Prognostic value of baseline ¹⁸F-FDG PET/CT metabolic parameters in post-transplant lymphoproliferative disorder after pediatric liver transplantation

Introduction

Post-transplant lymphoproliferative disorder (PTLD) is a serious complication after pediatric liver transplantation (pLT), comprising a spectrum of heterogeneous lesions that range from benign lymphoproliferation to malignant lymphomas (1). PTLD affects about 4.7–14.5% of pLT recipients, with a higher incidence than adults (2,3). It has been proven that the use of immunosuppressive agents and Epstein-Barr virus (EBV) infection play an important role in PTLD onset (4,5). Although risk-stratified treatment has improved outcomes, PTLD is still a major clinical challenge with poor prognosis (6,7). Therefore, there is an urgent clinical need to identify prognostic biomarkers to guide the treatment of PTLD.

There are no universally accepted prognostic biomarkers due to the heterogeneity of PTLD (8). Several biomarkers were identified previously and demonstrated high efficacy in prognosis prediction (9-11). Unfortunately, these biomarkers have been identified in adults or in populations combining adults and children, and these conclusions are primarily based on the kidney or multiple types of transplanted organs. However, the prognosis of PTLD does not only differ between adults and children, but can also differ between transplanted organs (12,13). There is no evidence whether these biomarkers can be applied to PTLD after pLT. The prognosis indicators are still controversial and unclear for PTLD after pLT.

Fluorine-18-fluorodeoxyglucose (¹⁸F-FDG) positron emission tomography (PET)/computed tomography (CT) has been widely used in patients with PTLD (14-20). ¹⁸F-FDG PET/CT metabolic parameters have been shown as prognostic predictors in adult PTLD (21). However, there are still no studies assessing the prognostic value of baseline ¹⁸F-FDG PET/CT in patients with PTLD after pLT. Consequently, we conducted this study to evaluate the prognostic value of baseline ¹⁸F-FDG PET/CT in patients with PTLD after pLT. We present this article in accordance with the STROBE reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-278/rc).

Methods

Patients

¹⁸F-FDG PET/CT images were collected retrospectively from all consecutive pLT recipients (≤18 years old) who were pathologically diagnosed with PTLD from November 2016 to January 2024 at Beijing Friendship Hospital. The inclusion criteria were as follows: (I) the interval between imaging and pathological diagnosis was less than 30 days; and (II) clinical data and follow-up were completed. The exclusion criteria were as follows: (I) received PTLD treatment before ¹⁸F-FDG PET/CT; (II) incomplete data; (III) low-quality ¹⁸F-FDG PET/CT images; (IV) patients with confirmed second malignancy that may interfere with the result; and (V) patients who did not experience endpoint events with follow-up time less than 3 years. The information collected included demographic information, clinical history, and biopsy details.

Ethics approval was obtained from the research ethics committee of Beijing Friendship Hospital (No. 2022-P2-191-01). This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments, and the requirement of individual consent for this retrospective analysis was waived.

Reference standard

Depending on the interval between the transplantation and onset, PTLD was classified into early-onset (≤1 year) and late-onset (>1 year) (22). The stage was determined according to the St. Jude staging system (23). When ¹⁸F-FDG PET/CT series scans were performed on the same patient before treatment, only the first scan was considered. The most recent date was incorporated into the analysis for patients who have undergone a secondary liver transplantation. The pathological diagnosis for PTLD was conducted by two independent pathologists, and the pathological specimens were obtained using excisional or fine-needle biopsy. The presence of EBV was detected by EBV-encoded RNA (EBER) in situ hybridization (ISH). According to the World Health Organization (WHO) 2017 classification based on morphology, PTLD is divided into non-destructive PTLD and destructive PTLD, while destructive PTLD could be further divided into polymorphic PTLD, monomorphic PTLD, and classic Hodgkin’s lymphoma-like PTLD (22).

Image acquisition

All patients were asked to keep fasted for at least 4 hours before ¹⁸F-FDG injection (3.7 MBq/kg) to ensure that the level of blood glucose was less than 11 mmol/L. This was done to improve the target-to-background ratio and acquire higher-quality images. Chloral hydrate was given to patients who could not remain motionless for sedation half an hour before scanning (0.5 mg/kg, upper limit was 20 mg). After 1 hour of injection, ¹⁸F-FDG PET/CT images were collected from the top of the cranium to the upper femur by using a PET/CT scanner (Siemens Biograph mCT, Knoxville, Germany) for 2 min/bed position. Low-dose CT was used for attenuation correction and anatomical localization, and parameters for CT were as follows: tube voltage 120 kV; tube current 160 mAs; pitch 0.55; layer thickness 3 mm; reconstructed increment 2 mm. The PET image reconstruction was performed with the ordered subset expectation maximization algorithm.

