Non-Invasive Detection of Acute Kidney Allograft Rejection Using [18F]FB-IL2 PET-CT: A First in Human Proof-of-Concept Study

Non-Invasive Detection of Acute Kidney Allograft Rejection Using [18F]FB-IL2 PET-CT: A First in Human Proof-of-Concept Study | 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 Non-Invasive Detection of Acute Kidney Allograft Rejection Using [18F]FB-IL2 PET-CT: A First in Human Proof-of-Concept Study Robert A. Pol, Niels Bloemendal, Alessia Artesani, Jan-Stephan Sanders, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8220933/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Despite its life-saving role, kidney transplantation is frequently complicated by acute T-cell–mediated rejection (TCMR), currently only diagnosed invasively by biopsy. [¹⁸F]FB-IL2 PET-CT is a novel molecular imaging approach that binds activated T cells via the IL-2 receptor. In this proof-of-concept study, 11 kidney transplant recipients with suspected rejection underwent [¹⁸F]FB-IL2 PET-CT. Tracer uptake in cortex versus medulla was quantified and compared with biopsy results, kidney function and peripheral blood T cells. Four patients were diagnosed with biopsy-confirmed TCMR. The cortex-to-medulla distribution volume ratio distinguished TCMR from non-rejection (p = 0.02) and correlated with interstitial inflammation (r = 0.68, p = 0.04) and proteinuria (r = 0.60, p = 0.05). No tracer administration adverse events were observed. [¹⁸F]FB-IL2 PET-CT appears safe and may provide a non-invasive alternative to biopsy for detection of acute TCMR in kidney transplant recipients. Larger, prospective studies are warranted to confirm its clinical utility. Health sciences/Nephrology/Renal replacement therapy Health sciences/Health care/Diagnosis Health sciences/Medical research Figures Figure 1 Figure 2 Figure 3 Introduction Kidney transplantation is the preferred treatment for patients with end-stage kidney disease, significantly improving both survival rates and quality of life [ 1 ]​. Despite advancements in immunosuppressive therapies, acute T-cell-mediated rejection (TCMR) continues to be an important challenge, particularly in the first-year post-transplantation, with substantial implications for long-term graft survival [ 2 ]. Early and accurate diagnosis of acute TCMR is essential for timely intervention to preserve graft function and patient health [ 3 ]. Currently, the gold standard for diagnosing kidney allograft rejection is a percutaneous kidney biopsy [ 4 , 5 ]​. While effective, biopsy carries risks including bleeding, pain, infection and graft loss ​[ 6 , 7 ]. Moreover, the small size of tissue biopsy may lead to sampling error and false-negative results, highlighting the need for non-invasive diagnostic alternatives [ 5 , 8 ]. Molecular imaging, particularly positron emission tomography-computed tomography (PET-CT), offers a promising alternative [ 9 ]. One such approach involves the use of the tracer 4-[ 18 F]fluorobenzoyl interleukin-2 ([ 18 F]FB-IL2), designed to visualise infiltrating T cells within the transplanted kidney. Interleukin-2 (IL-2) receptors, which are highly expressed on activated T-lymphocytes, serve as a primary target for this imaging technique [ 10 ]​. Previous clinical work has demonstrated that [¹⁸F]FB-IL2 PET imaging is safe and feasible for visualizing IL-2 receptor expression in solid tumours, supporting its potential for broader immunological applications [ 11 ]​. The application of [ 18 F]FB-IL2 PET-CT in kidney transplantation lies in its potential to provide a non-invasive, whole-organ assessment of immune activity, potentially detecting subclinical rejection and monitoring therapeutic responses, offering a promising alternative to the limitations associated with biopsies. This proof-of-concept study primarily aims, in a first-in-human application, to investigate whether [ 18 F]FB-IL2 PET-CT can act as a non-invasive diagnostic tool for acute TCMR by correlating [ 18 F]FB-IL2 PET-CT findings with histological results from kidney allograft biopsies. IL-2 can activate lymphocytes, raising concerns about immune stimulation and exacerbation of rejection [ 12 , 13 ]. While a previous study showed that [ 18 F]FB-IL2 PET-CT was well tolerated, the secondary aim of this study is to evaluate its safety, particularly regarding immune activation [ 14 ]. Methods Study design & population This was an observational proof-of-concept study aimed to explore the potential of [ 18 F]FB-IL2 PET-CT in detecting acute TCMR in kidney transplant recipients. The study was conducted at the University Medical Centre Groningen (UMCG) and included kidney transplant recipients (aged between 18 and 80 years) with a clinical indication for a biopsy due to suspected rejection. Exclusion criteria included patients with multiple organ transplants, severe comorbidities that could interfere with the study, contraindications for biopsy or PET-CT imaging (e.g. known allergies for contrast agents and claustrophobia), pregnant or breastfeeding women and patients who had recently (≤ 15 days) received basiliximab. Basiliximab, an IL-2 receptor antagonist with a half-life of approximately 7.4 days, suppresses T cell activation and could interfere with the ability of [ 18 F]FB-IL2 PET-CT scans to accurately detect immune activity related to allograft rejection [ 15 ]. The [ 18 F]FB-IL2 PET-CT scan was scheduled following clinical suspicion of rejection and coordinated with biopsy planning, but was always performed prior to the initiation of any anti-rejection therapy to ensure that imaging captured the untreated immunological state. The study was conducted in accordance with the Declaration of Helsinki and the Medical Research Involving Human Subjects Act, and received approval from the local Medical Ethics Committee. Written informed consent was obtained from all participants. The study was registered at ClinicalTrials.gov under the identifier NCT201501004. Patient Evaluation At study entry, clinical data were retrieved from patient records, including medical history, transplant indication, donor characteristics, immunosuppressive regimen, transplant function at the moment of suspected rejection (i.e. estimated glomerular filtration rate (eGFR), serum creatinine and proteinuria), as well as reason for suspected rejection. Laboratory parameters (blood count and blood chemistry) were also recorded. Transplant-specific data included donor type (living or deceased), human leukocyte antigen (HLA) mismatch compatibility, and the presence of donor-specific anti-HLA antibodies (DSA). A total of 15 kidney transplant recipients meeting inclusion criteria were consecutively enrolled. Patients were classified into rejection and non-rejection groups, all continuing standard nephrological care following hospital guidelines. Safety monitoring included structured assessment of adverse events, serious adverse events, and suspected unexpected serious adverse reactions, following Good Clinical Practice guidelines [ 16 ]. Given the IL-2 receptor-binding properties of [ 18 F]FB-IL2, special attention was given to potential immune activation, though this risk was considered minimal due to the sub-pharmacological dose (< 50 µg). To monitor for any signs of immune-related or general adverse reactions, all patients were clinically observed for at least one hour after the scan to detect any acute reactions (e.g., transient fever, headache, localised inflammation, claustrophobia, or allergic responses), were followed up within 24 hours to assess for delayed effects, and had their medical records reviewed up to four weeks post-scan for any late-onset events. No systemic effects were expected, as confirmed in preclinical and human trials, where [ 18 F]FB-IL2 was well tolerated without evidence of excessive T cell stimulation [ 14 , 17 ]. To ensure patient safety, clinical management decisions remained biopsy-based, preventing risk of delay in treatment. Percutaneous kidney biopsies were performed under ultrasonographic guidance in all patients with suspected allograft rejection and evaluated, as part of routine clinical assessment, by an expert pathologist using the Banff-classification [ 18 ]​. In case of inconclusive histological assessment, the clinical diagnosis was used to determine rejection status. PET-CT procedure All patients underwent [ 18 F]FB-IL2 PET-CT imaging at the Department of Nuclear Medicine and Molecular Imaging, UMCG. Following a low-dose CT scan for attenuation correction, a targeted dose of 200 MBq [ 18 F]FB-IL2 was administered intravenously over 5 minutes. The median administered activity was 197 MBq (interquartile range [IQR]: 142.1–202.08 MBq). A 60-minute dynamic PET scan (Biograph Vision mCT, Siemens Healthcare GmbH, Erlangen, Germany) was initiated simultaneously with tracer administration to assess time-dependent uptake and accumulation in the transplanted kidney. Images were reconstructed using an ordered-subset expectation maximization (OSEM) algorithm (4 iterations, 5 subsets) and corrected for detector normalization, deadtime, isotope decay, photon attenuation, random coincidences, and scatter. PET images were reconstructed into a series of 26 frames (7 × 10 seconds (s), 2 × 30 s, 3 × 60 s, 2 × 120 s, 2 × 180 s, 5 × 300 s, and 5 × 600 s). Spherical regions of interest (ROIs) with a volume of 0.52 cm³ were defined for the kidney cortex and medulla. Mean [ 18 F]FB-IL2 uptake at 60 minutes post-injection was corrected for body weight and injected dose to generate Standardised Uptake Values (SUV mean ) in the cortex and medulla, from which cortex-to-medulla ratios were calculated. Studies have shown that the binding of [ 18 F]FB-IL2 is reversible [ 14 , 19 ]. For this reason, Logan graphical analysis was used to determine the kinetic parameters, using the KinetiX package, a plug-in for the OsiriX DICOM viewer (Pixmeo SARL, Geneva, Switzerland) [ 20 ]. An image-derived whole blood activity curve was extracted from the aorta and used as input function for Logan graphical analysis to estimate the apparent total distribution volume (V T * ) parameter, which represents distribution of reversible tracer binding and perfusion [ 21 ]. Although Logan analysis typically uses a metabolite-corrected plasma input function to streamline the procedure and minimise patient burden, we instead employed an image-derived whole-blood curve without venous plasma sampling. This approach is supported by preclinical data showing that [¹⁸F]FB-IL2 exhibits reversible binding kinetics and limited in vivo metabolism [ 14 ], as well as by clinical findings in melanoma patients indicating minimal plasma metabolite formation during the imaging timeframe [ 11 ]. By using the image-derived whole blood curve as input function, we have assumed that the blood plasma ratio is constant over time and across patients and as a result a constant bias was introduced in the apparent V T * . By taking cortex-to-medulla V T * ratios this bias would cancel out. Logan linearization was performed from the onset of visually assessed linearity in the tissue activity curves and a T* = 30 min threshold was applied. V T * values were calculated separately for the cortex and medulla, and corresponding cortex-to-medulla V T * ratios were derived. All ROIs were reviewed and verified using OsiriX MD [ 20 ] (Pixmeo SARL, Geneva, Switzerland) for anatomical co-registration of PET images with the corresponding low-dose CT. Flow cytometry analyses (dup: abstract ?) Peripheral immune profiling was performed using multiparameter flow cytometry on EDTA-anticoagulated whole blood samples. A total of 500 µL peripheral blood was collected from each patient prior to PET-CT imaging. To remove plasma proteins and other interfering factors, samples were washed twice by adding 2 mL of phosphate-buffered saline (PBS) containing 1% bovine serum albumin (BSA), followed by centrifugation and removal of the supernatant. The resulting cell pellet was used for further staining. For staining, 100 µL of the washed cell suspension was incubated for 1 hour at room temperature with a monoclonal antibody panel targeting: CD3 (PerCP), CD4 (eFluor 450), CD8 (Alexa Fluor 700), CD127 (Alexa Fluor 647), CD25 (PE), CD45RO (FITC), and CCR7 (PE-Cy7). An additional 100 µL aliquot of each sample was used for fluorescence-minus-one control, specifically for CD25-PE gating. Red blood cells were lysed using FACS Lysing Solution (BD Biosciences) for 10 minutes. Cells were then washed, resuspended in PBS with 1% BSA and acquired on a BD LSR II flow cytometer. Flow cytometry data were analysed using Kaluza software (version 2.1, Beckman Coulter). CD3⁺ T cells were identified and further subdivided into CD4⁺ T-helper cells and CD8⁺ cytotoxic T cells. Within the CD4⁺ compartment, CD127- CD25hi Tregs were identified and subsequently categorised into naïve (CD45RO⁻) and memory (CD45RO⁺) subsets. Within the conventional CD4 + T cells (non-Tregs), four functional subsets were delineated based on CCR7 and CD45RO expression: naïve (CD45RO⁻CCR7⁺), central memory (CD45RO⁺CCR7⁺), effector memory (CD45RO⁺CCR7⁻), and terminally differentiated (CD45RO⁻CCR7⁻) T cells. A similar classification was applied to CD8⁺ T cells, including the terminally differentiated effector memory RA (TEMRA) population. In all T cell subsets, CD25 expression was quantified to assess activation status. Statistical Analysis Baseline characteristics were summarised using descriptive statistics. Continuous variables were reported as medians with IQRs, given the small sample size and associated limitations in reliably assessing normality. As such, only non-parametric tests were used for statistical comparisons. Group differences (e.g., rejection vs. no rejection) were assessed using the Wilcoxon Rank-Sum test. Statistical significance was defined as p < 0.05, with p-values between 0.05–0.1 reported as potential trends due to the small sample size. To facilitate interpretation, boxplots were generated for each dynamic [ 18 F]FB-IL2 PET-CT derived variable, illustrating medians, IQR, data spread, and individual observations. Corresponding Wilcoxon Rank-Sum p-values were displayed above the figure legend. To evaluate whether [¹⁸F]FB-IL2 PET-CT shows promise as a tool for detecting kidney allograft rejection, a comprehensive correlation analysis was conducted. A correlation matrix was constructed to examine associations between PET-derived imaging markers, histopathological features, kidney function parameters, and circulating immune cell subsets. Initially, given the diagnostic focus of this study, a single correlation analysis was planned across the full cohort (n = 11). However, due to biopsy sampling error, Banff histopathological features could not be reliably assessed in two patients. As a result, two separate correlation analyses were performed. The first included all 11 patients and explored associations between PET imaging markers and treated rejection, kidney function parameters (eGFR, serum creatinine, and proteinuria) and circulating immune cell subsets (CD3⁺, CD4⁺, CD8⁺, and Tregs). The second analysis, limited to 9 patients with interpretable biopsies, focused on selected Banff histopathological features of rejection, including interstitial inflammation, tubulitis, and interstitial fibrosis/tubular atrophy (IFTA). Variable selection was based on relevance and clinical applicability. PET parameters included SUV mean and V T * (both cortical averages and cortex-to-medulla ratios were used). Spearman’s rank correlation was used due to the small sample size and non-normal distribution of most variables. Correlation coefficients (r-values) were interpreted as weak (|r| = 0.1–0.3), moderate (|r| = 0.3–0.5), or strong (|r| >0.5). Statistical significance was defined as p < 0.05. P-values between 0.05 and 0.1 were reported as potential trends. To facilitate interpretation, results were visualised using heatmaps. All statistical analyses were conducted in R software (v4.1.0, R Foundation for Statistical Computing, Vienna, Austria), utilizing the Hmisc, ggplot2, reshape2, dplyr, pROC, caret, and openxlsx packages. Results Patient Characteristics at study entry A total of 15 kidney transplant recipients were initially enrolled. Four