The role of ¹⁸F-FDG PET/CT metabolic parameters in the differential diagnosis of post-transplant lymphoproliferative disorder after pediatric liver transplantation

Introduction

Post-transplant lymphoproliferative disorder (PTLD) is a significant complication after liver transplantation. PTLD encompasses a heterogeneous and potentially fatal group of malignant or pre-malignant lesions that range from benign lymphoproliferative disorders to aggressive lymphomas (1). Compared to adults, children have a higher risk of developing PTLD, and the incidence of PTLD among pediatric liver transplantation (pLT) recipients is 4.7–14.5% (2,3). Epstein-Barr virus (EBV) infection is believed to play a crucial role in pediatric PTLD development (4,5). In children with PTLD, lymphadenopathy is a common clinical symptom, and may be accompanied by other manifestations, including organ dysfunction, B symptoms, and allograft involvement (5,6). However, lymphadenopathy is a frequent and non-specific sign in healthy or sick children, and pediatric lymphadenopathy is benign in most patients (7-10).

Due to the significant differences in treatment methods for PTLD and non-PTLD lymphadenopathy, the differential diagnosis of PTLD and non-PTLD lymphadenopathy is crucial (10,11). The current diagnostic approach for PTLD relies on pathological examination (12). Unfortunately, not every pLT recipient suspected of PTLD can undergo biopsy due to its invasiveness, high cost, and potential complications (13-15). The use of the EBV-DNA viral load as a potential biomarker for PTLD diagnosis is limited by the kind of specimens required, the time of detection, its threshold value, and its low specificity in clinical application (16-21). Therefore, it is imperative to explore more effective and non-invasive approaches for discriminating between PTLD and non-PTLD lymphadenopathy.

Fluorine-18 fluorodeoxyglucose positron emission tomography/computerized tomography (¹⁸F-FDG PET/CT) has been widely employed in the detection, staging, and assessment of treatment responses in PTLD (22-29). However, research on the differential diagnostic value of the ¹⁸F-FDG PET/CT metabolic parameters of PTLD and non-PTLD lymphadenopathy in pLT recipients is limited. This study sought to evaluate the differential diagnostic efficacy of ¹⁸F-FDG PET/CT metabolic parameters for distinguishing between PTLD and non-PTLD lymphadenopathy in pLT recipients with suspected PTLD. We present this article in accordance with the STARD reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-23-1059/rc).

Methods

Patients

We retrospectively collected the ¹⁸F-FDG PET/CT scans of all consecutive pLT recipients (aged ≤18 years) who were clinically suspected of PTLD at the Beijing Friendship Hospital, Capital Medical University from November 2016 to September 2022. The ¹⁸F-FDG PET/CT indications for patients were as follows: lymphadenopathy; appearance of suspicious symptoms, such as unexplained digestive symptoms (including abdominal pain, diarrhea, vomiting, and bloating), anemia, B symptoms (including weight loss >10%, night sweats, and a body temperature >38 ℃), and suspicious lesions found by ultrasound. For patients who underwent series ¹⁸F-FDG PET/CT, only their first scan was included in the study. For patients who underwent a secondary liver transplantation, the most recent date of transplantation was used for further analysis (30). Patients were excluded from the study if they met any of the following exclusion criteria: (I) had incomplete clinical or imaging data; (II) had a confirmed second malignancy that might interfere with the results; (III) had received preemptive PTLD treatment before imaging; (IV) had poor quality images; (V) a non-PTLD patient with a follow-up period <2 years after the pathological examination (24,31). Data were collected from the electronic patient files, including demographic information, clinical history, and biopsy details; the biopsies were planned after imaging and diagnosis.

The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by the Research Ethics Committee of the Beijing Friendship Hospital, Capital Medical University, and the requirement of individual consent for this retrospective analysis was waived.

Reference standard

All the patients were diagnosed by pathological examination. Pathological specimens were obtained through surgical resection or fine needle aspiration, and the diagnoses were confirmed by at least two pathologists, who were blinded to the ¹⁸F-FDG PET/CT results. EBV-encoded RNA (EBER) in situ hybridization was performed to confirm the presence of EBV (32). All the PTLD cases were classified according to World Health Organization 2017 classification (33).