Image analysis

All images were evaluated independently by two experienced nuclear medicine physicians (K.Z. and C.W.) at Siemens Syngo via workstation, who were blinded to other information. Controversial diagnosis was resolved by consensus. Any lesion exhibiting increased ¹⁸F-FDG uptake, which could not be interpreted as physiological distribution or other non-PTLD pathologies, was identified as positive lesion. Spherical volumes were used to outline the volume of interest (VOI), and the threshold was set at 41% of the maximum standardized uptake value (SUVmax) of VOI (24,25). The following metabolic parameters were calculated from the lesion with the highest ¹⁸F-FDG uptake: SUVmax, average standardized uptake value (SUVavg), peak standardized uptake value (SUVpeak), metabolic tumor volume (MTV), and total lesion glycolysis (TLG, TLG = MTV × SUVavg). Total MTV (tMTV) and total TLG (tTLG) were calculated by summing MTV and TLG from all positive lesions (26). When calculating tMTV and tTLG, bone marrow involvement was considered only if there was focal uptake, while focal uptake lesions or diffuse splenic uptake exceeding 150% of hepatic background were considered as spleen involvement (26).

Follow-up

The prognosis of patients was determined by consulting the hospital medical records or by telephoning patients’ parents. Progression-free survival (PFS) probability was assessed for each patient, which was defined as time from date of pathological diagnosis of PTLD to the date of first observation of disease recurrence, progression, or death. Patients were considered as recurrence or progression if one of the following conditions were met: (I) recurrence/progression determined by Lugano criteria (27) for those who received series ¹⁸F-FDG PET/CT scans (17); (II) patient presented with symptom which could not be explained by other non-PTLD condition and released after receiving PTLD treatment; and (III) recurrence/progression confirmed by pathological examination. Early recurrence was defined as occurring within 1 year of diagnosis, whereas late recurrence was considered to occur after 1 year of diagnosis.

Statistical analysis

Categorical variables were expressed as counts and percentages [n (%)]. Continuous variables were expressed as median [interquartile range (IQR)]. In survival analysis, continuous variables, including age at pLT, age at PTLD, serum albumin level, serum tacrolimus level, and all metabolic parameters, were dichotomized according to specific cut-off values, which were identified using X-tile software (version 3.6.1). The Mann-Whitney U test was used to compare the differences in PET metabolic parameters between the early recurrence group and the late recurrence group. The Cox proportional hazards model was used for univariate and multivariate analysis. Candidate predictors were selected through univariate Cox proportional hazards regression, which were variables with P<0.1. Independent predictors were selected through multivariable Cox regression with stepwise forward selection. Survival curves were estimated using the Kaplan-Meier method, and the difference in PFS was assessed using the log-rank test. Finally, Mann-Whitney U test, Chi-squared test, or Pearson correlation analyses were performed to assess association or collinearity among all the variables included. The threshold for significance was set at P=0.05. Statistical analysis was carried out using SPSS Statistics 26 (IBM, Armonk, NY, USA), R software version 4.0.2 (Bell Laboratories, Vienna, Austria), and online in BioLadder.

Results

Patient characteristics and outcomes

The characteristics of all enrolled patients are shown in Table 1. After screening, there were 42 patients with PTLD after pLT who were enrolled in this retrospective study (Figure 1). There were 19 males (45.2%) and 23 females (54.8%) with a median age of 1.1 years at pLT (IQR, 0.5–2.0 years) and a median age of 2.9 years (IQR, 1.6–4.2 years) at PTLD. Twenty-one patients (50.0%) received liver transplantation because of biliary atresia, 7 (16.7%) because of metabolic diseases, including four urea cycle disorders, one glycogen storage disease type 1b, one argininemia, and one cerebrotendinous xanthomatosis. Thirty-five patients (83.3%) received living donor liver transplantation (LDLT), and 7 patients (16.7%) received deceased donor liver transplantation. Thirty-eight patients (90.5%) presented good Eastern Cooperative Oncology Group (ECOG) performance status score [0–2], while 4 patients (9.5%) presented poor ECOG score [3–4]. In laboratory results, the median serum albumin level was 35.8 g/L (IQR, 32.5–39.4 g/L), and the median serum tacrolimus level was 3.5 ng/mL (IQR, 2.2–5.3 ng/mL). Sixteen patients (37.5%) had early-onset PTLD, and 26 patients (61.9%) had late-onset PTLD. According to the St. Jude staging system (28), 13 (31.0%) and 29 (69.0%) patients had stage I–II and stage III–IV, and extranodal involvement was observed in 14 patients (33.3%). The diagnosis of 37 patients (88.1%) and 2 patient (4.8%) was based on needle biopsy of lymph nodes or liver, while 3 patients (7.1%) were diagnosed on surgical pathology of affected bowel. In terms of morphology, 31 patients (73.8%) had non-destructive PTLD, and 11 patients (26.2%) had destructive PTLD, including 6 (14.3%) polymorphic PTLD, 2 (4.8%) monomorphic PTLD, and 3 (7.1%) classical Hodgkin’s lymphoma-like PTLD (cHL PTLD). Additionally, most of patients included were EBER positive (n=40, 95.2%). Treatment of PTLD consisted of reduction of immune suppression (RIS, n=42, 100.0%), rituximab (n=30, 71.4%), antiviral agents (n=33, 78.6%), surgery (n=4, 9.5%), chemotherapy (n=3, 7.1%), EBV-specific cytotoxic T-lymphocyte (EBV-CTL, n=2, 4.8%). Details on the treatment combination were shown in Figure 2.

