e95.66\u0026thinsp;\u0026plusmn;\u0026thinsp;46.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eρ 0.389, P 0.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eρ 0.404, P 0.107\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeucocytes (10\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e6,37\u0026thinsp;\u0026plusmn;\u0026thinsp;1,46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eρ 0.048, P 0.855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eρ 0.012, P 0.963\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelets (10\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e245,47\u0026thinsp;\u0026plusmn;\u0026thinsp;39,36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eρ 0.013, P 0.959\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eρ 0.255, P 0.323\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophils (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e64.62\u0026thinsp;\u0026plusmn;\u0026thinsp;9.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eρ 0.338, P 0.184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eρ 0.368, P 0.147\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonocytes (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e10.75\u0026thinsp;\u0026plusmn;\u0026thinsp;3.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eρ -0.086, P 0.743\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eρ -0.006, P 0.981\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphocytes (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e22.09\u0026thinsp;\u0026plusmn;\u0026thinsp;6.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eρ -0.484, P 0.049\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eρ -0.498, P 0.042\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e2.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eρ -0.506, P 0.038\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eρ -0.482, P 0.050\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSIRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e2.36\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eρ 0.559, P 0.020\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eρ 0.436, P 0.080\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAISI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e621.21\u0026thinsp;\u0026plusmn;\u0026thinsp;679.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eρ 0.470, P 0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eρ 0.498, P 0.042\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e919.01\u0026thinsp;\u0026plusmn;\u0026thinsp;820.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eρ 0.390, P 0.122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eρ 0.407, P 0.105\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e3.57\u0026thinsp;\u0026plusmn;\u0026thinsp;2.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eρ 0.438, P 0.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eρ 0.424, P 0.090\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e197.90\u0026thinsp;\u0026plusmn;\u0026thinsp;85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eρ 0.085, P 0.747\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eρ 0.284, P 0.269\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: LDH: lactate dehydrogenase; ACE: angiotensin converting enzyme.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eRegarding blood leukocyte cells, the percentage of lymphocytes showed a negative correlation with both pulmonary metabolic activity (ρ = -0.484, P\u0026thinsp;=\u0026thinsp;0.049) and lymphadenopathy metabolic activity (ρ = -0.498, P\u0026thinsp;=\u0026thinsp;0.042).\u003c/p\u003e \u003cp\u003eIn terms of systemic inflammatory indices, the SUVmax of pulmonary conglomerates was correlated with the LMR and SIRI (ρ = -0.506, P\u0026thinsp;=\u0026thinsp;0.038 and ρ\u0026thinsp;=\u0026thinsp;0.559, P\u0026thinsp;=\u0026thinsp;0.02, respectively). The SUVmax of the lymphadenopathies was again correlated with the LMR (ρ = -0.482, P\u0026thinsp;=\u0026thinsp;0.05) and the AISI (ρ\u0026thinsp;=\u0026thinsp;0.498, P\u0026thinsp;=\u0026thinsp;0.042).\u003c/p\u003e\n\u003ch3\u003eMetabolic activity and lymphocyte subsets\u003c/h3\u003e\n\u003cp\u003eUpon further analysis of specific lymphocyte subsets, the results revealed significant correlations with the SUVmax (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Notably, there was a marked negative correlation between total CD3\u0026thinsp;+\u0026thinsp;cells, CD8\u0026thinsp;+\u0026thinsp;cells and CD8\u0026thinsp;+\u0026thinsp;NKT cells and lymphadenopathies, such that increased metabolic activity was associated with a decrease in these populations. However, only CD4\u0026thinsp;+\u0026thinsp;NKT cells and the SUVmax in pulmonary opacities were significantly correlated. Additionally, when the B cell lineage was analyzed, both memory B cells and plasma cells were significantly correlated with the SUV in the lymphadenopathies, although not in the lung opacities.