Image acquisition

All the patients underwent ¹⁸F-FDG PET/CT (Siemens Biograph mCT, Germany) according to the recommended protocol of the manufacturer. The patients were instructed to fast at least 4 hours to ensure they had a glucose level lower than 11.1 mmol/L, and were then subsequently injected with ¹⁸F-FDG (3.7 MBq/kg). The whole-body scans were acquired from the skull base to the upper femur approximately 1 hour after the injection. Children who could not remain still during the imaging process were given chloral hydrate for sedation half an hour prior to scanning (0.5 mg/kg, upper limit: 20 mg). The low-dose CT was performed for attenuation correction and anatomical reference with the following parameters: tube voltage: 120 kV; tube current: 160 mAs; pitch: 0.55; layer thickness: 3 mm; and reconstructed increment: 2 mm. The PET images were acquired for 2 min/bed position and were reconstructed using the ordered subset expectation maximization algorithm.

Image analysis

All the ¹⁸F-FDG PET/CT images were reviewed by two experienced nuclear medicine physicians, who were blinded to the pathological data, at a workstation. Consensus meetings were held to resolve any controversial diagnoses (25). Positive lesions were defined as focal areas exhibiting increased ¹⁸F-FDG uptake that were not associated with physiological distribution or non-PTLD pathologies (25,31). The volume of interest (VOI) was outlined by spherical volumes, and the threshold was set at 41% of the maximum standardized uptake value (SUV_max) of VOI according to the European Association of Nuclear Medicine because of its satisfactory inter-observer reproducibility (34,35). The metabolic parameters, including SUV_max, mean standardized uptake value (SUV_mean), peak standardized uptake value (SUV_peak), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) (which was calculated as the SUV _mean × MTV), were calculated from the lesion, with the SUV_max higher than other lesions in each patient. By summing the MTV and TLG from all the positive lesions, the MTV total (MTV_total) and TLG total (TLG_total) were also calculated (36). When calculating the metabolic parameters for the whole body, only focal uptake was considered indicative of bone marrow involvement, while diffuse involvement was excluded from consideration (37). Diffuse splenic uptake exceeding 150% of the hepatic background or any focal lesion in the spleen was considered splenic disease (37).

Statistical analysis

The qualitative variables are described as the count and percentage [n (%)], and were compared using the Chi-squared test. The normality of the continuous variables was determined by the Shapiro-Wilk test. Normally distributed continuous variables are expressed as the mean ± standard deviation, and skewed distributed continuous variables are expressed as the median with the interquartile range. Comparisons of the continuous variables between PTLD and non-PTLD lymphadenopathy were performed using the Mann-Whitney test or student t-test. The area under the receiver operating characteristic (ROC) curves (AUC) was calculated to evaluate the predictive value of ¹⁸F-FDG PET/CT. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the metabolic parameters were computed with 95% confidence intervals (CIs). Multivariable logistics regression models were built to discriminate between PTLD and non-PTLD lymphadenopathy. The maximum AUC was the basis for determining the best model, and the model not only included both the clinical and metabolic parameters, but also reflected the hottest lesion and whole-body situation. The Delong test was conducted using the R software pROC package (https://CRAN.R-project.org/package=pROC) to test differences between ROC curves. To compare the diagnostic values of different models, integrated discriminatory improvement (IDI) and net reclassification improvement (NRI) were computed using the R software PredictABEL package (https://CRAN.R-project.org/package=PredictABEL). The threshold for significance was set at P=0.05. The statistical analysis was carried out using SPSS Statistics 26 (IBM, Armonk, USA) and R software version 4.0.2 (Bell Laboratories, USA).

Results

Clinical characteristics

A total of 88 potential pLT recipients were identified. After screening, 57 eligible pLT patients were enrolled in this retrospective study, of whom 40 had PTLD and 17 had non-PTLD lymphadenopathy (Figure 1). The clinical characteristics of the patients in the PTLD and non-PTLD groups are set out in Table 1. In relation to the demographic data, age at the time of liver transplantation or at the time PTLD was suspected, and the time from liver transplantation to PTLD was suspected did not differ significantly between the PTLD and non-PTLD groups; however, gender differed significantly between the two groups [boy:girl, 18 (45%):22 (55%) vs. 13 (76.5%):4 (23.5%), respectively, P=0.029]. In relation to the clinical characteristics, digestive symptoms were more common in PTLD patients than non-PTLD patients [18 (45%) vs. 2 (12%), respectively, P=0.016]. Conversely, no significant difference in the percentages of patients with other symptoms, including anemia, B symptoms, and lymphadenopathy, was observed between the PTLD and non-PTLD groups. In relation to the pathological examinations, 15 (88%) patients had reactive lymphoid hyperplasia and 2 (12%) patients had dermatopathic lymphadenitis in the non-PTLD group, while 30 (75%) patients had non-destructive PTLD, 6 (15%) patients had polymorphic PTLD, 2 (5%) patients had monomorphic PTLD, and 2 (5%) patients had classical Hodgkin’s lymphoma-like PTLD in the PTLD group. In addition, more patients were EBER positive in the PTLD group than in the non-PTLD group [38 (95%) vs. 10 (59%), respectively, P=0.002].