Figure 1 Flowchart of patient inclusion and exclusion. ¹⁸F-FDG, fluorine-18 fluorodeoxyglucose; CT, computed tomography; PET, positron emission tomography; pLT, pediatric liver transplantation; PTLD, post-transplant lymphoproliferative disorder.

Figure 2 UpSet plot of details on treatment combination. There were 23 patients received RIS + antiviral agents + rituximab, 4 patients received RIS + antiviral agents, 3 patients received RIS monotherapy, 3 patients received RIS + rituximab, 1 patients received rituximab + antiviral agents, 1 patients received chemotherapy + RIS, 1 patients received RIS + surgery, 1 patients received RIS + rituximab + surgery, 1 patient received RIS + antiviral agents + chemotherapy, 1 patient received RIS + antiviral agents + EBV-CTL + chemotherapy, 1 patient received RIS + antiviral agents + surgery, 1 patient received RIS + antiviral agents + rituximab + EBV-CTL, 1 patient received RIS + antiviral agents + rituximab + surgery. EBV-CTL, Epstein-Barr virus-specific cytotoxic T-lymphocyte; RIS, reduction of immune suppression.

During a median follow-up period of 638 days (range, 57–2,354 days), 26 patients (61.9%) experienced events defined as PFS, with 25 (59.5%) experiencing recurrence or progression and 1 (2.3%) experiencing mortality attributed to non-PTLD etiology.

¹⁸F-FDG PET/CT metabolic parameters

In terms of metabolic parameters, the median SUVmax, SUVavg, SUVpeak, MTV, and TLG were 5.8 (IQR, 3.2–8.2), 3.4 (IQR, 2.0–5.0), 3.7 (IQR, 2.2–5.6), 3.3 mL (IQR, 1.9–5.7 mL), and 10.1 (IQR, 5.8–29.5), respectively. Reflecting systemic conditions, the median tMTV was 27.4 mL (IQR, 14.6–59.3 mL), and the median tTLG was 54.5 (IQR, 29.8–164.0).

The median tTLG in the early recurrence group and the late recurrence group were 191.0 (IQR, 46.0–280.0) and 39.0 (IQR, 17.5–116.0), respectively. Mann-Whitney U test showed that tTLG (Z=2.155, P=0.029) had a significant difference between the two groups (Table 2).

Prognostic factors for PFS

According to X-tile software analysis, the optimal cut-off values for predicting PFS were 1.0 years for age at pLT, 3.1 years for age at PTLD, 29.6 g/L for serum albumin, and 4.5 ng/mL for serum tacrolimus level. For PET-derived metabolic parameters, the optimal cut-offs were 8.9 for SUVmax, 5.5 for SUVavg, 3.8 for SUVpeak, 3.3 mL for MTV, 29.8 for TLG, 57.8 mL for tMTV, and 102.9 for tTLG (Table 3).

Univariate Cox regression analysis showed that serum tacrolimus level [hazard ratio (HR) =3.215; 95% confidence interval (CI): 1.406–7.351; P=0.006], morphology (HR =3.594; 95% CI: 1.613–8.010; P=0.002), stage (HR =3.159, 95% CI: 1.177–8.482; P=0.022), SUVmax (HR =3.135; 95% CI: 1.270–7.736; P=0.013), TLG (HR =2.388; 95% CI: 1.009–5.653; P=0.048), tMTV (HR =3.659; 95% CI: 1.574-8.509; P=0.003), tTLG (HR =4.942; 95% CI: 2.142–11.406; P<0.001) were significant predictors of PFS (Table 4).

The factors with P<0.1 were enrolled in the multivariate forward stepwise Cox regression analysis, including age at pLT (P=0.071), serum albumin level (P=0.098), serum tacrolimus level (P=0.006), stage (P=0.022), morphology (P=0.002), SUVmax (P=0.013), SUVpeak (P=0.088), TLG (P=0.048), tMTV (P=0.003), and tTLG (P<0.001). The results of multivariate analysis revealed that tTLG (HR =4.942; 95% CI: 2.142–11.406; P<0.001) was a significant independent predictor of PFS. Kaplan-Meier analysis of patients stratified by tTLG revealed that patients with higher tTLG (≥102.9) had worse PFS than those with lower tTLG (log-rank <0.001, P<0.001; Figure 3).

Figure 3 Kaplan-Meier survival curves for the PFS probability. Higher baseline tTLG (≥102.9) independently predicted worse PFS of PTLD after pLT. PFS, progression-free survival; pLT, pediatric liver transplantation; PTLD, post-transplant lymphoproliferative disorder; TLG, total lesion glycolysis; tTLG, total TLG.

Discussion

In this study, we assessed the utility of baseline FDG PET/CT in predicting prognosis for PTLD after pLT. Our findings showed that PET metabolic parameters play a crucial role in predicting PFS. Among all prediction factors included, tTLG (≥102.9) independently predicted PFS.

PTLD is a serious complication in pLT recipients, which negatively affects the patients’ prognosis (29). According to whether the structure of the involved lymph nodes is intact, PTLD can be classified into two types: non-destructive PTLD and destructive PTLD (30). Destructive PTLD can be further divided into polymorphic PTLD, monomorphic PTLD, and cHL PTLD. Compared with non-destructive PTLD, destructive PTLD has a poorer prognosis and requires more aggressive treatment methods (31). Because of PTLD heterogeneity, prognostication for patients with PTLD is difficult. In prior studies, several factors have been identified as potential predictors of prognosis, including age at PTLD, gender, ECOG score, extent of involvement (stage and extranodal involvement), onset time, serum albumin level, serum tacrolimus level, EBV status, morphology, and the treatment methods (29,32-38). However, the findings on their prognostic significance have been inconsistent across studies. In our study, we found that stage and morphology were the significant predictors for PFS among clinically prognostic markers. The conclusion is consistent with the research of Füreder et al. (38). The normal architecture of involved lymph node is preserved in non-destructive PTLD cases, while it’s destroyed in destructive PTLD cases (8). Owing to the more aggressive nature, patients with destructive PTLD tend to present an inferior prognosis (39). Similarly, our investigation revealed a significant association between destructive PTLD and worse PFS.

¹⁸F-FDG PET/CT is hybrid imaging modality that provides whole body structural and glucose metabolic information. Although ¹⁸F-FDG PET/CT has a certain role in the diagnosis of PTLD in pLT recipients, its prognostic value remains to be evaluated (5,7). Given that both lymphoma and PTLD are lymphoproliferative diseases sharing similar pathological features, and numerous studies have demonstrated the prognostic value of ¹⁸F-FDG PET/CT in lymphoma, it is plausible to consider the application of ¹⁸F-FDG PET/CT in the context of PTLD as well (40,41). Therefore, we investigated the value of the metabolic parameters of baseline ¹⁸F-FDG PET/CT compared with clinical parameters to provide prognostic marker in pLT recipients with PTLD before treatment. Standardized uptake value (SUV) is the most widely used index for various purposes when analyzing ¹⁸F-FDG PET/CT images, which represents glucose uptake of VOI. It has been reported as a prognostic predictor in lymphoma (42). In our study, SUVmax and SUVpeak were predictors in the univariate analysis, but not in the multivariate analysis. However, SUV measurements are influenced by many factors, such as image noise, statistical fluctuation, and partial volume effect (43). Volume-based parameters, such as MTV and TLG, are parameters consisting of a combination of volume and metabolism. Our result showed that MTV and TLG of the lesion with the highest FDG uptake were not independent predictor for PFS. Unlike solid tumors, defining the primary and metastatic lesions of PTLD is highly challenging. This may explain why MTV and TLG of single lesion could not predict the prognosis of PTLD after pLT in our study.

As a kind of lymphoproliferative disease, PTLD can affect multiple organs and lymph nodes (8). Therefore, PTLD is often diagnosed at an advanced stage. As systemic metabolic parameters, tMTV and tTLG representing whole body lesions could reflect overall tumor burden before treatment and may predict the prognosis of PTLD after pLT. Our result showed that tTLG was the only independent predictors of PFS, while tMTV was a significant predictor of PFS but not the independent one. Sharma et al. (44) concluded that tTLG was a crucial parameter to evaluate the therapeutic efficacy of pediatric lymphoma. Another article by Chen et al. (45) found that both tMTV and tTLG were independent predictors of pediatric lymphoma prognosis. Zhou et al. (46) retrospectively analyzed 47 patients with pediatric lymphoma, while Milgrom et al. (47) conducted a prospective multicenter analysis consisting of 94 children. Both studies showed that tTLG was the only independent predictor of prognosis among metabolic parameters measured on the pretreatment ¹⁸F-FDG PET/CT, which is consistent with our findings. When ¹⁸F-FDG PET/CT indicates a higher tTLG before treatment in patients with PTLD after pLT, it suggests that the patient has a wider invasion range, and a greater challenge for treatment intervention (Figure 4). Therefore, an excessive tumor burden semi-quantified by tTLG before treatment might indeed lead to worse PFS, and these patients may benefit from more aggressive treatment.