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\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\u003eCorrelations between lymphocyte subsets and the SUVmax of large opacities and adenopathies.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphocyte subsets (%) *\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAVERAGE SUVmax\u003c/p\u003e \u003cp\u003eLARGE OPACITIES\u003c/p\u003e \u003cp\u003e(ρ, P values)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD4\u003csup\u003e+\u003c/sup\u003eNKT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eρ -0.611, P 0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAVERAGE SUVmax\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eADENOPATHIES\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(ρ, P values)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD3\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eρ -0.522, P 0.038\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD3\u003csup\u003e+\u003c/sup\u003e CD8\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eρ -0.720, P 0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD8\u003csup\u003e+\u003c/sup\u003e NKT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eρ -0.517, P 0.034\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD20\u003csup\u003e+\u003c/sup\u003eCD19\u003csup\u003e+\u003c/sup\u003eCD38\u003csup\u003e++\u003c/sup\u003e CD27\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eρ 0.542, P 0.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD19\u003csup\u003e+\u003c/sup\u003eCD38\u003csup\u003e++\u003c/sup\u003e CD27\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eρ 0.522, P 0.032\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003eNote: Only lymphocyte subsets with statistically significant correlations are shown, the rest of the subsets are shown in supplementary Table S2.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003ePublications describing PET findings in silicosis are, in general, isolated cases detected in the context of suspected lung cancer [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. To our knowledge, this is the first prospective, systematic study that describes the distribution and intensity of lung lesions and lymphadenopathy in patients with silicosis due to ES and seeks to explore the relationships between biomarkers and cellular populations and PET/CT metabolic activity with the aim of better understanding the mechanisms of the disease.\u003c/p\u003e \u003cp\u003eOne of the characteristics detected in our series was intense metabolic activity, despite an average of more than 11 years since exposure had ceased, not only in lung lesions but also in mediastinal and even extrathoracic lymphadenopathy. Thus, 70% of our patients had hypermetabolic lymphadenopathy in the supraclavicular and abdominal regions. This contrasts with the series of six patients described by Reichert et al. with classical silicosis, where only one patient had mediastinal lymphadenopathy and none of the six patients had hypermetabolic extrathoracic lymphadenopathy [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The greater aggressiveness of this type of silicosis [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] and differences in the composition and morphology between natural and artificial compounds [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] could explain these findings, although this remains unresolved. On the other hand, the intense metabolic activity of lung lesions can cause confusion with neoplastic processes such as lung cancer [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], and hypermetabolic mediastinal and extrathoracic lymphadenopathy may lead to an overestimation of cancer staging.\u003c/p\u003e \u003cp\u003eWith respect to biomarkers, we selected those in which we had found differences between patients with simple and complicated silicosis in a previous study [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Among them, fibrinogen showed a highly significant correlation with the SUVmax of lung lesions. Fibrinogen is converted into fibrin, which induces cytokine expression and leukocyte recruitment [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] and can be deposited in lung tissue, serving as a platform for inflammatory cells and fibroblasts in processes such as idiopathic pulmonary fibrosis [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Additionally, fibrin deposition is greater in the lungs of patients with idiopathic pulmonary fibrosis (IPF) than in those of healthy volunteers using a fibrin-specific PET [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Elevated fibrinogen levels were detected in our previous series of patients with simple silicosis, and even higher levels were detected in patients with complicated silicosis [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Our current research shows a strong correlation between fibrinogen and metabolic activity in complicated silicosis, further supporting a possible role of the coagulation system in the development of progressive massive fibrosis.