Figure 1 Flowchart of the patient inclusion and exclusion process. ¹⁸F-FDG PET/CT, fluorine-18 fluorodeoxyglucose positron emission tomography/computerized tomography; PTLD, post-transplant lymphoproliferative disorder.

Comparison of the ¹⁸F-FDG PET/CT metabolic parameters between the PTLD group and non-PTLD group

The ¹⁸F-FDG PET/CT findings are set out in Table 2. The majority of the metabolic parameters, including the SUV_max [5.8 (3.3–7.9) vs. 3.5 (2.9–4.3), respectively, P=0.008], the SUV_mean [3.4 (2.1–4.9) vs. 2.2 (1.9–2.8), respectively, P=0.017], TLG [9.9 (5.7–29.1) vs. 5.0 (3.0–7.8), respectively, P=0.002], the MTV_total [27.4 (14.3–62.2) vs. 18.0 (12.0–24.3), respectively, P=0.040], and the TLG_total [54.2 (29.5–181.8) vs. 36.0 (22.5–43.5), respectively, P=0.026], were higher in the PTLD patients than non-PTLD patients. There were no statistically significant differences in the SUV_peak or MTV between the two groups.

The differential diagnostic performance of the ¹⁸F-FDG PET/CT metabolic parameters in the PTLD group and non-PTLD group

The performance results of ¹⁸F-FDG PET/CT for differential diagnosis are presented in Table 3. The ROC curves showed that TLG had the highest diagnostic efficacy in differentiating between PTLD and non-PTLD lymphadenopathy, with cut-off value of 6.08 and an AUC of 0.757 (95% CI: 0.632–0.883). TLG had an accuracy of 0.719 (95% CI: 0.585–0.830), a sensitivity of 0.725 (95% CI: 0.559–0.849), a specificity of 0.706 (95% CI: 0.440–0.886), a PPV of 0.853 (95% CI: 0.682–0.945), and a NPV of 0.522 (95% CI: 0.311–0.726). A multivariate logistic regression was then conducted to establish the following differential diagnostic model: digestive symptoms plus SUV_max plus TLG plus MTV_total. The model had an AUC of 0.868 (95% CI: 0.769–0.966), an accuracy of 0.754 (95% CI: 0.622–0.859), a sensitivity of 0.675 (95% CI: 0.508–0.809), a specificity of 0.941 (95% CI: 0.692–0.997), a PPV of 0.964 (95% CI: 0.798–0.998), and a NPV of 0.552 (95% CI: 0.360–0.730) (see Table 3). The model is shown below.

By substituting the parameters in the model, we could distinguish between PTLD and non-PTLD patients. If a patient had digestive symptoms, the “digestive symptoms” factor in the model was equal to 1, if not, it was equal to 0. Other metabolic parameters could be calculated at the workstation. If the value of the model calculated was less than the cut-off value (of 0.77), the prediction result of the model was PTLD. The results of the comparisons between the model and each of the metabolic parameters that the model contained are summarized in Table 4. Based on the Delong test, the model had a significantly higher AUC than the SUV_max (Z=2.355, P=0.019), TLG (Z=2.118, P=0.034), and MTV_total (Z=2.181, P=0.029). Compared with the SUV_max, the IDI of combined model was 0.252 (95% CI: 0.110–0.394, P<0.001) and the NRI of combined model was 0.628 (95% CI: 0.240–1.016, P=0.002); compared with TLG, the IDI was 0.321 (95% CI: 0.211–0.430, P<0.001) and the NRI of combined model was 0.697 (95% CI: 0.356–1.038, P<0.001); compared with the MTV_total, the IDI of combined model was 0.331 (95% CI: 0.191–0.472, P<0.001), the NRI of combined model was 0.704 (95% CI: 0.340–1.069, P<0.001). Thus, the model exhibited better differential diagnostic performance with multiparametric combination than metabolic parameters alone

Discussion

Our study showed that the ¹⁸F-FDG PET/CT metabolic parameters could differentiate between PTLD and non-PTLD lymphadenopathy in pLT recipients with suspected PTLD. Further, diagnostic models based on ¹⁸F-FDG PET metabolic parameters (i.e., SUV_max, TLG, and MTV_total) and clinical variables (i.e., digestive symptoms) can effectively help to differentiate between PTLD and non-PTLD lymphadenopathy.