Figure 4 ¹⁸F-FDG PET/CT performances in patients with PTLD after pLT. (A,B) A 2-month-old girl received pLT due to biliary atresia and developed abdominal pain and diarrhea 1 year after pLT. MIP images revealed multiple lymph nodes and ascending colon with ¹⁸F-FDG uptake, and tTLG was 105 (A). She received RIS + antiviral agents + rituximab + surgical excision of involved colon. MIP images revealed recurrence of ascending colon with ¹⁸F-FDG uptake 10 months after PTLD onset (B). The arrow points to recurrence lesion. (C,D) A 1 year-old girl received pLT due to biliary atresia and developed cervical lymphadenopathy 2 years after pLT. MIP images revealed cervical lymph nodes with ¹⁸F-FDG uptake, and tTLG was 50 (C). She received RIS + antiviral agents + rituximab, and post-treatment MIP images revealed complete remission (D). The patient was alive without recurrence at the end of follow-up. ¹⁸F-FDG, fluorine-18-fluorodeoxyglucose; CT, computed tomography; MIP, maximum intensity projection; PET, positron emission tomography; pLT, pediatric liver transplantation; PTLD, post-transplant lymphoproliferative disorder; RIS, reduction of immune suppression; TLG, total lesion glycolysis; tTLG, total TLG.

However, contrary to our finding, Montes de Jesus et al. (26) showed that tTLG was not associated with prognosis of adult PTLD after multiple types of organ transplantation. Besides age and type of organs, the opposing results of our study and theirs may be caused by distinct settings of thresholds. Overall, there are three main methods for setting of thresholds, including fixed absolute threshold, fixed relative threshold, and background adaptive threshold (48). The measured results of volume-based parameters vary depending on the method used, and each method has its own set of advantages and drawbacks. Based on the recommendation of the European Association of Nuclear Medicine, the threshold was set at 41% of the SUVmax in our study, due to its satisfactory inter-observer reproducibility (25). Additional studies are required to define the best method for setting of thresholds.

Additionally, our findings indicate that tTLG had a significant difference between the early recurrence group and the late recurrence group, with the latter exhibiting a significantly diminished tTLG compared to the former. However, further research is needed to find the impact of both early and late recurrence on the long-term survival of children.

There are several limitations in our study. Firstly, the study was limited by a small sample size and a relatively short follow-up duration. Second, owing to the retrospective nature of this study and different conditions of individual patients, certain diagnostic parameters, such as lactate dehydrogenase and levels of inflammatory proteins [interleukin (IL)6 or IL10], were not assessed (16). Thirdly, although we have established rigorous diagnostic criteria for PTLD, the possibility of omission or overdiagnosis still remains, which may lead to bias. Moving forward, we will conduct a prospective, large-sample, multicenter study and include more relevant predictive variables to address these issues.

Conclusions

Our study demonstrates that baseline tTLG measured on ¹⁸F-FDG PET/CT is an independent prognostic biomarker for PTLD after pLT. Patients with higher tTLG exhibit significantly poorer outcomes. Clinically, this supports using baseline tTLG to stratify PTLD patients into high- and low-risk groups at diagnosis. High-risk patients may benefit from intensified first-line therapy, such as initial combination therapy with RIS, rituximab, and chemotherapy, earlier transition to second-line treatments, or closer monitoring for relapse, while low-risk patients could potentially avoid overtreatment, such as unnecessary chemotherapy. This biomarker may thus guide personalized risk-adapted management strategies to improve survival.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-2025-278/rc

Data Sharing Statement: Available at https://qims.amegroups.com/article/view/10.21037/qims-2025-278/dss