\u003c/p\u003e \u003cp\u003eWe found no correlation between the SUVmax and exposure time or the time since exposure ended, but we did find a strong positive correlation between the size of the lung lesion and its metabolic activity.\u003c/p\u003e \u003cp\u003eThe progression rate is related to the silicon (Si) content in the nodules; the higher the Si content is, the faster the progression, even after exposure has ended [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Alveolar macrophages phagocytose silica particles, causing lysosomal damage and activation of the NLRP3 inflammasome, which leads to the release of multiple inflammatory and proinflammatory cytokines. These, along with ROS and RNS intermediaries, drive the cycle of apoptotic cell death and fibrosis [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Silica is released into the extracellular space, and along with cell death components, it results in a progressive increase in inflammatory and fibrogenic cells, leading to increased metabolic activity and the size of the silicotic conglomerate. Unraveling the cells involved could be of great importance in identifying therapeutic targets to help disrupt this vicious cycle.\u003c/p\u003e \u003cp\u003eThe relationships between specific blood cells and various outcomes of interstitial diseases are becoming increasingly evident and are a subject of investigation, as they may serve as accessible and easy-to-obtain biomarkers with prognostic value or even provide clues for potential therapeutic targets. For example, Kreuter [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] reported a negative correlation between the progression of IPF, hospitalizations, mortality and the number of lymphocytes. Achaiah et al. reported that neutrophils in the blood and lymphopenia are prognostic indicators of IPF progression [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn our study, the percentage of lymphocytes was negatively correlated with both pulmonary metabolic activity (r = -0.484, P\u0026thinsp;=\u0026thinsp;0.049) and lymphadenopathy (r = -0.498, P\u0026thinsp;=\u0026thinsp;0.042), indicating that an increased SUVmax was associated with fewer lymphocytes. Significant relationships were also found with certain inflammatory indices (LMR, SIRI, and AISI), which involve other blood cells, such as monocytes, neutrophils, and platelets, but whose numerator or denominator is the lymphocyte count. A decrease in lymphocytes and changes in these inflammatory indices have been detected in patients with complicated silicosis compared with those with simple silicosis and healthy controls [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Decreased lymphocytes and altered LMR were also detected among silica-exposed individuals without disease and patients with silicosis [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe relationship between decreased lymphocytes and the intensity of metabolic activity in both pulmonary opacities and lymphadenopathy is an interesting finding that has not been previously described. In the different lymphocyte subgroups, a decrease in CD4\u0026thinsp;+\u0026thinsp;natural killer T (NKT) cells was significantly correlated with increased metabolic activity in pulmonary opacities, whereas a decrease in CD3\u0026thinsp;+\u0026thinsp;and CD8\u0026thinsp;+\u0026thinsp;NKT cells was correlated with increased metabolic activity in lymphadenopathy. Davis et al., in an experimental model of silicosis, observed the accumulation of lymphocytes in nodules and lymphadenopathy. The lymphocytes found were predominantly CD4\u0026thinsp;+\u0026thinsp;T cells, but numerous CD8\u0026thinsp;+\u0026thinsp;T cells, NKT cells, and CD4-γδ-TCR\u0026thinsp;+\u0026thinsp;T cells were also present [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. NKT cells are considered to be at the frontier between innate and adaptive immunity, playing both protective and pathogenic roles, and they have been reported to contribute to various diseases, such as autoimmune diseases, infections, and cancer [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. CD4\u0026thinsp;+\u0026thinsp;NKT cell numbers are reduced in patients with multiple sclerosis, and they improve the disease by directing immune responses toward a Th2 response [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Expansion of CD8\u0026thinsp;+\u0026thinsp;NKT cells has been reported in chronic immune activation, such as sarcoidosis [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], and CD8\u0026thinsp;+\u0026thinsp;NKT cells are considered the most efficient transactivators of CD8\u0026thinsp;+\u0026thinsp;T cells [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. This could explain why we observed a negative correlation between CD8\u0026thinsp;+\u0026thinsp;T cells or CD8\u0026thinsp;+\u0026thinsp;NKT cells and the SUVmax of lymphadenopathy.