patients were excluded: One due to severely reduced vascular perfusion, as evidenced by repeated renograms showing minimal to absent blood flow to the transplant, which precluded accurate [ 18 F]FB-IL2 PET-CT imaging. Three patients were excluded after first enrolment due to having undergone biopsy for suspected rejection within 15 days of basiliximab induction therapy, which did not meet the predefined inclusion criteria. The final cohort included 11 patients (5 males, 45%; 6 females, 55%) with a median age of 53 years (IQR: 48–64). A flowchart of patient enrolment and exclusion is provided in Supplementary Fig. 1 . Primary causes of end-stage renal disease included glomerulonephritis, diabetic nephropathy, and polycystic kidney disease. All patients underwent [¹⁸F]FB-IL2 PET-CT due to clinical suspicion of rejection, most based on rising serum creatinine, reduced eGFR, or newly detected proteinuria. [¹⁸F]FB-IL2 was well tolerated in all participants and no general or immune-related adverse events were observed. Median serum creatinine at the time of the PET scan was 266 µmol/L (IQR: 205–341.5), with a median eGFR of 18 mL/min/1.73m² (IQR: 14.5–29) and a median proteinuria of 0.38 g/day (IQR: 0.20–0.46). Kidney transplant recipients received transplants from either deceased (n = 6) or living (n = 5) donors. HLA mismatch ranged from 0–2 alleles per locus, with two patients showing pre-transplant donor-specific antibodies against HLA class II and/or class I. Most patients (n = 10) received triple immunosuppressive therapy with tacrolimus, mycophenolate mofetil, and prednisolone. One patient was treated with tacrolimus, everolimus, and prednisolone. Basiliximab induction was administered to all patients, consisting of 20 mg on the day of transplantation and 20 mg four days post-transplantation. PET-CT imaging was performed ≥ 15 days after the last dose (range: 80–1017 days), in accordance with the study’s exclusion criteria. Further clinical, immunological, and transplant-related details are provided in Table 1 . Table 1 Patient and Transplant Characteristics at Time of Suspected Rejection. Patient number Age Sex (Male/ Female) BMI (Kg/m 2 ) Primary reason for ESRD & transplantation Proteinuria (g/day) Serum Creatinine (µmol/l) eGFR (ml/min/ 1.73m 2 ) Donor type (Living/ Deceased HLA mismatch (A-B-DR) HLA DSA Class I & class II (Neg/Pos) Maintenance therapy (TMP/TEP) Time between Basiliximab induction and IL-2 PET-scan (Days) 1 50 Male 37.42 IgA nephropathy 0.66 222 29 Deceased 1-2-1 I (Neg), II (Neg) TMP 99 2 62 Female 32.11 Systemic vasculitis 0.41 605 6 Deceased 1-1-0 I (Neg), II (Neg) TMP 385 3 46 Female 21.89 glomerulonephritis 0.16 338 13 Living 0-0-0 I (Neg), II (Neg) TMP 1008 4 65 Female 32.11 Diabetic nephropathy 0.16 156 30 Living 1-0-0 I (Neg), II (Neg) TMP 206 5 26 Male 20.58 Obstructive uropathy 0.50 408 16 Living 1-1-1 I (Neg), II (Neg) TMP 528 6 39 Male 26.57 Pyelo-/interstitial nephritis due to reflux 0.32 234 29 Deceased 1-2-0 I (Neg), II (Pos) TMP 80 7 53 Female 28.52 Glomerulonephritis, focal segmental glomerulosclerosis 0.38 188 26 Deceased 0-2-1 I (Neg), II (Pos) TMP 100 8 63 Male 26.61 Autosomal dominant polycystic kidney disease 1.50 302 18 Living 1-2-2 I (Neg), II (Neg) TMP 117 9 70 Female 23.39 Diabetic nephropathy 0.23 266 15 Deceased 2-2-1 I (Pos), II (Pos) TEP 1017 10 52 Female 16.60 Other identified renal disorders (cystic fibrosis) 0.11 170 29 Living 0-2-1 I (Neg), II (Neg) TMP 418 11 79 Male 26.29 Hypertension 0.40 345 14 Deceased 1-2-2 I (Neg), II (Neg) TMP 723 BMI ; Body Mass Index, ESRD ; End Stage Renal Disease, eGFR ; estimated Glomerular Filtration Rate, HLA ; Human Leukocyte Antigen, DSA ; Donor Specific Antibodies, TMP ; Tacrolimus, Mycophenolate mofetil, and Prednisolone, TEP ; Tacrolimus, Everolimus, and prednisolone, IL-2 ; Interleukin 2, PET ; Positions Emission Tomography, IgA ; Immunoglobulin A, n/a ; not applicable. Table 2 summarises the Banff classification findings and the time between transplantation and PET-CT. The median time from transplantation to PET-CT was 390 days (IQR: 112.5–630). Three patients (27%) had biopsy-proven acute TCMR and one patient showed borderline changes suggestive of rejection. In two cases, insufficient biopsy tissue prevented a definitive rejection diagnosis, but clinical evaluation indicated no rejection. No cases of vascular rejection were observed. Histological scoring was performed for all biopsies with sufficient tissue, with scores ranging from 0 (absence) to 3 (severe involvement). Global glomerulosclerosis was present in 5 of 9 evaluated patients (56%) (scores 0–2). Interstitial inflammation was detected in 4 of 9 patients (44%), with moderate to severe involvement (scores 2–3) in 3 patients (33%). Tubulitis was observed in 5 of 9 patients (56%), including 2 patients (22%) with severe involvement. IFTA was present in 5 of 7 biopsies (71%), with moderate to severe changes in 3 patients (43%). Periodic acid–Schiff positive arteriolar hyaline thickening was found in 3 of 9 patients (33%) (scores 1–3), while intimal arterial proliferation was present in 5 of 8 patients (63%), with mild to moderate involvement. Peritubular capillaritis was absent in all biopsies. Additionally, all biopsies were negative for C4d staining and Simian virus 40 immunohistochemistry, indicating no antibody-mediated rejection or BK nephropathy. Table 2 BANFF classification. Patient nr. Rejection (yes/no/ unclear) Rejection type Glomeruli (nr.) Global glomerulosclerosis (0–3) Interstitial inflammation (0–3) Tubulitis (0–3) IFTA (0–3) PAS + hyaline (0–3) Intima arterial proliferation (0–3) Peritubular Capillaritis (0–3) C4d (N/P) SV40 (N/P) Time between kidney transplantation and IL-2 PET-scan (days) 1 No n/a 7 0 0 0 1 0 0 0 N N 104 2 Yes Acute rejection grade I-B 7 1 3 3 - 0 0 0 N N 390 3 No n/a 11 1 0 0 1 0 2 0 N N 1014 4 No A n/a 0 - - - - - - - N N 211 5 Yes Acute rejection grade I-B 6 0 3 3 - 0 0 0 N N 533 6 No n/a 18 1 0 0 0 0 1 0 N N 84 7 No A n/a 0 - 0 0 1 - - 0 N N 104 8 Yes Borderline acute rejection 5 0 1 2 0 1 - 0 N N 121 9 Yes Acute rejection grade 1-A 21 2 2 2 2 2 2 0 N N 1021 10 No n/a 26 0 0 0 2 0 1 0 N N 422 11 No n/a 21 2 0 1 3 3 1 0 N N 727 Nr ; number, IFTA ; Interstitial fibrosis and tubular atrophy, PAS + hyaline ; periodic acid–Schiff positive arteriolar hyaline thickening, SV40 ; Simian virus 40, N/P ; negative/positive, IL-2 ; Interleukin-2, PET ; Positions Emission Tomography, n/a ; not applicable. PET analysis [¹⁸F]FB-IL2 PET-CT analysis demonstrated variability in tracer uptake among kidney transplant recipients (Table 3 ). Cortex SUV mean ranged from 5.31 to 13.23 g/ml (SUVbw), with a median of 8.24 (IQR: 6.60–10.78). The median SUV mean trended higher in rejection cases (10.39, IQR: 8.56–12.22) compared to non-rejection cases (6.67, IQR: 6.43–8.90, p = 0.23, r = 0.40). The SUV mean cortex-to-medulla ratio ranged from 1.06 to 1.99, with an overall median of 1.44 (IQR: 1.27–1.63). The median was similar between rejection cases (1.38, IQR: 1.28–1.49) and non-rejection cases (1.44, IQR: 1.27–1.83, p = 0.79, r = 0.11). Logan analysis showed that cortical V T * values ranged from 0.05 to 1.48 ml/cm 3 , with a median of 0.53 (IQR: 0.42–0.84). The distribution was higher in rejection cases, with a median of 0.80 (IQR: 0.65–1.04), compared to non-rejection cases, which had a median of 0.50 (IQR: 0.19–0.65, p = 0.23, r = 0.40). The cortex-to-medulla V T * ratio ranged from 0.28 to 1.38, with a median of 0.82 (IQR: 0.69–1.25). When comparing rejection and non-rejection groups, a statistically significant difference was found (p = 0.02, r = 0.68). The median ratio was higher in rejection cases (1.25, IQR: 1.15–1.34) compared to non-rejection cases (0.69, IQR: 0.67–0.79). Boxplots (Figs. 1 A– 1 D) illustrate the median, IQR, and individual data points for both rejection and non-rejection groups. An illustrative example of rejection vs non-rejection behaviour is reported in Fig. 2 A- 2 D. Table 3 SUV mean and Logan V T * perfusion analysis. SUV mean Logan V T * perfusion Patient number Cortex averaged ± StDv (g/ml[SUVbw]) Ratio cortex/medulla Cortex averaged ± Stdv (ml/cm 3 ) Ratio cortex/medulla 1 9.56 ± 0.63 1.26 0.77 ± 0.04 1.25 2 7.91 ± 0.66 1.47 0.53 ± 0.29 1.11 3 5.31 ± 0.88 1.27 0.05 ± 0.02 0.66 4 8.24 ± 0.76 1.73 0.53 ± 0.15 0.69 5 8.77 ± 0.77 1.25 0.69 ± 0.02 1.38 6 13.23 ± 1.01 1.92 0.50 ± 0.32 0.82 7 6.67 ± 0.79 1.06 0.34 ± 0.20 0.28 8 12.89 ± 0.83 1.29 1.39 ± 0.38 1.16 9 12.00 ± 1.33 1.53 0.92 ± 0.06 1.33 10 6.34 ± 0.81 1.44 1.48 ± 0.50 0.68 11 6.52 ± 0.69 1.99 0.05 ± 0.02 0.76 SUV mean ; Standardised uptake value mean, V T * ; Apparent volume of distribution of tracer, StDv ; Standard deviation. Flow cytometry analyses Flow cytometry analysis did not reveal any statistically significant differences in immune cell subset distributions between rejection and non-rejection groups ( Supplementary Table 1 ). This included total CD3 + lymphocytes (U = 10, p = 0.53), CD4 + helper T cells (U = 22, p = 0.16), CD8 + cytotoxic T cells (U = 7, p = 0.23), and Tregs (U = 17, p = 0.65). Correlation analyses To explore the relationship between [ 18 F]FB-IL2 PET-CT derived imaging parameters and immune-mediated kidney allograft rejection, two correlation analyses were performed. The first included all patients (n = 11) and examined associations between [ 18 F]FB-IL2 PET-CT parameters, clinical markers of graft function at time of suspected rejection, treated rejection, and peripheral immune cell subsets. The second focused on histopathological features of rejection based on the Banff classification (n = 9), excluding two patients due to non-interpretable biopsies caused by sampling error. Correlation heatmaps are visualised in Fig. 3 A (PET vs. treated rejection, kidney function, and immune cell subsets) and Fig. 3 B (PET vs. Banff features). Corresponding significance heatmaps are shown in Supplementary Figs. 2A and 2B . Among all PET-derived parameters, the cortex-to-medulla V T * ratio demonstrated the strongest and most consistent associations. This variable showed a strong positive correlation with treated rejection (r = 0.72, p = 0.01), as well as strong correlations with interstitial inflammation (r = 0.68, p = 0.04), tubulitis (r = 0.61, p = 0.08), and proteinuria (r = 0.60, p = 0.05). These findings suggest a clear relationship between elevated V T * ratios and histological indicators of acute rejection. Averaged cortex SUV mean and V T * both exhibited moderate correlations with treated rejection (r = 0.42, for both), but these did not reach statistical significance (p = 0.20, for both). In contrast, the other [ 18 F]FB-IL2 PET-CT parameter, SUV mean ratio cortex-to-medulla, showed limited or inconsistent associations across all Banff histological features and rejection status. Averaged cortex SUV mean showed a strong negative trend with IFTA (r = − 0.58, p = 0.10), suggesting a potential inverse relationship between PET tracer uptake and chronic structural injury. Finally, in relation to peripheral immune markers, the most notable finding was a strong positive correlation between averaged cortex V T * and Treg levels (r = 0.80, p = < 0.01), while averaged cortex SUV mean (r = 0.61, p = 0.05) and cortex-to-medulla V T * ratio (r = 0.50, p = 0.12) also showed strong correlations with Treg. No other immune cell subsets demonstrated meaningful or consistent associations with PET-derived measures. Discussion This proof-of-concept study evaluated, for the first time in human, the potential of [ 18 F]FB-IL2 PET-CT as a non-invasive imaging modality for detecting acute TCMR in kidney transplant recipients. Several findings demonstrated the potential diagnostic relevance of this technique. The most robust and statistically significant marker was the cortex-to-medulla V T * ratio, determined by Logan graphical analysis, which effectively differentiated rejection from non-rejection cases (p = 0.02, r = 0.68). This ratio also demonstrated strong correlations with patients who were treated for rejection (r = 0.72, p = 0.01) and histological features of TCMR, including interstitial inflammation (r = 0.68, p = 0.04) and tubulitis (r = 0.61, p = 0.08), as well as with proteinuria (r = 0.60, p = 0.05), a clinical indicator of graft injury. The use of the medulla as an internal reference likely improves signal stability by reducing variability due to renal perfusion and tracer kinetics [ 22 , 23 ]. In addition to the cortex-to-medulla ratio, other PET-derived parameters demonstrated biologically meaningful patterns that further support the physiological relevance of [¹⁸F]FB-IL2 uptake. Both V T * and SUV mean in the cortex showed moderate positive correlations with treated rejection (r = 0.42 for both), suggesting that these measures capture relevant immune activity within the graft. A particularly notable finding was the strong positive correlation between the V T in the cortex and circulating Tregs (r = 0.80, p = < 0.01). Tregs, characterised by high expression of the IL-2 receptor α-chain (CD25), play a critical role in modulating immune responses and maintaining peripheral tolerance [ 24 , 25 ]. Additional correlations were observed between Treg levels and other PET-derived parameters, in particular cortical SUV mean (r = 0.61, p = 0.05) and cortex-to-medulla V T * ratio (r = 0.50, p = 0.12). These findings raise the possibility that elevated [¹⁸F]FB-IL2 uptake may not solely reflect effector T cell infiltration, but could also indicate intragraft accumulation of Tregs, potentially as a counter-regulatory response during rejection. Whether this accumulation is protective or simply reflects immunologic activity remains unclear and warrants further investigation. Comparison with Existing Literature Our findings are consistent with previous work demonstrating the feasibility of IL-2 receptor-targeted imaging in solid organ transplantation. For instance, 99m Tc-HYNIC-IL-2 scintigraphy has shown high specificity for moderate to severe rejection in lung transplant recipients, but with limited sensitivity for early immune activation [ 26 ]. Compared to these SPECT-based techniques, [¹⁸F]FB-IL2 PET-CT offers higher spatial resolution, the ability to perform quantitative kinetic modelling, and a more favourable biodistribution profile. Beyond the transplant setting, Van de Donk et al. demonstrated the safety and feasibility of [¹⁸F]FB-IL2 PET imaging in patients with metastatic melanoma, confirming its utility for detecting activated T cell populations in vivo [ 11 ]. Our study builds upon this foundation by evaluating the tracer in the context of solid organ transplantation, where immune responses are directed against allograft tissue rather than tumour cells. To our knowledge, this is the first study to demonstrate correlations between [¹⁸F]FB-IL2 PET uptake and histological features of acute TCMR, underscoring its potential role in non-invasive transplant rejection assessment. Moreover, the relatively homogeneous structure of the kidney allograft, compared to more anatomically variable organs such as the lungs, may enhance the robustness of the cortex-to-medulla V T * ratio as a quantitative imaging biomarker. This structural distinction supports the rationale for using an internal reference approach in kidney imaging, where well-defined cortical and medullary compartments enable robust intrarenal comparisons. The observed associations between PET signals and systemic immune markers further underscore the potential of [¹⁸F]FB-IL2 PET-CT to bridge local and peripheral immune dynamics. This is in line with prior evidence suggesting that systemic T cell activation can precede histopathological manifestations of rejection [ 27 , 28 ], supporting the role of PET imaging in early immune surveillance. Strengths and Limitations A major strength of this study is its integrative, multimodal approach, combining data from [¹⁸F]FB-IL2 PET-CT imaging, Banff histopathology, kidney function markers, and peripheral immune profiling. Whereas percutaneous biopsy is subject to sampling error by evaluating only a small portion of the graft, which can lead to misdiagnosis or underdiagnosis of rejection[ 29 , 30 ], [¹⁸F]FB-IL2 PET-CT enables whole-organ immune activity assessment. This advantage was underscored by two cases in our cohort where biopsy samples were non-diagnostic due to insufficient tissue, highlighting a key limitation of biopsy-based diagnostics. Moreover, this study was conducted in a real-world clinical setting, offering initial evidence for the feasibility of integrating [ 18 F]FB-IL2 PET-CT into standard transplant workflows. However, several limitations must be acknowledged. First, the small sample size (n = 11) limits statistical power and generalizability. Second, logistical constraints, such as scanner availability, the requirement for pre-treatment imaging and the exclusion of patients who had recently received basiliximab, posed challenges to timely patient recruitment. Third, the cross-sectional nature of this study precludes assessment of the predictive value of [ 18 F]FB-IL2 PET-CT parameters towards long-term graft outcomes. Fourth, interpretation of tracer binding is inherently complex, as IL-2 receptor expression is not exclusive to activated effector T cells but is also present on Tregs and natural killer cells. Future studies should aim to validate these findings in larger, longitudinal cohorts and establish standardised protocols for PET acquisition and analysis to support clinical implementation. Finally, a methodological limitation of this study is the use of an image-derived whole blood input function without metabolite or blood-to-plasma correction, due to the absence of arterial or venous blood sampling. While this deviates from the formal assumptions of Logan graphical analysis, prior preclinical and clinical studies have demonstrated minimal in vivo metabolism of [¹⁸F]FB-IL2 [ 11 , 14 ], supporting the plausibility of this approximation in a first-in-human, non-invasive transplant setting. Nonetheless, V T * estimates should be interpreted with caution given the lack of a validated plasma input function. Clinical Implications and Future Directions Despite its exploratory nature, this study provides compelling evidence that [¹⁸F]FB-IL2 PET-CT is a safe, biologically informative, non-invasive and potentially clinically valuable tool for assessing immune activity in kidney allografts. Among the evaluated parameters, the cortex-to-medulla V T * ratio emerged as the most reproducible and diagnostically informative imaging metric, correlating with treated rejection, interstitial inflammation, tubulitis, proteinuria, and circulating Treg levels. Furthermore, the cortex-to-medulla V T * ratio showed statistically significant discrimination between rejection and non-rejection cases. Its robustness may derive from the use of the medulla as an internal reference [ 22 , 23 ]. [¹⁸F]FB-IL2 was well tolerated in all participants and no adverse events related to immune activation were observed, consistent with prior human studies with different IL-2 tracers [ 11 , 26 , 31 , 32 ]. This favourable safety profile supports its potential for clinical use in transplant surveillance. Future research should aim to validate the above findings in larger, prospective studies with longitudinal follow-up to assess predictive value. Standardization of imaging protocols, particularly for Logan analysis-derived V T * measures, and comparative evaluation against emerging biomarkers such as donor-derived cell-free DNA and urinary chemokines will be essential to define the place of [¹⁸F]FB-IL2 PET-CT in clinical practice. By enabling earlier and more comprehensive rejection assessment, PET imaging could transform transplant surveillance, reduce reliance on invasive biopsies, and ultimately improve long-term graft outcomes. Conclusion This study provides first-in-human evidence that [¹⁸F]FB-IL2 PET-CT may serve as a non-invasive imaging modality for detecting acute TCMR in kidney transplant recipients. By integrating molecular imaging with histopathology, graft function, and peripheral immune profiling, [¹⁸F]FB-IL2 PET-CT offers a comprehensive, whole-organ perspective on intragraft immune activity, overcoming key limitations of biopsy-based assessment. The cortex-to-medulla V T * ratio, determined by Logan graphical analysis, emerged as a promising clinically informative imaging biomarker, associated not only with rejection but also with functional and immunological indicators of intragraft inflammation. These findings suggest that [¹⁸F]FB-IL2 PET-CT could complement or, in selected scenarios, reduce the need for biopsy, particularly when conventional diagnostics are inconclusive or carry procedural risk. Future multicentre studies with longitudinal follow-up are essential to validate these findings, standardise PET-based biomarkers, and define their role within personalised transplant monitoring strategies. Abbreviations [ 18 F]FB-IL2 Fluorine-18-labeled fluoro-benzoyl interleukin-2 CT Computed tomography EDTA Ethylenediaminetetraacetic acid eGFR Estimated glomerular filtration rate HLA Human leukocyte antigen IFTA Interstitial fibrosis and tubular atrophy IL-2 Interleukin-2 IQRs Interquartile ranges Ki Net influx rate constant PET Positron emission tomography ROIs Regions of interest SUV mean Mean standardised uptake value TCMR T-cell-mediated rejection TEMRA Terminally differentiated effector memory T cells Tregs Regulatory T cells UMCG University Medical Centre Groningen V T * Apparent volume of distribution Declarations Authorship contribution NB Conceptualization, methodology, formal analysis, investigation, resources, data curation, writing – original draft, visualization and project administration. AA Formal analysis, data curation, writing – review & editing and visualization. JS Conceptualization, methodology, writing – review & editing and supervision. SB Conceptualization, methodology, investigation, resources, data curation, review & editing, supervision, project administration and funding acquisition. EV Conceptualization, methodology, resources and writing – review & editing. TB Methodology, formal analysis and writing – review & editing. AD Formal analysis and writing – review & editing. AG Conceptualization, methodology, writing – review & editing and supervision. RS Conceptualization, methodology, resources, writing – review & editing, supervision and project administration. RP Conceptualization, methodology, resources, writing – review & editing, supervision, project administration and funding acquisition. Disclosure E.F.J. de Vries declares to have been involved in contracted research for Hoffmann-La Roche, Eli Lilly, Bristol Myers Squibb, Novartis, Janssen-Cilag BV, Mesentech, GE Healthcare and GlaxoSmithKline, not related to this study and paid to the institution in the past 5 years. The other authors of this manuscript have no conflicts of interest to declare. Funding This study was supported by an unrestricted research grant from Chiesi Pharmaceuticals and the University Medical Centre Groningen Innovation Prize. Data availability The datasets generated and/or analysed during the current study are available from the corresponding author on reasonable request. References Krishnan N, Higgins R, Short A, Zehnder D, Pitcher D, Hudson A et al (2015) Kidney Transplantation Significantly Improves Patient and Graft Survival Irrespective of BMI: A Cohort Study. Am J Transplant. ;15 Alasfar S, Kodali L, Schinstock CA (2023) Current Therapies in Kidney Transplant Rejection. J Clin Med. ;12 Cooper JE (2020) Evaluation and Treatment of Acute Rejection in Kidney Allografts. Clin J Am Soc Nephrol 15:430–438 Al-Awwa I, Hariharan S, First M (1998) Importance of allograft biopsy in renal transplant recipients: Correlation between clinical and histological diagnosis. Am J Kidney Dis 31:S15–S18 Morgan TA, Chandran S, Burger IM, Zhang CA, Goldstein RB (2016) Complications of Ultrasound-Guided Renal Transplant Biopsies. Am J Transplant 16:1298–1305 Redfield RR, McCune KR, Rao A, Sadowski E, Hanson M, Kolterman AJ et al (2016) Nature, timing, and severity of complications from ultrasound-guided percutaneous renal transplant biopsy. Transpl Int 29:167–172 Nankivell BJ, Chapman JR (2006) The Significance of Subclinical Rejection and the Value of Protocol Biopsies. Am J Transplant 6:2006–2012 Wilczek HE (1990) Percutaneous needle biopsy of the renal allograft. A clinical safety evaluation of 1129 biopsies. Transplantation 50:790–797 Song Y, Wang Y, Wang W, Xie Y, Zhang J, Liu J et al (2025) Advancements in noninvasive techniques for transplant rejection: from biomarker detection to molecular imaging. J Transl Med 23:147 Leonard WJ, Krönke M, Peffer NJ, Depper JM, Greene WC (1985) Interleukin 2 receptor gene expression in normal human T lymphocytes. Proc Natl Acad Sci U S A 82:6281–6285 van de Donk PP, Wind TT, Hooiveld-Noeken JS, van der Veen EL, Glaudemans AWJM, Diepstra A et al (2021) Interleukin-2 PET imaging in patients with metastatic melanoma before and during immune checkpoint inhibitor therapy. Eur J Nucl Med Mol Imaging 48:4369–4376 Signore A, Picarelli A, Annovazzi A, Britton KE, Grossman AB, Bonanno E et al (2003) 123I-Interleukin-2: biochemical characterization and in vivo use for imaging autoimmune diseases. Nucl Med Commun 24:305–316 Loose D, Signore A, Staelens L, Bulcke K, Vanden, Vermeersch H, Dierckx RA et al (2008) 123I-Interleukin-2 uptake in squamous cell carcinoma of the head and neck carcinoma. Eur J Nucl Med Mol Imaging 35:281–286 Di Gialleonardo V, Signore A, Willemsen ATM, Sijbesma JWA, Dierckx RAJO, de Vries EFJ (2012) Pharmacokinetic modelling of N-(4-[(18)F]fluorobenzoyl)interleukin-2 binding to activated lymphocytes in an xenograft model of inflammation. Eur J Nucl Med Mol Imaging 39:1551–1560 Kovarik JM, Kahan BD, Rajagopalan PR, Bennett W, Mulloy LL, Gerbeau C et al (1999) Population pharmacokinetics and exposure-response relationships for basiliximab in kidney transplantation. The U.S. Simulect Renal Transplant Study Group. Transplantation 68:1288–1294 International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) (2016) ICH E6(R2) Good Clinical Practice: Integrated Addendum to ICH E6(R1) Salis P, Caccamo C, Verzaro R, Gruttadauria S, Artero M (2008) The role of basiliximab in the evolving renal transplantation immunosuppression protocol. Biologics 2:175–188 Roufosse C, Simmonds N, Clahsen-van Groningen M, Haas M, Henriksen KJ, Horsfield C et al (2018) A 2018 Reference Guide to the Banff Classification of Renal Allograft Pathology. Transplantation 102:1795–1814 Hartimath SV, Manuelli V, Zijlma R, Signore A, Nayak TK, Freimoser-Grundschober A et al (2018) Pharmacokinetic properties of radiolabeled mutant Interleukin-2v: a PET imaging study. Oncotarget 9:7162–7174 Besson FL, Faure S (2024) PET KinetiX-A Software Solution for PET Parametric Imaging at the Whole Field of View Level. J imaging Inf Med 37:842–850 Logan J, Fowler JS, Volkow ND, Wolf AP, Dewey SL, Schlyer DJ et al (1990) Graphical analysis of reversible radioligand binding from time-activity measurements applied to [N-11C-methyl]-(-)-cocaine PET studies in human subjects. J Cereb Blood Flow Metab 10:740–747 Päivärinta J, Anastasiou IA, Koivuviita N, Sharma K, Nuutila P, Ferrannini E et al (2023) Renal Perfusion, Oxygenation and Metabolism: The Role of Imaging. J Clin Med 12:5141 Kopitkó C, Medve L, Gondos T, Soliman KMM, Fülöp T (2022) Mediators of Regional Kidney Perfusion during Surgical Pneumo-Peritoneum Creation and the Risk of Acute Kidney Injury—A Review of Basic Physiology. J Clin Med 11:2728 Chinen T, Kannan AK, Levine AG, Fan X, Klein U, Zheng Y et al (2016) An essential role for the IL-2 receptor in Treg cell function. Nat Immunol 17:1322–1333 Boyman O, Sprent J (2012) The role of interleukin-2 during homeostasis and activation of the immune system. Nat Rev Immunol 12:180–190 Telenga ED, van der Bij W, de Vries EFJ, Verschuuren EAM, Timens W, Luurtsema G et al (2019) 99mTc-HYNIC-IL-2 scintigraphy to detect acute rejection in lung transplantation patients: a proof-of-concept study. EJNMMI Res 9:41 Brunet M, Millán López O, López-Hoyos M (2016) T-Cell Cytokines as Predictive Markers of the Risk of Allograft Rejection. Ther Drug Monit 38:S21–S28 Sabek O, Dorak MT, Kotb M, Gaber AO, Gaber L (2002) Quantitative detection of T-cell activation markers by real-time PCR in renal transplant rejection and correlation with histopathologic evaluation. Transplantation 74:701–707 Sorof JM, Vartanian RK, Olson JL, Tomlanovich SJ, Vincenti FG, Amend WJ (1995) Histopathological concordance of paired renal allograft biopsy cores. Effect on the diagnosis and management of acute rejection. Transplantation 60:1215–1219 Nissen CJ, Moreno V, Davis VG, Walker PD (2022) Increasing Incidence of Inadequate Kidney Biopsy Samples Over Time: A 16-Year Retrospective Analysis From a Large National Renal Biopsy Laboratory. Kidney Int Rep 7:251–258 Loose D, Signore A, Staelens L, Bulcke K, Vanden, Vermeersch H, Dierckx RA et al (2008) (123)I-Interleukin-2 uptake in squamous cell carcinoma of the head and neck carcinoma. Eur J Nucl Med Mol Imaging 35:281–286 Signore A, Picarelli A, Annovazzi A, Britton KE, Grossman AB, Bonanno E et al (2003) 123I-Interleukin-2: biochemical characterization and in vivo use for imaging autoimmune diseases. Nucl Med Commun 24:305–316 Additional Declarations Yes there is potential Competing Interest. E.F.J. de Vries declares to have been involved in contracted research for Hoffmann-La Roche, Eli Lilly, Bristol Myers Squibb, Novartis, Janssen-Cilag BV, Mesentech, GE Healthcare and GlaxoSmithKline, not related to this study and paid to the institution in the past 5 years. The other authors of this manuscript have no conflicts of interest to declare. Supplementary Files SupplementaryInformationIL2PET.docx Supplementary Information IL-2 PET Cite Share Download PDF Status: Under Review Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8220933","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":555336442,"identity":"a63dfb23-300a-40bb-bc43-7d3d4624ea97","order_by":0,"name":"Robert A. Pol","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAlElEQVRIiWNgGAWjYLCCBxUwBtFaEs7AGERrSWwjRYt5A+/BB4nzDkfz9y9ge0CUFpkDfMkGidsO58648YDdgCgtEgw8ZhIgLRskDrBJkKBlDulaGoBa+BuI1cIM9EvCsXSgXxjbiNTC3nvwwYca69z+/sPHJD4Qo4WBmQemObGBKA1AANPCf4BYHaNgFIyCUTDSAAAN0C95PbiFSgAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-2236-5233","institution":"University of Groningen and University Medical Center Groningen","correspondingAuthor":true,"prefix":"","firstName":"Robert","middleName":"A.","lastName":"Pol","suffix":""},{"id":555336443,"identity":"e0898f63-c439-4960-8131-f814f0475651","order_by":1,"name":"Niels Bloemendal","email":"","orcid":"","institution":"University of Groningen and University Medical Center Groningen","correspondingAuthor":false,"prefix":"","firstName":"Niels","middleName":"","lastName":"Bloemendal","suffix":""},{"id":555336444,"identity":"177c6572-f253-45fa-b786-9c2a42e03043","order_by":2,"name":"Alessia Artesani","email":"","orcid":"","institution":"Humanitas University","correspondingAuthor":false,"prefix":"","firstName":"Alessia","middleName":"","lastName":"Artesani","suffix":""},{"id":555336445,"identity":"5af6a2f7-1560-4076-896e-14ac35a0768b","order_by":3,"name":"Jan-Stephan Sanders","email":"","orcid":"","institution":"University of Groningen and University Medical Center Groningen","correspondingAuthor":false,"prefix":"","firstName":"Jan-Stephan","middleName":"","lastName":"Sanders","suffix":""},{"id":555336446,"identity":"3cb23b45-f382-4daa-9929-11aa09103cbf","order_by":4,"name":"Stan Benjamens","email":"","orcid":"","institution":"Ikazia hospital","correspondingAuthor":false,"prefix":"","firstName":"Stan","middleName":"","lastName":"Benjamens","suffix":""},{"id":555336447,"identity":"268042e4-a94f-4225-848f-2d61c5282e6b","order_by":5,"name":"Erik de Vries","email":"","orcid":"https://orcid.org/0000-0002-6915-1590","institution":"University Medical Center Groningen","correspondingAuthor":false,"prefix":"","firstName":"Erik","middleName":"","lastName":"de Vries","suffix":""},{"id":555336448,"identity":"ed71f336-b8f7-49ad-bc86-8970d6908480","order_by":6,"name":"Theo Bijma","email":"","orcid":"","institution":"University of Groningen and University Medical Center Groningen","correspondingAuthor":false,"prefix":"","firstName":"Theo","middleName":"","lastName":"Bijma","suffix":""},{"id":555336449,"identity":"75d1b22f-7281-48bc-a830-2fe59e3a3aab","order_by":7,"name":"Arjan Diepstra","email":"","orcid":"https://orcid.org/0000-0001-9239-1050","institution":"University Medical Center Groningen","correspondingAuthor":false,"prefix":"","firstName":"Arjan","middleName":"","lastName":"Diepstra","suffix":""},{"id":555336450,"identity":"7004cd19-8a9a-433b-8dcf-ae343bd408e5","order_by":8,"name":"Andor Glaudemans","email":"","orcid":"https://orcid.org/0000-0001-8081-0641","institution":"University of Groningen","correspondingAuthor":false,"prefix":"","firstName":"Andor","middleName":"","lastName":"Glaudemans","suffix":""},{"id":555336451,"identity":"994d0bb1-598d-4434-9719-1cd18f3dda72","order_by":9,"name":"Riemer Slart","email":"","orcid":"","institution":"University of Groningen, University Medical Center Groningen, Groningen, Netherlands","correspondingAuthor":false,"prefix":"","firstName":"Riemer","middleName":"","lastName":"Slart","suffix":""}],"badges":[],"createdAt":"2025-11-27 10:25:53","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8220933/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8220933/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101935622,"identity":"bba22a55-ae49-4bdf-9180-8bdb7d9e4d43","added_by":"auto","created_at":"2026-02-05 08:27:46","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":47136,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigures 1A-1D: [\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e18\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003eF]FB-IL2 PET-CT imaging parameters in kidney transplant recipients with and without acute rejection.