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-278/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. Ethics approval was obtained from the research ethics committee of Beijing Friendship Hospital (No. 2022-P2-191-01). This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments, and the requirement of individual consent for this retrospective analysis was waived.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. Absalon MJ, Khoury RA, Phillips CL. Post-transplant lymphoproliferative disorder after solid-organ transplant in children. Semin Pediatr Surg 2017;26:257-66. [Crossref] [PubMed]
2. Lauro A, Arpinati M, Pinna AD. Managing the challenge of PTLD in liver and bowel transplant recipients. Br J Haematol 2015;169:157-72. [Crossref] [PubMed]
3. Hsu CT, Chang MH, Ho MC, Chang HH, Lu MY, Jou ST, Ni YH, Chen HL, Hsu HY, Wu JF. Post-transplantation lymphoproliferative disease in pediatric liver recipients in Taiwan. J Formos Med Assoc 2019;118:1537-45. [Crossref] [PubMed]
4. Lindsay J, Othman J, Heldman MR, Slavin MA. Epstein-Barr virus posttransplant lymphoproliferative disorder: update on management and outcomes. Curr Opin Infect Dis 2021;34:635-45. [Crossref] [PubMed]
5. Okamoto T, Okajima H, Uebayashi EY, Ogawa E, Yamada Y, Umeda K, Hiramatsu H, Hatano E. Management of Epstein-Barr Virus Infection and Post-Transplant Lymphoproliferative Disorder in Pediatric Liver Transplantation. J Clin Med 2022;11:2166. [Crossref] [PubMed]
6. Atallah-Yunes SA, Salman O, Robertson MJ. Post-transplant lymphoproliferative disorder: Update on treatment and novel therapies. Br J Haematol 2023;201:383-95. [Crossref] [PubMed]
7. Si Z, Lu D, Zhai L, Zheng W, Dong C, Sun C, Wang K, Zhang W, Wei X, Zhang Z, Zhao S, Gao W, Shen Z. The value of (18) F-FDG PET/CT quantitative indexes in the diagnosis of nondestructive posttransplant lymphoproliferative disorders after pediatric liver transplantation. Pediatr Transplant 2023;27:e14501. [Crossref] [PubMed]
8. Marie E, Navallas M, Navarro OM, Punnett A, Shammas A, Gupta A, Chami R, Shroff MM, Vali R. Posttransplant Lymphoproliferative Disorder in Children: A 360-degree Perspective. Radiographics 2020;40:241-65. [Crossref] [PubMed]
9. Dharnidharka VR, Webster AC, Martinez OM, Preiksaitis JK, Leblond V, Choquet S. Post-transplant lymphoproliferative disorders. Nat Rev Dis Primers 2016;2:15088. [Crossref] [PubMed]
10. Caillard S, Porcher R, Provot F, Dantal J, Choquet S, Durrbach A, et al. Post-transplantation lymphoproliferative disorder after kidney transplantation: report of a nationwide French registry and the development of a new prognostic score. J Clin Oncol 2013;31:1302-9. [Crossref] [PubMed]
11. Montanari F, Radeski D, Seshan V, Alobeid B, Bhagat G, O'Connor OA. Recursive partitioning analysis of prognostic factors in post-transplant lymphoproliferative disorders (PTLD): a 120 case single institution series. Br J Haematol 2015;171:491-500. [Crossref] [PubMed]
12. L'Huillier AG, Dipchand AI, Ng VL, Hebert D, Avitzur Y, Solomon M, Ngan BY, Stephens D, Punnett AS, Barton M, Allen UD. Posttransplant lymphoproliferative disorder in pediatric patients: Survival rates according to primary sites of occurrence and a proposed clinical categorization. Am J Transplant 2019;19:2764-74. [Crossref] [PubMed]
13. Evens AM, David KA, Helenowski I, Nelson B, Kaufman D, Kircher SM, Gimelfarb A, Hattersley E, Mauro LA, Jovanovic B, Chadburn A, Stiff P, Winter JN, Mehta J, Van Besien K, Gregory S, Gordon LI, Shammo JM, Smith SE, Smith SM. Multicenter analysis of 80 solid organ transplantation recipients with post-transplantation lymphoproliferative disease: outcomes and prognostic factors in the modern era. J Clin Oncol 2010;28:1038-46. [Crossref] [PubMed]
14. Song H, Guja KE, Iagaru A. (18)F-FDG PET/CT for Evaluation of Post-Transplant Lymphoproliferative Disorder (PTLD). Semin Nucl Med 2021;51:392-403. [Crossref] [PubMed]
15. Metser U, Lo G. FDG-PET/CT in abdominal post-transplant lymphoproliferative disease. Br J Radiol 2016;89:20150844. [Crossref] [PubMed]
16. Montes de Jesus FM, Kwee TC, Kahle XU, Nijland M, van Meerten T, Huls G, Dierckx RAJO, Rosati S, Diepstra A, van der Bij W, Verschuuren EAM, Glaudemans AWJM, Noordzij W. Diagnostic performance of FDG-PET/CT of post-transplant lymphoproliferative disorder and factors affecting diagnostic yield. Eur J Nucl Med Mol Imaging 2020;47:529-36. [Crossref] [PubMed]