\u003c/p\u003e \u003cp\u003eFinally, we observed that memory B cells and plasmablasts or plasma cells were positively correlated with the SUVmax of lymphadenopathy. Complex relationships between B cells and NKT cells have been described, which could explain the observed relationships between the percentages of these subpopulations and the SUVmax values of large opacities and lymphadenopathy [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The hypothesis is that silica-activated macrophages produce interleukins that attract and activate lymphocytes, which in turn attract and activate more macrophages [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. In this way, some lymphocyte subsets could play different roles in the inflammatory activity, unbalancing the global response and favoring the progression of the disease.\u003c/p\u003e \u003cp\u003eOne limitation of the study is the lack of knowledge regarding the inhaled silica load, and correlating this with exposure time may be inaccurate, as some of our workers experienced intense exposure over short periods of time [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Another limitation is the sample size, although to our knowledge, this is the largest prospective series on silicosis and the only one related to ES. Despite this limitation, we have found very interesting findings that may help improve our understanding of the disease mechanisms and the anatomical distribution of hypermetabolic lymphadenopathy. Recently, it has been suggested that lung function using FVC is not a sensitive measure of progression in most types of pneumoconiosis [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], thus PET/CT could be an alternative for evaluating the efficacy of drugs in this disease.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, ES silicosis produces intense metabolic activity not only in the lungs but also in thoracic and extrathoracic lymphadenopathies, and this activity persists even years after exposure has ceased. Considering that silica is a Group 1 carcinogen and the potential coexistence of lung cancer, understanding the topographic distribution of hypermetabolic lymphadenopathy related to silica exposure is important to avoid overstaging neoplastic processes through PET. Additionally, the strong relationships found between metabolic activity and fibrinogen open a pathway for exploring new drugs not yet tested in this disease, such as monoclonal antibodies antagonizing factor XIIa, which are currently being tested in idiopathic pulmonary fibrosis (CSL312 Safety, Pharmacokinetics, and Pharmacodynamics in Idiopathic Pulmonary Fibrosis. ClinicalTrials.gov ID NCT05130970). Finally, the relationships found between different lymphocyte subsets and metabolic activity open a path for further research in silicosis, of which this study could constitute the first step.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eSeventeen patients who were diagnosed with silicosis and progressive massive fibrosis (PMF) caused by ES were included in the study. These patients are part of a cohort being monitored at Puerta del Mar University Hospital (C\u0026aacute;diz, Spain). All patients were diagnosed with silicosis based on their history of exposure to ES and radiological findings, chest radiography and high-resolution computed tomography (HRCT), and some were further confirmed through lung or lymph node biopsy. The study was approved by the Research Ethics Committee of the province of C\u0026aacute;diz and the Spanish Agency of Medicines and Medical Devices (Eudra CT 2021-002701-94, date 13/08/2021). All research was performed following the Declaration of Helsinki, in accordance with relevant guidelines/regulations and informed consent was obtained from all participants.\u003c/p\u003e \u003cp\u003eThe inclusion criteria were as follows: male patients aged 18 to 65 years, with at least five years of exposure to ES, and diagnosed with silicosis and PMF. The exclusion criteria were as follows: active smokers, other diseases affecting silicosis progression (cancer, HIV, hepatitis, liver or renal failure), active infectious disease and immunosuppressive, immunomodulatory, antifibrotic or biological therapies. Only patients taking prednisone at doses of 20 mg/day or lower were included. The first patient was enrolled in November 2021, and the last patient was enrolled in September 2022.\u003c/p\u003e \u003cp\u003eEleven patients were diagnosed with ES silicosis between 2010 and 2011 and subsequently ceased exposure. The remaining patients were diagnosed later, although exposure had ceased years earlier. At the time of diagnosis, 11 patients had simple silicosis, while 6 were diagnosed with complicated silicosis PMF category A.\u003c/p\u003e \u003cp\u003eThe sociodemographic characteristics of the patients at the time of their inclusion in the study are described in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. All patients had worked for small companies involved in cutting, polishing and installing kitchen and bathroom countertops.