\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e[¹⁸F]FB-IL2\u003c/strong\u003e; fluorine-18–labelled fluoro-benzoyl interleukin-2, \u003cstrong\u003ePET-CT\u003c/strong\u003e; positron emission tomography–computed tomography\u003cstrong\u003e, SUV\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003emean\u003c/strong\u003e\u003c/sub\u003e; mean Standardised uptake value, \u003cstrong\u003eSUV\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e- C\u003c/strong\u003e; mean cortical standardised uptake value, \u003cstrong\u003eSUV\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e– C/M\u003c/strong\u003e; mean standerdised uptake value cortex-to-medulla ratio, \u003cstrong\u003eV\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003eT\u003c/strong\u003e\u003c/sub\u003e\u003csup\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/sup\u003e; Apparent volume of distribution of tracer, \u003cstrong\u003eV\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003eT\u003c/strong\u003e\u003c/sub\u003e\u003csup\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e - C\u003c/strong\u003e; Apparent cortical volume of distribution, \u003cstrong\u003eV\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003eT\u003c/strong\u003e\u003c/sub\u003e\u003csup\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e - C/M\u003c/strong\u003e; apparent volume of distribution cortex-to-medulla ratio.\u003cbr\u003e\nShown are data from kidney transplant recipients with biopsy-proven acute T-cell–mediated rejection (n = 4) and those without rejection (n = 7). Boxes represent interquartile ranges, horizontal lines indicate medians, whiskers denote data range, and dots show individual values. p-values from two-sided Wilcoxon rank-sum tests are shown above each panel. (\u003cstrong\u003eA\u003c/strong\u003e) Cortical SUV\u003csub\u003emean\u003c/sub\u003e, (\u003cstrong\u003eB\u003c/strong\u003e) cortex-to-medulla SUV\u003csub\u003emean\u003c/sub\u003e ratio, (\u003cstrong\u003eC\u003c/strong\u003e) cortical V\u003csub\u003eT\u003c/sub\u003e\u003csup\u003e*\u003c/sup\u003e, and (\u003cstrong\u003eD\u003c/strong\u003e) cortex-to-medulla V\u003csub\u003eT\u003c/sub\u003e\u003csup\u003e*\u003c/sup\u003e ratio.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8220933/v1/0164dc12f37e0070d6641c54.jpg"},{"id":101935546,"identity":"efb73635-8519-4e7a-a41a-4effb0bba27a","added_by":"auto","created_at":"2026-02-05 08:27:21","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":76297,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigures 2A-2D: [\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e18\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003eF]FB-IL2 PET-CT image results in kidney transplant recipients with and without acute rejection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e[¹⁸F]FB-IL2\u003c/strong\u003e; fluorine-18–labelled fluoro-benzoyl interleukin-2, \u003cstrong\u003ePET-CT\u003c/strong\u003e; positron emission tomography-computed tomography\u003cstrong\u003e, V\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003eT\u003c/strong\u003e\u003c/sub\u003e\u003csup\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e – C/M\u003c/strong\u003e; apparent volume of distribution cortex-to-medulla ratio. Representative [¹⁸F]FB-IL2 PET images and corresponding volume of distribution cortex-to-medulla (V\u003csub\u003eT\u003c/sub\u003e\u003csup\u003e*\u003c/sup\u003e – C/M) ratio maps from kidney transplant recipients with biopsy-proven acute T-cell–mediated rejection (\u003cem\u003eupper row\u003c/em\u003e) and without rejection (\u003cem\u003elower row\u003c/em\u003e). \u003cstrong\u003e(A, C)\u003c/strong\u003e PET images show uptake patterns in the kidney region acquired with [¹⁸F]FB-IL2. \u003cstrong\u003e(B, D)\u003c/strong\u003e V\u003csub\u003eT\u003c/sub\u003e\u003csup\u003e*\u003c/sup\u003e – C/M ratio maps indicating differential tracer uptake in the cortex and medulla regions. The bar on the right shows the color-coded V\u003csub\u003eT\u003c/sub\u003e\u003csup\u003e*\u003c/sup\u003e – C/M values, with Logan V\u003csub\u003eT\u003c/sub\u003e\u003csup\u003e*\u003c/sup\u003e – C/M ratios greater than 1 indicating rejection behaviour.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8220933/v1/dd52a0a42a64e02303570cf2.jpg"},{"id":101935502,"identity":"7d5ac087-0c39-4028-bf1c-498ea7464606","added_by":"auto","created_at":"2026-02-05 08:27:17","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":61248,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 3A-B: Correlation heatmaps of [\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e18\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003eF]FB-IL2 PET-CT parameters with clinical, immunological, and histopathological features of kidney allograft rejection.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e[¹⁸F]FB-IL2\u003c/strong\u003e; fluorine-18–labeled fluoro-benzoyl interleukin-2, \u003cstrong\u003ePET-CT\u003c/strong\u003e; positron emission tomography–computed tomography, \u003cstrong\u003eSUV\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e- C\u003c/strong\u003e; mean cortical standardised uptake value, \u003cstrong\u003eSUV\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e– C/M\u003c/strong\u003e; mean standerdised uptake value cortex-to-medulla ratio, \u003cstrong\u003eV\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003eT\u003c/strong\u003e\u003c/sub\u003e\u003csup\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e - C\u003c/strong\u003e; apparent cortical volume of distribution, \u003cstrong\u003eV\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003eT\u003c/strong\u003e\u003c/sub\u003e\u003csup\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e – C/M\u003c/strong\u003e; apparent volume of distribution cortex-to-medulla ratio, \u003cstrong\u003eeGFR\u003c/strong\u003e; estimated glomerular filtration rate, \u003cstrong\u003eCD3⁺\u003c/strong\u003e; \u003cstrong\u003eCD4⁺\u003c/strong\u003e; \u003cstrong\u003eCD8⁺\u003c/strong\u003e; peripheral T cell subsets, \u003cstrong\u003eTreg\u003c/strong\u003e; regulatory T cells, \u003cstrong\u003eIFTA\u003c/strong\u003e; interstitial fibrosis and tubular atrophy.\u003cbr\u003e\n(\u003cstrong\u003eA\u003c/strong\u003e) Heatmap showing Spearman correlation coefficients (r) between PET-derived imaging parameters and clinical variables at the time of suspected rejection, including treated rejection, kidney function (eGFR, serum creatinine, proteinuria), and peripheral immune cell subsets (n = 11). (\u003cstrong\u003eB\u003c/strong\u003e) Heatmap showing correlations between PET parameters and Banff histological features of rejection, including interstitial inflammation, tubulitis, and IFTA (n = 9; 2 biopsies excluded due to non-interpretable sampling).\u003cbr\u003e\nColor gradients indicate the strength and direction of correlations (red = positive, blue = negative). Numerical r-values are displayed in each tile; corresponding p-values are shown in Supplementary Figures 1A and 1B. Asterisks indicate significance levels: p \u0026lt; 0.1 (*), p \u0026lt; 0.05 (**), p \u0026lt; 0.01 (***).\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8220933/v1/9e1ef39648f44a1589c0b148.jpg"},{"id":101935783,"identity":"a0f66e47-ad04-4410-b895-1d437e8e028f","added_by":"auto","created_at":"2026-02-05 08:28:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1529542,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8220933/v1/54a74210-c8a9-4d3d-b42a-388f05c34730.pdf"},{"id":101935643,"identity":"2a320598-a2b8-484b-bf36-f2cb0df6be12","added_by":"auto","created_at":"2026-02-05 08:27:50","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":260118,"visible":true,"origin":"","legend":"Supplementary Information IL-2 PET","description":"","filename":"SupplementaryInformationIL2PET.docx","url":"https://assets-eu.researchsquare.com/files/rs-8220933/v1/610d41d3dafd9030c0d7014b.docx"}],"financialInterests":"\u003cb\u003eYes\u003c/b\u003e there is potential Competing Interest.\nE.F.J. de Vries declares to have been involved in contracted research for Hoffmann-La Roche, Eli Lilly, Bristol Myers Squibb, Novartis, Janssen-Cilag BV, Mesentech, GE Healthcare and GlaxoSmithKline, not related to this study and paid to the institution in the past 5 years. The other authors of this manuscript have no conflicts of interest to declare.","formattedTitle":"Non-Invasive Detection of Acute Kidney Allograft Rejection Using [18F]FB-IL2 PET-CT: A First in Human Proof-of-Concept Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eKidney transplantation is the preferred treatment for patients with end-stage kidney disease, significantly improving both survival rates and quality of life [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]​. Despite advancements in immunosuppressive therapies, acute T-cell-mediated rejection (TCMR) continues to be an important challenge, particularly in the first-year post-transplantation, with substantial implications for long-term graft survival [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Early and accurate diagnosis of acute TCMR is essential for timely intervention to preserve graft function and patient health [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Currently, the gold standard for diagnosing kidney allograft rejection is a percutaneous kidney biopsy [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]​. While effective, biopsy carries risks including bleeding, pain, infection and graft loss ​[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Moreover, the small size of tissue biopsy may lead to sampling error and false-negative results, highlighting the need for non-invasive diagnostic alternatives [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Molecular imaging, particularly positron emission tomography-computed tomography (PET-CT), offers a promising alternative [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. One such approach involves the use of the tracer 4-[\u003csup\u003e18\u003c/sup\u003eF]fluorobenzoyl interleukin-2 ([\u003csup\u003e18\u003c/sup\u003eF]FB-IL2), designed to visualise infiltrating T cells within the transplanted kidney. Interleukin-2 (IL-2) receptors, which are highly expressed on activated T-lymphocytes, serve as a primary target for this imaging technique [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]​. Previous clinical work has demonstrated that [\u0026sup1;⁸F]FB-IL2 PET imaging is safe and feasible for visualizing IL-2 receptor expression in solid tumours, supporting its potential for broader immunological applications [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]​. The application of [\u003csup\u003e18\u003c/sup\u003eF]FB-IL2 PET-CT in kidney transplantation lies in its potential to provide a non-invasive, whole-organ assessment of immune activity, potentially detecting subclinical rejection and monitoring therapeutic responses, offering a promising alternative to the limitations associated with biopsies. This proof-of-concept study primarily aims, in a first-in-human application, to investigate whether [\u003csup\u003e18\u003c/sup\u003eF]FB-IL2 PET-CT can act as a non-invasive diagnostic tool for acute TCMR by correlating [\u003csup\u003e18\u003c/sup\u003eF]FB-IL2 PET-CT findings with histological results from kidney allograft biopsies. IL-2 can activate lymphocytes, raising concerns about immune stimulation and exacerbation of rejection [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. While a previous study showed that [\u003csup\u003e18\u003c/sup\u003eF]FB-IL2 PET-CT was well tolerated, the secondary aim of this study is to evaluate its safety, particularly regarding immune activation [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003eStudy design\u003c/b\u003e \u003cb\u003e\u0026amp; population\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis was an observational proof-of-concept study aimed to explore the potential of [\u003csup\u003e18\u003c/sup\u003eF]FB-IL2 PET-CT in detecting acute TCMR in kidney transplant recipients. The study was conducted at the University Medical Centre Groningen (UMCG) and included kidney transplant recipients (aged between 18 and 80 years) with a clinical indication for a biopsy due to suspected rejection. Exclusion criteria included patients with multiple organ transplants, severe comorbidities that could interfere with the study, contraindications for biopsy or PET-CT imaging (e.g. known allergies for contrast agents and claustrophobia), pregnant or breastfeeding women and patients who had recently (\u0026le;\u0026thinsp;15 days) received basiliximab. Basiliximab, an IL-2 receptor antagonist with a half-life of approximately 7.4 days, suppresses T cell activation and could interfere with the ability of [\u003csup\u003e18\u003c/sup\u003eF]FB-IL2 PET-CT scans to accurately detect immune activity related to allograft rejection [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The [\u003csup\u003e18\u003c/sup\u003eF]FB-IL2 PET-CT scan was scheduled following clinical suspicion of rejection and coordinated with biopsy planning, but was always performed prior to the initiation of any anti-rejection therapy to ensure that imaging captured the untreated immunological state. The study was conducted in accordance with the Declaration of Helsinki and the Medical Research Involving Human Subjects Act, and received approval from the local Medical Ethics Committee. Written informed consent was obtained from all participants. The study was registered at ClinicalTrials.gov under the identifier NCT201501004.