17. Montes de Jesus FM, Glaudemans AWJM, Tissing WJ, Dierckx RAJO, Rosati S, Diepstra A, Noordzij W, Kwee TC. (18)F-FDG PET/CT in the Diagnostic and Treatment Evaluation of Pediatric Posttransplant Lymphoproliferative Disorders. J Nucl Med 2020;61:1307-13. [Crossref] [PubMed]
18. Feng L, Yang X, Lu X, Wang W, Yang J. Solitary Central Nervous System Relapse of Posttransplant Lymphoproliferative Disorder on 18 F-FDG PET/CT. Clin Nucl Med 2022;47:1007-9. [Crossref] [PubMed]
19. Xu YF, Yang JG. Roles of F-18-Fluoro-2-Deoxy-Glucose PET/Computed Tomography Scans in the Management of Post-Transplant Lymphoproliferative Disease in Pediatric Patient. PET Clin 2020;15:309-19. [Crossref] [PubMed]
20. Lu X, Kan Y, Wang W, Yang J. Primary Cutaneous Natural Killer/T-Cell Lymphoma: A Posttransplant Lymphoproliferative Disorder Demonstrated by 18F-FDG PET/CT. Clin Nucl Med 2021;46:595-8. [Crossref] [PubMed]
21. Van Keerberghen CA, Goffin K, Vergote V, Tousseyn T, Verhoef G, Laenen A, Vandenberghe P, Dierickx D, Gheysens O. Role of interim and end of treatment positron emission tomography for response assessment and prediction of relapse in posttransplant lymphoproliferative disorder. Acta Oncol 2019;58:1041-7. [Crossref] [PubMed]
22. Markouli M, Ullah F, Omar N, Apostolopoulou A, Dhillon P, Diamantopoulos P, Dower J, Gurnari C, Ahmed S, Dima D. Recent Advances in Adult Post-Transplant Lymphoproliferative Disorder. Cancers (Basel) 2022;14:5949. [Crossref] [PubMed]
23. Rosolen A, Perkins SL, Pinkerton CR, Guillerman RP, Sandlund JT, Patte C, Reiter A, Cairo MS. Revised International Pediatric Non-Hodgkin Lymphoma Staging System. J Clin Oncol 2015;33:2112-8. [Crossref] [PubMed]
24. Adams HJ, de Klerk JM, Fijnheer R, Heggelman BG, Dubois SV, Nievelstein RA, Kwee TC. Prognostic superiority of the National Comprehensive Cancer Network International Prognostic Index over pretreatment whole-body volumetric-metabolic FDG-PET/CT metrics in diffuse large B-cell lymphoma. Eur J Haematol 2015;94:532-9. [Crossref] [PubMed]
25. Boellaard R, Delgado-Bolton R, Oyen WJ, Giammarile F, Tatsch K, Eschner W, et al. FDG PET/CT: EANM procedure guidelines for tumour imaging: version 2.0. Eur J Nucl Med Mol Imaging 2015;42:328-54. [Crossref] [PubMed]
26. Montes de Jesus F, Dierickx D, Vergote V, Noordzij W, Dierckx RAJO, Deroose CM, Glaudemans AWJM, Gheysens O, Kwee TC. Prognostic superiority of International Prognostic Index over [18F]FDG PET/CT volumetric parameters in post-transplant lymphoproliferative disorder. EJNMMI Res 2021;11:29.
27. Cheson BD, Fisher RI, Barrington SF, Cavalli F, Schwartz LH, Zucca E, et al. Recommendations for initial evaluation, staging, and response assessment of Hodgkin and non-Hodgkin lymphoma: the Lugano classification. J Clin Oncol 2014;32:3059-68. [Crossref] [PubMed]
28. Murphy SB, Fairclough DL, Hutchison RE, Berard CW. Non-Hodgkin's lymphomas of childhood: an analysis of the histology, staging, and response to treatment of 338 cases at a single institution. J Clin Oncol 1989;7:186-93. [Crossref] [PubMed]
29. Eshraghian A, Imanieh MH, Dehghani SM, Nikeghbalian S, Shamsaeefar A, Barshans F, Kazemi K, Geramizadeh B, Malek-Hosseini SA. Post-transplant lymphoproliferative disorder after liver transplantation: Incidence, long-term survival and impact of serum tacrolimus level. World J Gastroenterol 2017;23:1224-32. [Crossref] [PubMed]
30. Campo E, Jaffe ES, Cook JR, Quintanilla-Martinez L, Swerdlow SH, Anderson KC, et al. The International Consensus Classification of Mature Lymphoid Neoplasms: a report from the Clinical Advisory Committee. Blood 2022;140:1229-53. [Crossref] [PubMed]
31. Atallah-Yunes SA, Khurana A, Habermann TM. Non-DLBCL monomorphic and Hodgkin lymphoma PTLD: clinical insights and treatment strategies. Blood Adv 2025;9:5792-801. [Crossref] [PubMed]
32. Trofe J, Buell JF, Beebe TM, Hanaway MJ, First MR, Alloway RR, Gross TG, Succop P, Woodle ES. Analysis of factors that influence survival with post-transplant lymphoproliferative disorder in renal transplant recipients: the Israel Penn International Transplant Tumor Registry experience. Am J Transplant 2005;5:775-80. [Crossref] [PubMed]
33. Dharnidharka VR, Martz KL, Stablein DM, Benfield MR. Improved survival with recent Post-Transplant Lymphoproliferative Disorder (PTLD) in children with kidney transplants. Am J Transplant 2011;11:751-8. [Crossref] [PubMed]