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSociodemographic and labor data of the participants and pulmonary function values\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e44\u0026thinsp;\u0026plusmn;\u0026thinsp;5.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eStarting Exposure Age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e21.65\u0026thinsp;\u0026plusmn;\u0026thinsp;5.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eDuration of Exposure (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e10.94\u0026thinsp;\u0026plusmn;\u0026thinsp;3.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eYears from start exposure to diagnosis of silicosis.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e13.0\u0026thinsp;\u0026plusmn;\u0026thinsp;4.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eYears from start exposure to diagnosis of PMF.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e16.9\u0026thinsp;\u0026plusmn;\u0026thinsp;3.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eYears from cessation of exposure to blood extraction and PET/CT (study)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e11.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNever-smoker*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e9 (53%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEx-smoker*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (47%)\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePack-years\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.13\u0026thinsp;\u0026plusmn;\u0026thinsp;4.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eFVC (mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e3914\u0026thinsp;\u0026plusmn;\u0026thinsp;968\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eFVC (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e79.1\u0026thinsp;\u0026plusmn;\u0026thinsp;17.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eFEV\u003csub\u003e1\u003c/sub\u003e (mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e2796\u0026thinsp;\u0026plusmn;\u0026thinsp;899\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eFEV\u003csub\u003e1\u003c/sub\u003e (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e70.8\u0026thinsp;\u0026plusmn;\u0026thinsp;21.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eFEV\u003csub\u003e1\u003c/sub\u003e/FVC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.69\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eDLCOc (mmol/min/kPa)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e8.62\u0026thinsp;\u0026plusmn;\u0026thinsp;1.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eDLCOc (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e85.3\u0026thinsp;\u0026plusmn;\u0026thinsp;14.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: FEV\u003csub\u003e1\u003c/sub\u003e: Forced expiratory volume in the first second, forced vital capacity (FVC); DLCOc: Diffusing capacity of lung for carbon monoxide. *Number of cases (percentage).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eA comprehensive clinical interview, respiratory function tests, blood sampling, and a PET/CT scan were performed on all patients, all on the same day. Respiratory function tests were carried out by trained personnel using an EasyOne Pro system (ndd Medizintechnik AG, Zurich, Switzerland), following international guidelines [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. The radiological classification of silicosis was based on the International Labor Organization (ILO) classification for chest radiographs [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. The CT, integrated into the PET/CT scans, was performed using 2 mm slices in the thoracic region, and the classification of images was done using the International Classification of HRCT for Occupational and Environmental Respiratory Diseases (ICOERD) [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Large opacities were defined when the mean diameter, measured in two perpendicular axes, exceeded 1 cm. Three experts independently interpreted the chest X-rays and HRCT scans, using the ICOERD classification to categorize large opacities as A, B, or C.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eAcquisition of PET/CT studies and image analysis\u003c/h2\u003e \u003cp\u003ePET/CT scans were performed using a Biograph mCT 20 Excel hybrid scanner (Siemens\u0026reg;) with time-of-flight (TOF) technology, following a minimum 6-hour fasting period and relative rest the day before the scan. Fifty-five to sixty-five minutes after the administration of \u003csup\u003e18\u003c/sup\u003eF-FDG, a low-dose CT scan (Somatom Definition AS 20) was acquired with CARE Dose 4D software, 80 KW, 20 slices per rotation, and 2 mm thickness, without oral or intravenous contrast administration. PET scans were performed from the vertex to the proximal third of the lower extremities, with the arms positioned overhead. Images were obtained with and without attenuation correction, and iterative reconstruction was performed using 6 iterations, 21 segments, and a Gaussian filter of 500 mm in the axial, coronal, and sagittal planes.