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003ePatient Evaluation\u003c/h2\u003e\u003cp\u003eAt study entry, clinical data were retrieved from patient records, including medical history, transplant indication, donor characteristics, immunosuppressive regimen, transplant function at the moment of suspected rejection (i.e. estimated glomerular filtration rate (eGFR), serum creatinine and proteinuria), as well as reason for suspected rejection. Laboratory parameters (blood count and blood chemistry) were also recorded. Transplant-specific data included donor type (living or deceased), human leukocyte antigen (HLA) mismatch compatibility, and the presence of donor-specific anti-HLA antibodies (DSA). A total of 15 kidney transplant recipients meeting inclusion criteria were consecutively enrolled. Patients were classified into rejection and non-rejection groups, all continuing standard nephrological care following hospital guidelines. Safety monitoring included structured assessment of adverse events, serious adverse events, and suspected unexpected serious adverse reactions, following Good Clinical Practice guidelines [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Given the IL-2 receptor-binding properties of [\u003csup\u003e18\u003c/sup\u003eF]FB-IL2, special attention was given to potential immune activation, though this risk was considered minimal due to the sub-pharmacological dose (\u0026lt;\u0026thinsp;50 \u0026micro;g). To monitor for any signs of immune-related or general adverse reactions, all patients were clinically observed for at least one hour after the scan to detect any acute reactions (e.g., transient fever, headache, localised inflammation, claustrophobia, or allergic responses), were followed up within 24 hours to assess for delayed effects, and had their medical records reviewed up to four weeks post-scan for any late-onset events. No systemic effects were expected, as confirmed in preclinical and human trials, where [\u003csup\u003e18\u003c/sup\u003eF]FB-IL2 was well tolerated without evidence of excessive T cell stimulation [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. To ensure patient safety, clinical management decisions remained biopsy-based, preventing risk of delay in treatment. Percutaneous kidney biopsies were performed under ultrasonographic guidance in all patients with suspected allograft rejection and evaluated, as part of routine clinical assessment, by an expert pathologist using the Banff-classification [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]​. In case of inconclusive histological assessment, the clinical diagnosis was used to determine rejection status.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003ePET-CT procedure\u003c/h3\u003e\n\u003cp\u003eAll patients underwent [\u003csup\u003e18\u003c/sup\u003eF]FB-IL2 PET-CT imaging at the Department of Nuclear Medicine and Molecular Imaging, UMCG. Following a low-dose CT scan for attenuation correction, a targeted dose of 200 MBq [\u003csup\u003e18\u003c/sup\u003eF]FB-IL2 was administered intravenously over 5 minutes. The median administered activity was 197 MBq (interquartile range [IQR]: 142.1\u0026ndash;202.08 MBq). A 60-minute dynamic PET scan (Biograph Vision mCT, Siemens Healthcare GmbH, Erlangen, Germany) was initiated simultaneously with tracer administration to assess time-dependent uptake and accumulation in the transplanted kidney. Images were reconstructed using an ordered-subset expectation maximization (OSEM) algorithm (4 iterations, 5 subsets) and corrected for detector normalization, deadtime, isotope decay, photon attenuation, random coincidences, and scatter. PET images were reconstructed into a series of 26 frames (7 \u0026times; 10 seconds (s), 2 \u0026times; 30 s, 3 \u0026times; 60 s, 2 \u0026times; 120 s, 2 \u0026times; 180 s, 5 \u0026times; 300 s, and 5 \u0026times; 600 s). Spherical regions of interest (ROIs) with a volume of 0.52 cm\u0026sup3; were defined for the kidney cortex and medulla. Mean [\u003csup\u003e18\u003c/sup\u003eF]FB-IL2 uptake at 60 minutes post-injection was corrected for body weight and injected dose to generate Standardised Uptake Values (SUV\u003csub\u003emean\u003c/sub\u003e) in the cortex and medulla, from which cortex-to-medulla ratios were calculated. Studies have shown that the binding of [\u003csup\u003e18\u003c/sup\u003eF]FB-IL2 is reversible [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. For this reason, Logan graphical analysis was used to determine the kinetic parameters, using the KinetiX package, a plug-in for the OsiriX DICOM viewer (Pixmeo SARL, Geneva, Switzerland) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. An image-derived whole blood activity curve was extracted from the aorta and used as input function for Logan graphical analysis to estimate the apparent total distribution volume (V\u003csub\u003eT\u003c/sub\u003e\u003csup\u003e*\u003c/sup\u003e) parameter, which represents distribution of reversible tracer binding and perfusion [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Although Logan analysis typically uses a metabolite-corrected plasma input function to streamline the procedure and minimise patient burden, we instead employed an image-derived whole-blood curve without venous plasma sampling. This approach is supported by preclinical data showing that [\u0026sup1;⁸F]FB-IL2 exhibits reversible binding kinetics and limited in vivo metabolism [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], as well as by clinical findings in melanoma patients indicating minimal plasma metabolite formation during the imaging timeframe [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. By using the image-derived whole blood curve as input function, we have assumed that the blood plasma ratio is constant over time and across patients and as a result a constant bias was introduced in the apparent V\u003csub\u003eT\u003c/sub\u003e\u003csup\u003e*\u003c/sup\u003e. By taking cortex-to-medulla V\u003csub\u003eT\u003c/sub\u003e\u003csup\u003e*\u003c/sup\u003e ratios this bias would cancel out. Logan linearization was performed from the onset of visually assessed linearity in the tissue activity curves and a T* = 30 min threshold was applied. V\u003csub\u003eT\u003c/sub\u003e\u003csup\u003e*\u003c/sup\u003e values were calculated separately for the cortex and medulla, and corresponding cortex-to-medulla V\u003csub\u003eT\u003c/sub\u003e\u003csup\u003e*\u003c/sup\u003e ratios were derived. All ROIs were reviewed and verified using OsiriX MD [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] (Pixmeo SARL, Geneva, Switzerland) for anatomical co-registration of PET images with the corresponding low-dose CT.\u003c/p\u003e\n\u003ch3\u003eFlow cytometry analyses (dup: abstract ?)\u003c/h3\u003e\n\u003cp\u003ePeripheral immune profiling was performed using multiparameter flow cytometry on EDTA-anticoagulated whole blood samples. A total of 500 \u0026micro;L peripheral blood was collected from each patient prior to PET-CT imaging. To remove plasma proteins and other interfering factors, samples were washed twice by adding 2 mL of phosphate-buffered saline (PBS) containing 1% bovine serum albumin (BSA), followed by centrifugation and removal of the supernatant. The resulting cell pellet was used for further staining. For staining, 100 \u0026micro;L of the washed cell suspension was incubated for 1 hour at room temperature with a monoclonal antibody panel targeting: CD3 (PerCP), CD4 (eFluor 450), CD8 (Alexa Fluor 700), CD127 (Alexa Fluor 647), CD25 (PE), CD45RO (FITC), and CCR7 (PE-Cy7). An additional 100 \u0026micro;L aliquot of each sample was used for fluorescence-minus-one control, specifically for CD25-PE gating. Red blood cells were lysed using FACS Lysing Solution (BD Biosciences) for 10 minutes. Cells were then washed, resuspended in PBS with 1% BSA and acquired on a BD LSR II flow cytometer. Flow cytometry data were analysed using Kaluza software (version 2.1, Beckman Coulter). CD3⁺ T cells were identified and further subdivided into CD4⁺ T-helper cells and CD8⁺ cytotoxic T cells. Within the CD4⁺ compartment, CD127- CD25hi Tregs were identified and subsequently categorised into na\u0026iuml;ve (CD45RO⁻) and memory (CD45RO⁺) subsets. Within the conventional CD4\u0026thinsp;+\u0026thinsp;T cells (non-Tregs), four functional subsets were delineated based on CCR7 and CD45RO expression: na\u0026iuml;ve (CD45RO⁻CCR7⁺), central memory (CD45RO⁺CCR7⁺), effector memory (CD45RO⁺CCR7⁻), and terminally differentiated (CD45RO⁻CCR7⁻) T cells. A similar classification was applied to CD8⁺ T cells, including the terminally differentiated effector memory RA (TEMRA) population. In all T cell subsets, CD25 expression was quantified to assess activation status.\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eBaseline characteristics were summarised using descriptive statistics. Continuous variables were reported as medians with IQRs, given the small sample size and associated limitations in reliably assessing normality. As such, only non-parametric tests were used for statistical comparisons. Group differences (e.g., rejection vs. no rejection) were assessed using the Wilcoxon Rank-Sum test. Statistical significance was defined as p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, with p-values between 0.05\u0026ndash;0.1 reported as potential trends due to the small sample size. To facilitate interpretation, boxplots were generated for each dynamic [\u003csup\u003e18\u003c/sup\u003eF]FB-IL2 PET-CT derived variable, illustrating medians, IQR, data spread, and individual observations. Corresponding Wilcoxon Rank-Sum p-values were displayed above the figure legend. To evaluate whether [\u0026sup1;⁸F]FB-IL2 PET-CT shows promise as a tool for detecting kidney allograft rejection, a comprehensive correlation analysis was conducted. A correlation matrix was constructed to examine associations between PET-derived imaging markers, histopathological features, kidney function parameters, and circulating immune cell subsets. Initially, given the diagnostic focus of this study, a single correlation analysis was planned across the full cohort (n\u0026thinsp;=\u0026thinsp;11). However, due to biopsy sampling error, Banff histopathological features could not be reliably assessed in two patients. As a result, two separate correlation analyses were performed. The first included all 11 patients and explored associations between PET imaging markers and treated rejection, kidney function parameters (eGFR, serum creatinine, and proteinuria) and circulating immune cell subsets (CD3⁺, CD4⁺, CD8⁺, and Tregs). The second analysis, limited to 9 patients with interpretable biopsies, focused on selected Banff histopathological features of rejection, including interstitial inflammation, tubulitis, and interstitial fibrosis/tubular atrophy (IFTA). Variable selection was based on relevance and clinical applicability. PET parameters included SUV\u003csub\u003emean\u003c/sub\u003e and V\u003csub\u003eT\u003c/sub\u003e\u003csup\u003e*\u003c/sup\u003e (both cortical averages and cortex-to-medulla ratios were used). Spearman\u0026rsquo;s rank correlation was used due to the small sample size and non-normal distribution of most variables. Correlation coefficients (r-values) were interpreted as weak (|r| = 0.1\u0026ndash;0.3), moderate (|r| = 0.3\u0026ndash;0.5), or strong (|r| \u0026gt;0.5). Statistical significance was defined as p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. P-values between 0.05 and 0.1 were reported as potential trends. To facilitate interpretation, results were visualised using heatmaps. All statistical analyses were conducted in R software (v4.1.0, R Foundation for Statistical Computing, Vienna, Austria), utilizing the Hmisc, ggplot2, reshape2, dplyr, pROC, caret, and openxlsx packages.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003ePatient Characteristics at study entry\u003c/h2\u003e\u003cp\u003eA total of 15 kidney transplant recipients were initially enrolled. Four patients were excluded: One due to severely reduced vascular perfusion, as evidenced by repeated renograms showing minimal to absent blood flow to the transplant, which precluded accurate [\u003csup\u003e18\u003c/sup\u003eF]FB-IL2 PET-CT imaging. Three patients were excluded after first enrolment due to having undergone biopsy for suspected rejection within 15 days of basiliximab induction therapy, which did not meet the predefined inclusion criteria. The final cohort included 11 patients (5 males, 45%; 6 females, 55%) with a median age of 53 years (IQR: 48\u0026ndash;64). A flowchart of patient enrolment and exclusion is provided in \u003cb\u003eSupplementary Fig.\u0026nbsp;1\u003c/b\u003e. Primary causes of end-stage renal disease included glomerulonephritis, diabetic nephropathy, and polycystic kidney disease. All patients underwent [\u0026sup1;⁸F]FB-IL2 PET-CT due to clinical suspicion of rejection, most based on rising serum creatinine, reduced eGFR, or newly detected proteinuria. [\u0026sup1;⁸F]FB-IL2 was well tolerated in all participants and no general or immune-related adverse events were observed. Median serum creatinine at the time of the PET scan was 266 \u0026micro;mol/L (IQR: 205\u0026ndash;341.5), with a median eGFR of 18 mL/min/1.73m\u0026sup2; (IQR: 14.5\u0026ndash;29) and a median proteinuria of 0.38 g/day (IQR: 0.20\u0026ndash;0.46). Kidney transplant recipients received transplants from either deceased (n\u0026thinsp;=\u0026thinsp;6) or living (n\u0026thinsp;=\u0026thinsp;5) donors. HLA mismatch ranged from 0\u0026ndash;2 alleles per locus, with two patients showing pre-transplant donor-specific antibodies against HLA class II and/or class I. Most patients (n\u0026thinsp;=\u0026thinsp;10) received triple immunosuppressive therapy with tacrolimus, mycophenolate mofetil, and prednisolone. One patient was treated with tacrolimus, everolimus, and prednisolone. Basiliximab induction was administered to all patients, consisting of 20 mg on the day of transplantation and 20 mg four days post-transplantation. PET-CT imaging was performed\u0026thinsp;\u0026ge;\u0026thinsp;15 days after the last dose (range: 80\u0026ndash;1017 days), in accordance with the study\u0026rsquo;s exclusion criteria. Further clinical, immunological, and transplant-related details are provided 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\u003ePatient and Transplant Characteristics at Time of Suspected Rejection.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"13\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" 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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePatient number\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eAge\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eSex\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003e(Male/\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eFemale)\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eBMI (Kg/m\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e)\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003ePrimary reason\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003efor ESRD \u0026amp; transplantation\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eProteinuria (g/day)\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eSerum Creatinine (\u0026micro;mol/l)\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003eeGFR\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003e(ml/min/\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003e1.73m\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e)\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cem\u003eDonor type\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003e(Living/\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eDeceased\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cem\u003eHLA mismatch (A-B-DR)\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cem\u003eHLA DSA\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eClass I \u0026amp;\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eclass II (Neg/Pos)\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u003cem\u003eMaintenance therapy (TMP/TEP)\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u003cem\u003eTime between Basiliximab induction and IL-2 PET-scan (Days)\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003e1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e37.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIgA nephropathy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e222\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eDeceased\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c10\"\u003e\u003cp\u003e1-2-1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003eI (Neg), II (Neg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003eTMP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e99\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003e2\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e32.