34. Rosenberg AS, Klein AK, Ruthazer R, Evens AM. Hodgkin lymphoma post-transplant lymphoproliferative disorder: A comparative analysis of clinical characteristics, prognosis, and survival. Am J Hematol 2016;91:560-5. [Crossref] [PubMed]
35. Ghobrial IM, Habermann TM, Maurer MJ, Geyer SM, Ristow KM, Larson TS, Walker RC, Ansell SM, Macon WR, Gores GG, Stegall MD, McGregor CG. Prognostic analysis for survival in adult solid organ transplant recipients with post-transplantation lymphoproliferative disorders. J Clin Oncol 2005;23:7574-82. [Crossref] [PubMed]
36. Caillard S, Lelong C, Pessione F, Moulin BFrench PTLD Working Group. Post-transplant lymphoproliferative disorders occurring after renal transplantation in adults: report of 230 cases from the French Registry. Am J Transplant 2006;6:2735-42. [Crossref] [PubMed]
37. Kumarasinghe G, Lavee O, Parker A, Nivison-Smith I, Milliken S, Dodds A, Joseph J, Fay K, Ma DD, Malouf M, Plit M, Havryk A, Keogh AM, Hayward CS, Kotlyar E, Jabbour A, Glanville AR, Macdonald PS, Moore JJ. Post-transplant lymphoproliferative disease in heart and lung transplantation: Defining risk and prognostic factors. J Heart Lung Transplant 2015;34:1406-14. [Crossref] [PubMed]
38. Füreder A, Kropshofer G, Benesch M, Dworzak M, Greil S, Huber WD, Hubmann H, Lawitschka A, Mann G, Michel-Behnke I, Müller-Sacherer T, Pichler H, Simonitsch-Klupp I, Schwinger W, Szepfalusi Z, Crazzolara R, Attarbaschi AAustrian Society of Pediatric Hematology and Oncology. Characteristics, management, and outcome of pediatric patients with post-transplant lymphoproliferative disease-A 20 years' experience from Austria. Cancer Rep (Hoboken) 2021;4:e1375. [Crossref] [PubMed]
39. Voorhees TJ, Kannan KK, Galeotti J, Grover N, Vaidya R, Moore DT, Montgomery ND, Beaven AW, Dittus C. Identification of high-risk monomorphic post-transplant lymphoproliferative disorder following solid organ transplantation. Leuk Lymphoma 2021;62:86-94. [Crossref] [PubMed]
40. Jing F, Liu Y, Zhao X, Wang N, Dai M, Chen X, Zhang Z, Zhang J, Wang J, Wang Y. Baseline (18)F-FDG PET/CT radiomics for prognosis prediction in diffuse large B cell lymphoma. EJNMMI Res 2023;13:92. [Crossref] [PubMed]
41. Driessen J, Zwezerijnen GJC, Schöder H, Kersten MJ, Moskowitz AJ, Moskowitz CH, Eertink JJ, Heymans MW, Boellaard R, Zijlstra JM. Prognostic model using 18F-FDG PET radiomics predicts progression-free survival in relapsed/refractory Hodgkin lymphoma. Blood Adv 2023;7:6732-43. [Crossref] [PubMed]
42. Miyazaki Y, Nawa Y, Miyagawa M, Kohashi S, Nakase K, Yasukawa M, Hara M. Maximum standard uptake value of 18F-fluorodeoxyglucose positron emission tomography is a prognostic factor for progression-free survival of newly diagnosed patients with diffuse large B cell lymphoma. Ann Hematol 2013;92:239-44. [Crossref] [PubMed]
43. Akamatsu G, Ikari Y, Nishida H, Nishio T, Ohnishi A, Maebatake A, Sasaki M, Senda M. Influence of Statistical Fluctuation on Reproducibility and Accuracy of SUVmax and SUVpeak: A Phantom Study. J Nucl Med Technol 2015;43:222-6. [Crossref] [PubMed]
44. Sharma P, Gupta A, Patel C, Bakhshi S, Malhotra A, Kumar R. Pediatric lymphoma: metabolic tumor burden as a quantitative index for treatment response evaluation. Ann Nucl Med 2012;26:58-66. [Crossref] [PubMed]
45. Chen S, He K, Feng F, Wang S, Yin Y, Fu H, Wang H. Metabolic tumor burden on baseline (18)F-FDG PET/CT improves risk stratification in pediatric patients with mature B-cell lymphoma. Eur J Nucl Med Mol Imaging 2019;46:1830-9. [Crossref] [PubMed]
46. Zhou Y, Hong Z, Zhou M, Sang S, Zhang B, Li J, Li Q, Wu Y, Deng S. Prognostic value of baseline (18) F-FDG PET/CT metabolic parameters in paediatric lymphoma. J Med Imaging Radiat Oncol 2020;64:87-95. [Crossref] [PubMed]
47. Milgrom SA, Kim J, Pei Q, Lee I, Hoppe BS, Wu Y, Hodgson D, Kessel S, McCarten KM, Roberts K, Lo AC, Cole PD, Kelly KM, Cho SY. Baseline metabolic tumour burden improves risk stratification in Hodgkin lymphoma: A Children's Oncology Group study. Br J Haematol 2023;201:1192-9. [Crossref] [PubMed]
48. Im HJ, Bradshaw T, Solaiyappan M, Cho SY. Current Methods to Define Metabolic Tumor Volume in Positron Emission Tomography: Which One is Better? Nucl Med Mol Imaging 2018;52:5-15. [Crossref] [PubMed]