\u003c/p\u003e \u003cp\u003eThe \u003csup\u003e18\u003c/sup\u003eF-FDG dose was calculated based on patient weight (MBq/kg), ranging between 259 and 381 MBq. The acquisition time varied between 2 and 35 minutes per bed position, and blood glucose levels did not exceed 200 mg/dL in any case. PET/CT scans were performed under consistent conditions of dose, injection-to-acquisition time, and reconstruction parameters.\u003c/p\u003e \u003cp\u003eThe PET/CT findings were classified as pulmonary, hilar-mediastinal (H-M) and extrathoracic. Lesions were considered pathological when \u003csup\u003e18\u003c/sup\u003eF-FDG uptake exceeded that of the thoracic descending aorta. All hypermetabolic large opacities in the pulmonary parenchyma were evaluated, and the opacity with the highest uptake was selected from each lung.\u003c/p\u003e \u003cp\u003eFor thoracic or extrathoracic lymph nodes, the largest and most metabolically active nodes were highlighted. All studies were reviewed independently by two experienced nuclear medicine physicians, and a third expert opinion was requested in cases of doubt.\u003c/p\u003e \u003cp\u003eFor quantitative PET/CT analysis, volumes of interest (VOI) were manually defined, and the maximum standard uptake values (SUVmax) were obtained from all regions [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of Systemic Inflammatory Indices and Lymphocyte Populations\u003c/h2\u003e \u003cp\u003eBlood samples were collected after fasting and immediately processed for biochemical and hematological analysis. Standard hematological parameters were automatically analyzed using an XN-1000 analyzer (Sysmex, Germany). The following inflammatory indices were calculated: neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), neutrophil \u0026times; platelet-to-lymphocyte ratio (systemic immune-inflammation index or SII), neutrophil \u0026times; monocyte-to-lymphocyte ratio (systemic inflammation response index or SIRI), and neutrophil \u0026times; monocyte \u0026times; platelet-to-lymphocyte ratio (aggregate index of systemic inflammation or AISI).\u003c/p\u003e \u003cp\u003eA detailed immune lymphocyte profile was performed on 150 \u0026micro;L of fresh peripheral blood using cell surface immunostaining with the corresponding fluorochrome-conjugated antibodies for 15 minutes in the dark (supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). After treating the blood with 2 ml of lysing solution (Becton Dickinson; San Jose, CA, USA) for 5 minutes in the dark, the samples were analyzed by flow cytometry, and the results are reported as percentages of total lymphocytes. For intracellular staining, after surface staining, the cells were fixed and permeabilized with the Cytofix/Cytoperm Kit (Becton Dickinson). Then, intracellular fluorochrome-conjugated antibodies (anti-GATA3, anti-RORγT and anti-t-BET) were added, and the samples were incubated for 30 minutes in the dark, washed, centrifuged, and analyzed by flow cytometry. The lymphocyte subsets analyzed are shown in supplementary Table S2.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eResults are expressed as mean and standard deviation (SD) or number of cases and percentages. Initially, the normal or non-normal distribution of each dataset was assessed using the Kolmogorov-Smirnov test. Bivariate techniques were used to analyze variable relationships (Spearman\u0026rsquo;s correlation test). Significant relationships were further analyzed using simple regression analysis. A significance level of P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was adopted for all tests.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConception and design of the study: A.L.J., A.C.C., J.R.R.C. and M.P.V. Data collection, research and data analysis: A.L.J., A.C.C., J.R.R.C. and G.J.G. Subject recruitment, data collection and analyzed images: A.L.J., G.J.G., A.H.M., J.R.R.C., M.L.P.F.R., M.A.C.S. and M.P.V. Original draft, review and editing: A.L.J., A.C.C. and J.R.R.C. Critical revision of the manuscript: All authors. Funding acquisition: A.L.J. and A.C.C. All the authors have read and approved the final version of the submitted manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data are not publicly available due to privacy or ethical restrictions. The data that support the findings of this study are available on request from the corresponding author for researchers who meet the criteria for confidential data access as stipulated by participant informed consent and the Institutional Research Ethics Committee of the province of Cadiz, Spain.