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSystemic vasculitis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e605\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eDeceased\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c10\"\u003e\u003cp\u003e1-1-0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003eI (Neg), II (Neg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003eTMP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e385\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003e3\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e21.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eglomerulonephritis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e338\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eLiving\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c10\"\u003e\u003cp\u003e0-0-0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003eI (Neg), II (Neg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003eTMP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e1008\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003e4\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e32.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDiabetic nephropathy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e156\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eLiving\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c10\"\u003e\u003cp\u003e1-0-0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003eI (Neg), II (Neg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003eTMP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e206\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003e5\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e20.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eObstructive uropathy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e408\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eLiving\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c10\"\u003e\u003cp\u003e1-1-1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003eI (Neg), II (Neg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003eTMP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e528\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003e6\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e26.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePyelo-/interstitial nephritis due to reflux\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e234\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eDeceased\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c10\"\u003e\u003cp\u003e1-2-0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003eI (Neg), II (Pos)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003eTMP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003e7\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e28.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGlomerulonephritis, focal segmental glomerulosclerosis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e188\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eDeceased\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c10\"\u003e\u003cp\u003e0-2-1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003eI (Neg), II (Pos)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003eTMP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003e8\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e26.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAutosomal dominant polycystic kidney disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e302\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eLiving\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c10\"\u003e\u003cp\u003e1-2-2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003eI (Neg), II (Neg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003eTMP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e117\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003e9\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e23.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDiabetic nephropathy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e266\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eDeceased\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c10\"\u003e\u003cp\u003e2-2-1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003eI (Pos), II (Pos)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003eTEP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e1017\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003e10\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e16.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOther identified renal disorders (cystic fibrosis)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eLiving\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c10\"\u003e\u003cp\u003e0-2-1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003eI (Neg), II (Neg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003eTMP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e418\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003e11\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e26.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHypertension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e345\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eDeceased\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c10\"\u003e\u003cp\u003e1-2-2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003eI (Neg), II (Neg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003eTMP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e723\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"13\"\u003e\u003cb\u003eBMI\u003c/b\u003e; Body Mass Index, \u003cb\u003eESRD\u003c/b\u003e; End Stage Renal Disease, \u003cb\u003eeGFR\u003c/b\u003e; estimated Glomerular Filtration Rate, \u003cb\u003eHLA\u003c/b\u003e; Human Leukocyte Antigen, \u003cb\u003eDSA\u003c/b\u003e; Donor Specific Antibodies, \u003cb\u003eTMP\u003c/b\u003e; Tacrolimus, Mycophenolate mofetil, and Prednisolone, \u003cb\u003eTEP\u003c/b\u003e; Tacrolimus, Everolimus, and prednisolone, \u003cb\u003eIL-2\u003c/b\u003e; Interleukin 2, \u003cb\u003ePET\u003c/b\u003e; Positions Emission Tomography, \u003cb\u003eIgA\u003c/b\u003e; Immunoglobulin A, \u003cb\u003en/a\u003c/b\u003e; not applicable.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e summarises the Banff classification findings and the time between transplantation and PET-CT. The median time from transplantation to PET-CT was 390 days (IQR: 112.5\u0026ndash;630). Three patients (27%) had biopsy-proven acute TCMR and one patient showed borderline changes suggestive of rejection. In two cases, insufficient biopsy tissue prevented a definitive rejection diagnosis, but clinical evaluation indicated no rejection. No cases of vascular rejection were observed. Histological scoring was performed for all biopsies with sufficient tissue, with scores ranging from 0 (absence) to 3 (severe involvement). Global glomerulosclerosis was present in 5 of 9 evaluated patients (56%) (scores 0\u0026ndash;2). Interstitial inflammation was detected in 4 of 9 patients (44%), with moderate to severe involvement (scores 2\u0026ndash;3) in 3 patients (33%). Tubulitis was observed in 5 of 9 patients (56%), including 2 patients (22%) with severe involvement. IFTA was present in 5 of 7 biopsies (71%), with moderate to severe changes in 3 patients (43%). Periodic acid\u0026ndash;Schiff positive arteriolar hyaline thickening was found in 3 of 9 patients (33%) (scores 1\u0026ndash;3), while intimal arterial proliferation was present in 5 of 8 patients (63%), with mild to moderate involvement. Peritubular capillaritis was absent in all biopsies. Additionally, all biopsies were negative for C4d staining and Simian virus 40 immunohistochemistry, indicating no antibody-mediated rejection or BK nephropathy.\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\u003eBANFF classification.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"14\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\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=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePatient nr.\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eRejection (yes/no/\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eunclear)\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eRejection type\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eGlomeruli (nr.)\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eGlobal glomerulosclerosis (0\u0026ndash;3)\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eInterstitial inflammation (0\u0026ndash;3)\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eTubulitis (0\u0026ndash;3)\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003eIFTA (0\u0026ndash;3)\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cem\u003ePAS\u0026thinsp;+\u0026thinsp;hyaline (0\u0026ndash;3)\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cem\u003eIntima arterial proliferation (0\u0026ndash;3)\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cem\u003ePeritubular Capillaritis\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003e(0\u0026ndash;3)\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u003cem\u003eC4d (N/P)\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u003cem\u003eSV40 (N/P)\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c14\"\u003e\u003cp\u003e\u003cem\u003eTime between kidney transplantation and IL-2 PET-scan (days)\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003e1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003en/a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e104\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003e2\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAcute rejection grade I-B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e390\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003e3\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003en/a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e1014\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003e4\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo \u003csup\u003e\u003cb\u003eA\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003en/a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e211\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003e5\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAcute rejection grade I-B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e533\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003e6\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003en/a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e84\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003e7\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo \u003csup\u003e\u003cb\u003eA\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003en/a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e104\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003e8\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBorderline acute rejection\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e121\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003e9\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAcute rejection grade 1-A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e1021\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003e10\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003en/a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e422\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003e11\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003en/a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e727\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"14\"\u003e\u003cb\u003eNr\u003c/b\u003e; number, \u003cb\u003eIFTA\u003c/b\u003e; Interstitial fibrosis and tubular atrophy, \u003cb\u003ePAS\u0026thinsp;+\u0026thinsp;hyaline\u003c/b\u003e; periodic acid\u0026ndash;Schiff positive arteriolar hyaline thickening, \u003cb\u003eSV40\u003c/b\u003e; Simian virus 40, \u003cb\u003eN/P\u003c/b\u003e; negative/positive, \u003cb\u003eIL-2\u003c/b\u003e; Interleukin-2, \u003cb\u003ePET\u003c/b\u003e; Positions Emission Tomography, \u003cb\u003en/a\u003c/b\u003e; not applicable.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003ePET analysis\u003c/h3\u003e\n\u003cp\u003e[\u0026sup1;⁸F]FB-IL2 PET-CT analysis demonstrated variability in tracer uptake among kidney transplant recipients (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Cortex SUV\u003csub\u003emean\u003c/sub\u003e ranged from 5.31 to 13.23 g/ml (SUVbw), with a median of 8.24 (IQR: 6.60\u0026ndash;10.78). The median SUV\u003csub\u003emean\u003c/sub\u003e trended higher in rejection cases (10.39, IQR: 8.56\u0026ndash;12.22) compared to non-rejection cases (6.67, IQR: 6.43\u0026ndash;8.90, p\u0026thinsp;=\u0026thinsp;0.23, r\u0026thinsp;=\u0026thinsp;0.40). The SUV\u003csub\u003emean\u003c/sub\u003e cortex-to-medulla ratio ranged from 1.06 to 1.99, with an overall median of 1.44 (IQR: 1.27\u0026ndash;1.63). The median was similar between rejection cases (1.38, IQR: 1.28\u0026ndash;1.49) and non-rejection cases (1.44, IQR: 1.27\u0026ndash;1.83, p\u0026thinsp;=\u0026thinsp;0.79, r\u0026thinsp;=\u0026thinsp;0.11). Logan analysis showed that cortical V\u003csub\u003eT\u003c/sub\u003e\u003csup\u003e*\u003c/sup\u003e values ranged from 0.05 to 1.48 ml/cm\u003csup\u003e3\u003c/sup\u003e, with a median of 0.53 (IQR: 0.42\u0026ndash;0.84). The distribution was higher in rejection cases, with a median of 0.80 (IQR: 0.65\u0026ndash;1.04), compared to non-rejection cases, which had a median of 0.50 (IQR: 0.19\u0026ndash;0.65, p\u0026thinsp;=\u0026thinsp;0.23, r\u0026thinsp;=\u0026thinsp;0.40). The cortex-to-medulla V\u003csub\u003eT\u003c/sub\u003e\u003csup\u003e*\u003c/sup\u003e ratio ranged from 0.28 to 1.38, with a median of 0.82 (IQR: 0.69\u0026ndash;1.25). When comparing rejection and non-rejection groups, a statistically significant difference was found (p\u0026thinsp;=\u0026thinsp;0.02, r\u0026thinsp;=\u0026thinsp;0.68). The median ratio was higher in rejection cases (1.25, IQR: 1.15\u0026ndash;1.34) compared to non-rejection cases (0.69, IQR: 0.67\u0026ndash;0.79). Boxplots (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA\u0026ndash;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD) illustrate the median, IQR, and individual data points for both rejection and non-rejection groups. An illustrative example of rejection vs non-rejection behaviour is reported in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD.\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\u003e\u003cb\u003eSUV\u003c/b\u003e\u003csub\u003e\u003cb\u003emean\u003c/b\u003e\u003c/sub\u003e \u003cb\u003eand Logan\u003c/b\u003e V\u003csub\u003eT\u003c/sub\u003e\u003csup\u003e*\u003c/sup\u003e \u003cb\u003eperfusion analysis.\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eSUV\u003csub\u003emean\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eLogan V\u003csub\u003eT\u003c/sub\u003e\u003csup\u003e*\u003c/sup\u003e perfusion\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePatient number\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eCortex averaged\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003e\u0026plusmn; StDv (g/ml[SUVbw])\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eRatio cortex/medulla\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eCortex\u0026nbsp;averaged\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003e\u0026plusmn; Stdv\u0026nbsp;(ml/cm\u003c/em\u003e\u003csup\u003e\u003cem\u003e3\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e)\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eRatio cortex/medulla\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.25\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\u003e7.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.11\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\u003e5.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.66\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\u003e8.