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding of the research\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by the Programa Estatal de Generaci\u0026oacute;n de Conocimiento y Fortalecimiento del Sistema Espa\u0026ntilde;ol de I+D+i, financed by the Instituto de Salud Carlos III and Fondo Europeo de Desarrollo Regional (FEDER) 2014\u0026ndash;2020, grant number PI19/01064, to A.C.C and A.L.J.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHoy, R. F. et al. 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Imaging\u003c/em\u003e. \u003cb\u003e42\u003c/b\u003e, 328\u0026ndash;354. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00259-014-2961-x\u003c/span\u003e\u003cspan address=\"10.1007/s00259-014-2961-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2015).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Silicosis, Engineered stone, Systemic inflammatory indices, Lymphocyte subsets, Positron emission tomography","lastPublishedDoi":"10.21203/rs.3.rs-5879579/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5879579/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEngineered stone silicosis is an interstitial lung disease that progresses rapidly causing, in many cases, respiratory insufficiency and death. Metabolic activity in lungs and adenopathies and its relationships with systemic inflammation are unknown. Patients with complicated silicosis were enrolled. All had worked for at least 5 years in finishing and installing engineered stone and had ceased exposure for at least 7 years. Clinical data, positron emission tomography/computed tomography using \u003csup\u003e18\u003c/sup\u003eF-fluorodeoxyglucose (\u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT), respiratory function tests and blood samples were collected. Patients\u0026rsquo; mean age was 44\u0026thinsp;\u0026plusmn;\u0026thinsp;5.4 years. The average exposure duration was 10.94\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2. Years from cessation of exposure was 11.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6. The average maximum standardized uptake value (SUVmax) of large opacities was 6.32\u0026thinsp;\u0026plusmn;\u0026thinsp;3. All patients presented hypermetabolic mediastinal lymphadenopathies and 88.2% also extrathoracic lymphadenopathies. SUV max of large opacities was correlated with Fibrinogen (ρ\u0026thinsp;=\u0026thinsp;0.717, P\u0026thinsp;=\u0026thinsp;0.001), lymphocyte-to-monocyte ratio (ρ = -0.506, P\u0026thinsp;=\u0026thinsp;0.038), systemic inflammatory response index (ρ\u0026thinsp;=\u0026thinsp;0.559, P\u0026thinsp;=\u0026thinsp;0.02) and CD4\u0026thinsp;+\u0026thinsp;NKT cells. Large lung opacities and lymphadenopathies showed high metabolic activity even years after silica exposure ended. The relationships between metabolic activity and some inflammatory factors open a pathway for exploring new therapeutic targets.\u003c/p\u003e","manuscriptTitle":"Complicated silicosis due to engineered stone: High metabolic activity in positron emission tomography and systemic inflammation years after exposure cessation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-03 08:02:27","doi":"10.21203/rs.3.rs-5879579/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-03-19T05:02:14+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-05T07:03:14+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-02T09:37:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"209953700202091977093446201383007923229","date":"2025-02-20T23:22:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"219493796721932876829658602777150450688","date":"2025-02-20T19:13:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"186699960876906519647398207519179685309","date":"2025-02-19T02:07:10+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-02-19T00:19:06+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-02-19T00:01:50+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-01-31T16:36:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-01-31T10:20:36+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-01-22T09:35:25+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"afc3d4e5-9a34-4ded-9d68-39f163ba8b34","owner":[],"postedDate":"February 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":43685645,"name":"Health sciences/Medical research/Biomarkers/Predictive markers"},{"id":43685646,"name":"Health sciences/Medical research/Biomarkers/Prognostic markers"},{"id":43685647,"name":"Health sciences/Diseases/Respiratory tract diseases"}],"tags":[],"updatedAt":"2025-07-21T16:08:01+00:00","versionOfRecord":{"articleIdentity":"rs-5879579","link":"https://doi.org/10.1038/s41598-025-10562-5","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-07-14 16:05:29","publishedOnDateReadable":"July 14th, 2025"},"versionCreatedAt":"2025-02-03 08:02:27","video":"","vorDoi":"10.1038/s41598-025-10562-5","vorDoiUrl":"https://doi.org/10.1038/s41598-025-10562-5","workflowStages":[]},"version":"v1","identity":"rs-5879579","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5879579","identity":"rs-5879579","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}