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.69\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=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.69\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.38\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=\"left\" colname=\"c2\"\u003e\u003cp\u003e13.23\u0026thinsp;\u0026plusmn;\u0026thinsp;1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.82\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\u003e6.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.28\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\u003e12.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.16\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\u003e12.00\u0026thinsp;\u0026plusmn;\u0026thinsp;1.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.33\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\u003e6.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.68\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\u003e6.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.76\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eSUV\u003c/b\u003e\u003csub\u003e\u003cb\u003emean\u003c/b\u003e\u003c/sub\u003e; Standardised uptake value mean, V\u003csub\u003eT\u003c/sub\u003e\u003csup\u003e*\u003c/sup\u003e; Apparent volume of distribution of tracer, \u003cb\u003eStDv\u003c/b\u003e; Standard deviation.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003eFlow cytometry analyses\u003c/h3\u003e\n\u003cp\u003eFlow cytometry analysis did not reveal any statistically significant differences in immune cell subset distributions between rejection and non-rejection groups (\u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e). This included total CD3\u003csup\u003e+\u003c/sup\u003e lymphocytes (U\u0026thinsp;=\u0026thinsp;10, p\u0026thinsp;=\u0026thinsp;0.53), CD4\u003csup\u003e+\u003c/sup\u003e helper T cells (U\u0026thinsp;=\u0026thinsp;22, p\u0026thinsp;=\u0026thinsp;0.16), CD8\u003csup\u003e+\u003c/sup\u003e cytotoxic T cells (U\u0026thinsp;=\u0026thinsp;7, p\u0026thinsp;=\u0026thinsp;0.23), and Tregs (U\u0026thinsp;=\u0026thinsp;17, p\u0026thinsp;=\u0026thinsp;0.65).\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eCorrelation analyses\u003c/h2\u003e\u003cp\u003eTo explore the relationship between [\u003csup\u003e18\u003c/sup\u003eF]FB-IL2 PET-CT derived imaging parameters and immune-mediated kidney allograft rejection, two correlation analyses were performed. The first included all patients (n\u0026thinsp;=\u0026thinsp;11) and examined associations between [\u003csup\u003e18\u003c/sup\u003eF]FB-IL2 PET-CT parameters, clinical markers of graft function at time of suspected rejection, treated rejection, and peripheral immune cell subsets. The second focused on histopathological features of rejection based on the Banff classification (n\u0026thinsp;=\u0026thinsp;9), excluding two patients due to non-interpretable biopsies caused by sampling error. Correlation heatmaps are visualised in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA (PET vs. treated rejection, kidney function, and immune cell subsets) and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB (PET vs. Banff features). Corresponding significance heatmaps are shown in \u003cb\u003eSupplementary Figs.\u0026nbsp;2A and 2B\u003c/b\u003e.\u003c/p\u003e\u003cp\u003eAmong all PET-derived parameters, the cortex-to-medulla V\u003csub\u003eT\u003c/sub\u003e\u003csup\u003e*\u003c/sup\u003e ratio demonstrated the strongest and most consistent associations. This variable showed a strong positive correlation with treated rejection (r\u0026thinsp;=\u0026thinsp;0.72, p\u0026thinsp;=\u0026thinsp;0.01), as well as strong correlations with interstitial inflammation (r\u0026thinsp;=\u0026thinsp;0.68, p\u0026thinsp;=\u0026thinsp;0.04), tubulitis (r\u0026thinsp;=\u0026thinsp;0.61, p\u0026thinsp;=\u0026thinsp;0.08), and proteinuria (r\u0026thinsp;=\u0026thinsp;0.60, p\u0026thinsp;=\u0026thinsp;0.05). These findings suggest a clear relationship between elevated V\u003csub\u003eT\u003c/sub\u003e\u003csup\u003e*\u003c/sup\u003e ratios and histological indicators of acute rejection. Averaged cortex SUV\u003csub\u003emean\u003c/sub\u003e and V\u003csub\u003eT\u003c/sub\u003e\u003csup\u003e*\u003c/sup\u003e both exhibited moderate correlations with treated rejection (r\u0026thinsp;=\u0026thinsp;0.42, for both), but these did not reach statistical significance (p\u0026thinsp;=\u0026thinsp;0.20, for both). In contrast, the other [\u003csup\u003e18\u003c/sup\u003eF]FB-IL2 PET-CT parameter, SUV\u003csub\u003emean\u003c/sub\u003e ratio cortex-to-medulla, showed limited or inconsistent associations across all Banff histological features and rejection status. Averaged cortex SUV\u003csub\u003emean\u003c/sub\u003e showed a strong negative trend with IFTA (r = \u0026minus;\u0026thinsp;0.58, p\u0026thinsp;=\u0026thinsp;0.10), suggesting a potential inverse relationship between PET tracer uptake and chronic structural injury. Finally, in relation to peripheral immune markers, the most notable finding was a strong positive correlation between averaged cortex V\u003csub\u003eT\u003c/sub\u003e\u003csup\u003e*\u003c/sup\u003e and Treg levels (r\u0026thinsp;=\u0026thinsp;0.80, p\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;0.01), while averaged cortex SUV\u003csub\u003emean\u003c/sub\u003e (r\u0026thinsp;=\u0026thinsp;0.61, p\u0026thinsp;=\u0026thinsp;0.05) and cortex-to-medulla V\u003csub\u003eT\u003c/sub\u003e\u003csup\u003e*\u003c/sup\u003e ratio (r\u0026thinsp;=\u0026thinsp;0.50, p\u0026thinsp;=\u0026thinsp;0.12) also showed strong correlations with Treg. No other immune cell subsets demonstrated meaningful or consistent associations with PET-derived measures.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis proof-of-concept study evaluated, for the first time in human, the potential of [\u003csup\u003e18\u003c/sup\u003eF]FB-IL2 PET-CT as a non-invasive imaging modality for detecting acute TCMR in kidney transplant recipients. Several findings demonstrated the potential diagnostic relevance of this technique. The most robust and statistically significant marker was the cortex-to-medulla V\u003csub\u003eT\u003c/sub\u003e\u003csup\u003e*\u003c/sup\u003e ratio, determined by Logan graphical analysis, which effectively differentiated rejection from non-rejection cases (p\u0026thinsp;=\u0026thinsp;0.02, r\u0026thinsp;=\u0026thinsp;0.68). This ratio also demonstrated strong correlations with patients who were treated for rejection (r\u0026thinsp;=\u0026thinsp;0.72, p\u0026thinsp;=\u0026thinsp;0.01) and histological features of TCMR, including interstitial inflammation (r\u0026thinsp;=\u0026thinsp;0.68, p\u0026thinsp;=\u0026thinsp;0.04) and tubulitis (r\u0026thinsp;=\u0026thinsp;0.61, p\u0026thinsp;=\u0026thinsp;0.08), as well as with proteinuria (r\u0026thinsp;=\u0026thinsp;0.60, p\u0026thinsp;=\u0026thinsp;0.05), a clinical indicator of graft injury. The use of the medulla as an internal reference likely improves signal stability by reducing variability due to renal perfusion and tracer kinetics [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In addition to the cortex-to-medulla ratio, other PET-derived parameters demonstrated biologically meaningful patterns that further support the physiological relevance of [\u0026sup1;⁸F]FB-IL2 uptake. Both V\u003csub\u003eT\u003c/sub\u003e\u003csup\u003e*\u003c/sup\u003e and SUV\u003csub\u003emean\u003c/sub\u003e in the cortex showed moderate positive correlations with treated rejection (r\u0026thinsp;=\u0026thinsp;0.42 for both), suggesting that these measures capture relevant immune activity within the graft. A particularly notable finding was the strong positive correlation between the V\u003csub\u003eT\u003c/sub\u003e in the cortex and circulating Tregs (r\u0026thinsp;=\u0026thinsp;0.80, p\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Tregs, characterised by high expression of the IL-2 receptor α-chain (CD25), play a critical role in modulating immune responses and maintaining peripheral tolerance [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Additional correlations were observed between Treg levels and other PET-derived parameters, in particular cortical SUV\u003csub\u003emean\u003c/sub\u003e (r\u0026thinsp;=\u0026thinsp;0.61, p\u0026thinsp;=\u0026thinsp;0.05) and cortex-to-medulla V\u003csub\u003eT\u003c/sub\u003e\u003csup\u003e*\u003c/sup\u003e ratio (r\u0026thinsp;=\u0026thinsp;0.50, p\u0026thinsp;=\u0026thinsp;0.12). These findings raise the possibility that elevated [\u0026sup1;⁸F]FB-IL2 uptake may not solely reflect effector T cell infiltration, but could also indicate intragraft accumulation of Tregs, potentially as a counter-regulatory response during rejection. Whether this accumulation is protective or simply reflects immunologic activity remains unclear and warrants further investigation.\u003c/p\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eComparison with Existing Literature\u003c/h2\u003e\u003cp\u003eOur findings are consistent with previous work demonstrating the feasibility of IL-2 receptor-targeted imaging in solid organ transplantation. For instance, \u003csup\u003e99m\u003c/sup\u003eTc-HYNIC-IL-2 scintigraphy has shown high specificity for moderate to severe rejection in lung transplant recipients, but with limited sensitivity for early immune activation [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Compared to these SPECT-based techniques, [\u0026sup1;⁸F]FB-IL2 PET-CT offers higher spatial resolution, the ability to perform quantitative kinetic modelling, and a more favourable biodistribution profile. Beyond the transplant setting, Van de Donk et al. demonstrated the safety and feasibility of [\u0026sup1;⁸F]FB-IL2 PET imaging in patients with metastatic melanoma, confirming its utility for detecting activated T cell populations in vivo [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Our study builds upon this foundation by evaluating the tracer in the context of solid organ transplantation, where immune responses are directed against allograft tissue rather than tumour cells. To our knowledge, this is the first study to demonstrate correlations between [\u0026sup1;⁸F]FB-IL2 PET uptake and histological features of acute TCMR, underscoring its potential role in non-invasive transplant rejection assessment. Moreover, the relatively homogeneous structure of the kidney allograft, compared to more anatomically variable organs such as the lungs, may enhance the robustness of the cortex-to-medulla V\u003csub\u003eT\u003c/sub\u003e\u003csup\u003e*\u003c/sup\u003e ratio as a quantitative imaging biomarker. This structural distinction supports the rationale for using an internal reference approach in kidney imaging, where well-defined cortical and medullary compartments enable robust intrarenal comparisons. The observed associations between PET signals and systemic immune markers further underscore the potential of [\u0026sup1;⁸F]FB-IL2 PET-CT to bridge local and peripheral immune dynamics. This is in line with prior evidence suggesting that systemic T cell activation can precede histopathological manifestations of rejection [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], supporting the role of PET imaging in early immune surveillance.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eStrengths and Limitations\u003c/h2\u003e\u003cp\u003eA major strength of this study is its integrative, multimodal approach, combining data from [\u0026sup1;⁸F]FB-IL2 PET-CT imaging, Banff histopathology, kidney function markers, and peripheral immune profiling. Whereas percutaneous biopsy is subject to sampling error by evaluating only a small portion of the graft, which can lead to misdiagnosis or underdiagnosis of rejection[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], [\u0026sup1;⁸F]FB-IL2 PET-CT enables whole-organ immune activity assessment. This advantage was underscored by two cases in our cohort where biopsy samples were non-diagnostic due to insufficient tissue, highlighting a key limitation of biopsy-based diagnostics. Moreover, this study was conducted in a real-world clinical setting, offering initial evidence for the feasibility of integrating [\u003csup\u003e18\u003c/sup\u003eF]FB-IL2 PET-CT into standard transplant workflows. However, several limitations must be acknowledged. First, the small sample size (n\u0026thinsp;=\u0026thinsp;11) limits statistical power and generalizability. Second, logistical constraints, such as scanner availability, the requirement for pre-treatment imaging and the exclusion of patients who had recently received basiliximab, posed challenges to timely patient recruitment. Third, the cross-sectional nature of this study precludes assessment of the predictive value of [\u003csup\u003e18\u003c/sup\u003eF]FB-IL2 PET-CT parameters towards long-term graft outcomes. Fourth, interpretation of tracer binding is inherently complex, as IL-2 receptor expression is not exclusive to activated effector T cells but is also present on Tregs and natural killer cells. Future studies should aim to validate these findings in larger, longitudinal cohorts and establish standardised protocols for PET acquisition and analysis to support clinical implementation. Finally, a methodological limitation of this study is the use of an image-derived whole blood input function without metabolite or blood-to-plasma correction, due to the absence of arterial or venous blood sampling. While this deviates from the formal assumptions of Logan graphical analysis, prior preclinical and clinical studies have demonstrated minimal in vivo metabolism of [\u0026sup1;⁸F]FB-IL2 [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], supporting the plausibility of this approximation in a first-in-human, non-invasive transplant setting. Nonetheless, V\u003csub\u003eT\u003c/sub\u003e\u003csup\u003e*\u003c/sup\u003e estimates should be interpreted with caution given the lack of a validated plasma input function.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eClinical Implications and Future Directions\u003c/h2\u003e\u003cp\u003eDespite its exploratory nature, this study provides compelling evidence that [\u0026sup1;⁸F]FB-IL2 PET-CT is a safe, biologically informative, non-invasive and potentially clinically valuable tool for assessing immune activity in kidney allografts. Among the evaluated parameters, the cortex-to-medulla V\u003csub\u003eT\u003c/sub\u003e\u003csup\u003e*\u003c/sup\u003e ratio emerged as the most reproducible and diagnostically informative imaging metric, correlating with treated rejection, interstitial inflammation, tubulitis, proteinuria, and circulating Treg levels. Furthermore, the cortex-to-medulla V\u003csub\u003eT\u003c/sub\u003e\u003csup\u003e*\u003c/sup\u003e ratio showed statistically significant discrimination between rejection and non-rejection cases. Its robustness may derive from the use of the medulla as an internal reference [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. [\u0026sup1;⁸F]FB-IL2 was well tolerated in all participants and no adverse events related to immune activation were observed, consistent with prior human studies with different IL-2 tracers [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. This favourable safety profile supports its potential for clinical use in transplant surveillance. Future research should aim to validate the above findings in larger, prospective studies with longitudinal follow-up to assess predictive value. Standardization of imaging protocols, particularly for Logan analysis-derived V\u003csub\u003eT\u003c/sub\u003e\u003csup\u003e*\u003c/sup\u003e measures, and comparative evaluation against emerging biomarkers such as donor-derived cell-free DNA and urinary chemokines will be essential to define the place of [\u0026sup1;⁸F]FB-IL2 PET-CT in clinical practice. By enabling earlier and more comprehensive rejection assessment, PET imaging could transform transplant surveillance, reduce reliance on invasive biopsies, and ultimately improve long-term graft outcomes.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study provides first-in-human evidence that [\u0026sup1;⁸F]FB-IL2 PET-CT may serve as a non-invasive imaging modality for detecting acute TCMR in kidney transplant recipients. By integrating molecular imaging with histopathology, graft function, and peripheral immune profiling, [\u0026sup1;⁸F]FB-IL2 PET-CT offers a comprehensive, whole-organ perspective on intragraft immune activity, overcoming key limitations of biopsy-based assessment. The cortex-to-medulla V\u003csub\u003eT\u003c/sub\u003e\u003csup\u003e*\u003c/sup\u003e ratio, determined by Logan graphical analysis, emerged as a promising clinically informative imaging biomarker, associated not only with rejection but also with functional and immunological indicators of intragraft inflammation.\u003c/p\u003e\u003cp\u003eThese findings suggest that [\u0026sup1;⁸F]FB-IL2 PET-CT could complement or, in selected scenarios, reduce the need for biopsy, particularly when conventional diagnostics are inconclusive or carry procedural risk. Future multicentre studies with longitudinal follow-up are essential to validate these findings, standardise PET-based biomarkers, and define their role within personalised transplant monitoring strategies.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e[\u003csup\u003e18\u003c/sup\u003eF]FB-IL2 Fluorine-18-labeled fluoro-benzoyl interleukin-2\u003c/p\u003e\n\u003cp\u003eCT Computed tomography\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEDTA Ethylenediaminetetraacetic acid\u003c/p\u003e\n\u003cp\u003eeGFR Estimated glomerular filtration rate\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHLA Human leukocyte antigen\u003c/p\u003e\n\u003cp\u003eIFTA Interstitial fibrosis and tubular atrophy\u003c/p\u003e\n\u003cp\u003eIL-2 Interleukin-2\u003c/p\u003e\n\u003cp\u003eIQRs Interquartile ranges\u003c/p\u003e\n\u003cp\u003eKi Net influx rate constant\u003c/p\u003e\n\u003cp\u003ePET Positron emission tomography\u003c/p\u003e\n\u003cp\u003eROIs Regions of interest\u003c/p\u003e\n\u003cp\u003eSUV\u003csub\u003emean\u003c/sub\u003e Mean standardised uptake value\u003c/p\u003e\n\u003cp\u003eTCMR T-cell-mediated rejection\u003c/p\u003e\n\u003cp\u003eTEMRA Terminally differentiated effector memory T cells\u003c/p\u003e\n\u003cp\u003eTregs Regulatory T cells\u003c/p\u003e\n\u003cp\u003eUMCG University Medical Centre Groningen\u003c/p\u003e\n\u003cp\u003eV\u003csub\u003eT\u003c/sub\u003e\u003csup\u003e*\u003c/sup\u003e Apparent volume of distribution\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cu\u003eAuthorship contribution\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eNB\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Conceptualization, methodology, formal analysis, investigation, resources, data curation, writing – original draft, visualization and project administration.\u003c/p\u003e\n\u003cp\u003eAA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Formal analysis, data curation, writing – review \u0026amp; editing and visualization.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eJS\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Conceptualization, methodology, writing – review \u0026amp; editing and supervision.\u003c/p\u003e\n\u003cp\u003eSB\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Conceptualization, methodology, investigation, resources, data curation, review \u0026amp; editing, supervision, project administration and funding acquisition.\u003c/p\u003e\n\u003cp\u003eEV\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Conceptualization, methodology, resources and writing – review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003eTB\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Methodology, formal analysis and writing – review \u0026amp; editing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAD\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Formal analysis and writing – review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003eAG\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Conceptualization, methodology, writing – review \u0026amp; editing and supervision.\u003c/p\u003e\n\u003cp\u003eRS\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Conceptualization, methodology, resources, writing – review \u0026amp; editing, supervision and project administration.\u003c/p\u003e\n\u003cp\u003eRP\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Conceptualization, methodology, resources, writing – review \u0026amp; editing, supervision, project administration and funding acquisition.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eDisclosure\u003c/u\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eE.F.J. de Vries declares to have been involved in contracted research for Hoffmann-La Roche, Eli Lilly, Bristol Myers Squibb, Novartis, Janssen-Cilag BV, Mesentech, GE Healthcare and GlaxoSmithKline, not related to this study and paid to the institution in the past 5 years. The other authors of this manuscript have no conflicts of interest to declare.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eFunding\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by an unrestricted research grant from Chiesi Pharmaceuticals and the University Medical Centre Groningen Innovation Prize.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eData availability\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analysed during the current study are available from the corresponding author on reasonable request.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKrishnan N, Higgins R, Short A, Zehnder D, Pitcher D, Hudson A et al (2015) Kidney Transplantation Significantly Improves Patient and Graft Survival Irrespective of BMI: A Cohort Study. Am J Transplant. ;15\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlasfar S, Kodali L, Schinstock CA (2023) Current Therapies in Kidney Transplant Rejection. J Clin Med. ;12\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCooper JE (2020) Evaluation and Treatment of Acute Rejection in Kidney Allografts. Clin J Am Soc Nephrol 15:430\u0026ndash;438\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAl-Awwa I, Hariharan S, First M (1998) Importance of allograft biopsy in renal transplant recipients: Correlation between clinical and histological diagnosis. Am J Kidney Dis 31:S15\u0026ndash;S18\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMorgan TA, Chandran S, Burger IM, Zhang CA, Goldstein RB (2016) Complications of Ultrasound-Guided Renal Transplant Biopsies. Am J Transplant 16:1298\u0026ndash;1305\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRedfield RR, McCune KR, Rao A, Sadowski E, Hanson M, Kolterman AJ et al (2016) Nature, timing, and severity of complications from ultrasound-guided percutaneous renal transplant biopsy. Transpl Int 29:167\u0026ndash;172\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNankivell BJ, Chapman JR (2006) The Significance of Subclinical Rejection and the Value of Protocol Biopsies. Am J Transplant 6:2006\u0026ndash;2012\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWilczek HE (1990) Percutaneous needle biopsy of the renal allograft. A clinical safety evaluation of 1129 biopsies. Transplantation 50:790\u0026ndash;797\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSong Y, Wang Y, Wang W, Xie Y, Zhang J, Liu J et al (2025) Advancements in noninvasive techniques for transplant rejection: from biomarker detection to molecular imaging. J Transl Med 23:147\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLeonard WJ, Kr\u0026ouml;nke M, Peffer NJ, Depper JM, Greene WC (1985) Interleukin 2 receptor gene expression in normal human T lymphocytes. Proc Natl Acad Sci U S A 82:6281\u0026ndash;6285\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003evan de Donk PP, Wind TT, Hooiveld-Noeken JS, van der Veen EL, Glaudemans AWJM, Diepstra A et al (2021) Interleukin-2 PET imaging in patients with metastatic melanoma before and during immune checkpoint inhibitor therapy. Eur J Nucl Med Mol Imaging 48:4369\u0026ndash;4376\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSignore A, Picarelli A, Annovazzi A, Britton KE, Grossman AB, Bonanno E et al (2003) 123I-Interleukin-2: biochemical characterization and in vivo use for imaging autoimmune diseases. Nucl Med Commun 24:305\u0026ndash;316\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLoose D, Signore A, Staelens L, Bulcke K, Vanden, Vermeersch H, Dierckx RA et al (2008) 123I-Interleukin-2 uptake in squamous cell carcinoma of the head and neck carcinoma. Eur J Nucl Med Mol Imaging 35:281\u0026ndash;286\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDi Gialleonardo V, Signore A, Willemsen ATM, Sijbesma JWA, Dierckx RAJO, de Vries EFJ (2012) Pharmacokinetic modelling of N-(4-[(18)F]fluorobenzoyl)interleukin-2 binding to activated lymphocytes in an xenograft model of inflammation. Eur J Nucl Med Mol Imaging 39:1551\u0026ndash;1560\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKovarik JM, Kahan BD, Rajagopalan PR, Bennett W, Mulloy LL, Gerbeau C et al (1999) Population pharmacokinetics and exposure-response relationships for basiliximab in kidney transplantation. The U.S. Simulect Renal Transplant Study Group. Transplantation 68:1288\u0026ndash;1294\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eInternational Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) (2016) ICH E6(R2) Good Clinical Practice: Integrated Addendum to ICH E6(R1)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSalis P, Caccamo C, Verzaro R, Gruttadauria S, Artero M (2008) The role of basiliximab in the evolving renal transplantation immunosuppression protocol. Biologics 2:175\u0026ndash;188\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRoufosse C, Simmonds N, Clahsen-van Groningen M, Haas M, Henriksen KJ, Horsfield C et al (2018) A 2018 Reference Guide to the Banff Classification of Renal Allograft Pathology. Transplantation 102:1795\u0026ndash;1814\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHartimath SV, Manuelli V, Zijlma R, Signore A, Nayak TK, Freimoser-Grundschober A et al (2018) Pharmacokinetic properties of radiolabeled mutant Interleukin-2v: a PET imaging study. Oncotarget 9:7162\u0026ndash;7174\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBesson FL, Faure S (2024) PET KinetiX-A Software Solution for PET Parametric Imaging at the Whole Field of View Level. J imaging Inf Med 37:842\u0026ndash;850\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLogan J, Fowler JS, Volkow ND, Wolf AP, Dewey SL, Schlyer DJ et al (1990) Graphical analysis of reversible radioligand binding from time-activity measurements applied to [N-11C-methyl]-(-)-cocaine PET studies in human subjects. J Cereb Blood Flow Metab 10:740\u0026ndash;747\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eP\u0026auml;iv\u0026auml;rinta J, Anastasiou IA, Koivuviita N, Sharma K, Nuutila P, Ferrannini E et al (2023) Renal Perfusion, Oxygenation and Metabolism: The Role of Imaging. J Clin Med 12:5141\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKopitk\u0026oacute; C, Medve L, Gondos T, Soliman KMM, F\u0026uuml;l\u0026ouml;p T (2022) Mediators of Regional Kidney Perfusion during Surgical Pneumo-Peritoneum Creation and the Risk of Acute Kidney Injury\u0026mdash;A Review of Basic Physiology. J Clin Med 11:2728\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChinen T, Kannan AK, Levine AG, Fan X, Klein U, Zheng Y et al (2016) An essential role for the IL-2 receptor in Treg cell function. Nat Immunol 17:1322\u0026ndash;1333\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBoyman O, Sprent J (2012) The role of interleukin-2 during homeostasis and activation of the immune system. Nat Rev Immunol 12:180\u0026ndash;190\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTelenga ED, van der Bij W, de Vries EFJ, Verschuuren EAM, Timens W, Luurtsema G et al (2019) 99mTc-HYNIC-IL-2 scintigraphy to detect acute rejection in lung transplantation patients: a proof-of-concept study. EJNMMI Res 9:41\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBrunet M, Mill\u0026aacute;n L\u0026oacute;pez O, L\u0026oacute;pez-Hoyos M (2016) T-Cell Cytokines as Predictive Markers of the Risk of Allograft Rejection. Ther Drug Monit 38:S21\u0026ndash;S28\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSabek O, Dorak MT, Kotb M, Gaber AO, Gaber L (2002) Quantitative detection of T-cell activation markers by real-time PCR in renal transplant rejection and correlation with histopathologic evaluation. Transplantation 74:701\u0026ndash;707\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSorof JM, Vartanian RK, Olson JL, Tomlanovich SJ, Vincenti FG, Amend WJ (1995) Histopathological concordance of paired renal allograft biopsy cores. Effect on the diagnosis and management of acute rejection. Transplantation 60:1215\u0026ndash;1219\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNissen CJ, Moreno V, Davis VG, Walker PD (2022) Increasing Incidence of Inadequate Kidney Biopsy Samples Over Time: A 16-Year Retrospective Analysis From a Large National Renal Biopsy Laboratory. Kidney Int Rep 7:251\u0026ndash;258\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLoose D, Signore A, Staelens L, Bulcke K, Vanden, Vermeersch H, Dierckx RA et al (2008) (123)I-Interleukin-2 uptake in squamous cell carcinoma of the head and neck carcinoma. Eur J Nucl Med Mol Imaging 35:281\u0026ndash;286\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSignore A, Picarelli A, Annovazzi A, Britton KE, Grossman AB, Bonanno E et al (2003) 123I-Interleukin-2: biochemical characterization and in vivo use for imaging autoimmune diseases. Nucl Med Commun 24:305\u0026ndash;316\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":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8220933/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8220933/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDespite its life-saving role, kidney transplantation is frequently complicated by acute T-cell\u0026ndash;mediated rejection (TCMR), currently only diagnosed invasively by biopsy. [\u0026sup1;⁸F]FB-IL2 PET-CT is a novel molecular imaging approach that binds activated T cells via the IL-2 receptor. In this proof-of-concept study, 11 kidney transplant recipients with suspected rejection underwent [\u0026sup1;⁸F]FB-IL2 PET-CT. Tracer uptake in cortex versus medulla was quantified and compared with biopsy results, kidney function and peripheral blood T cells. Four patients were diagnosed with biopsy-confirmed TCMR. The cortex-to-medulla distribution volume ratio distinguished TCMR from non-rejection (p\u0026thinsp;=\u0026thinsp;0.02) and correlated with interstitial inflammation (r\u0026thinsp;=\u0026thinsp;0.68, p\u0026thinsp;=\u0026thinsp;0.04) and proteinuria (r\u0026thinsp;=\u0026thinsp;0.60, p\u0026thinsp;=\u0026thinsp;0.05). No tracer administration adverse events were observed. [\u0026sup1;⁸F]FB-IL2 PET-CT appears safe and may provide a non-invasive alternative to biopsy for detection of acute TCMR in kidney transplant recipients. Larger, prospective studies are warranted to confirm its clinical utility.\u003c/p\u003e","manuscriptTitle":"Non-Invasive Detection of Acute Kidney Allograft Rejection Using [18F]FB-IL2 PET-CT: A First in Human Proof-of-Concept Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-05 08:24:30","doi":"10.21203/rs.3.rs-8220933/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"communications-medicine","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"commsmed","sideBox":"Learn more about [Communications Medicine](http://www.nature.com/commsmed)","snPcode":"43856","submissionUrl":"https://mts-commsmed.nature.com/cgi-bin/main.plex","title":"Communications Medicine","twitterHandle":"@commsmedicine","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Communications Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"03fa08eb-04f8-478c-834d-a796ab116ca1","owner":[],"postedDate":"February 5th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":59105235,"name":"Health sciences/Nephrology/Renal replacement therapy"},{"id":59105236,"name":"Health sciences/Health care/Diagnosis"},{"id":59105237,"name":"Health sciences/Medical research"}],"tags":[],"updatedAt":"2026-02-05T08:24:31+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-05 08:24:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8220933","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8220933","identity":"rs-8220933","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}