It is still a challenge to differentiate PTLD from non-PTLD in pLT recipients. The clinical presentation of PTLD, including the nodal and extranodal disease, is non-specific and highly variable. Because of the abundant lymphoid tissues in the digestive system, the gastrointestinal tract is the most commonly affected organ among the extranodal organs (6,38). Therefore, PTLD needs to be considered in pLT recipients with unexplained digestive symptoms other than lymphadenopathy, such as abdominal pain, vomiting, and diarrhea (39,40). Radiographic assessment, which is non-invasive, is an important component of diagnosing PTLD (41). Ultrasound is the preferred initial non-invasive imaging examination; however, it may be greatly affected by intestinal gas (6). CT, which is another routine examination, is prone to missed diagnoses in cases with extranodal lesions (42). When chest involvement is suspected, magnetic resonance imaging is restricted by the small number of signal-generating protons due to the air in the lungs (43).

¹⁸F-FDG PET/CT is a combination of CT and PET techniques that provides metabolic and anatomic information simultaneously. However, the use of PTLD in pediatric patients, particularly those who have undergone liver transplantation and subsequently developed PTLD, remains relatively limited. A variety of ¹⁸F-FDG PET/CT parameters have been used to reflect the metabolic activity of the target VOI. The SUV_max, which is defined as the maximum uptake value among the VOI, is the most widely used parameter due to its simplicity and repeatability. In our study, the SUV_max had high specificity but low sensitivity. The discriminatory value of the SUV_max has been also reported by Si et al. (44). However, the SUV_max does not represent the metabolism of the whole lesion and may be disturbed by various factors, such as image noise, statistical fluctuation, and partial volume effect (45).

As a volumetric parameter, the MTV represents the volume of cells with high glycolytic activity, while TLG reflects both the volume and activity, and thus provides a better measure of the whole tumor metabolic activity (46). In our study, TLG had moderate differential diagnostic ability (AUC =0.757), which shows the discrimination value of the volumetric parameters. Due to the clinical characteristics of PTLD in pLT, we also evaluated the condition of systemic lesions by ¹⁸F-FDG PET/CT. By summing the MTV and TLG of all target lesions, the MTV_total and TLG_total, which are indicators of whole-body tumor burden and have been proven valuable in predicting prognosis and evaluating treatment efficacy, were then calculated (47). However, the discriminatory performance of the MTV_total (AUC =0.688) and TLG_total (AUC =0.674) were not satisfactory. This may be due to the lower metabolic activity of some PTLD lesions, which is similar to that of non-PTLD lesions. Therefore, the application of a single metabolic parameter of ¹⁸F-FDG PET/CT may not provide assistance in providing differential diagnoses.

Due to the limitations of any single parameter in providing effective differential diagnosis capabilities, we integrated the clinical characteristics and metabolic parameters to construct a diagnostic model. The model combined clinical characteristics (i.e., digestive symptoms), the metabolic parameters of single lesions (i.e., the SUV_max and TLG), and the systemic metabolic status of the patients (i.e., the MTV_total) (Figure 2). The IDI and Delong test results showed that the combination model had a higher diagnostic efficacy (AUC =0.868) than any of the metabolic parameters alone, and moderate sensitivity (0.675) and high specificity (0.941). To our knowledge, our study is the first to establish a diagnostic model for discriminating between PTLD and non-PTLD lymphadenopathy.

Figure 2 PTLD and non-PTLD lymphadenopathy in pLT recipients. (A) PTLD case. A 4-year-old boy presented with abdominal pain and lymphadenopathy 3 years after pLT for biliary atresia. The key findings of the ¹⁸F-FDG PET/CT were as follows. First, the posterior wall of the nasopharynx was thickened with abnormal ¹⁸F-FDG uptake. Second, the cervical lymph nodes were enlarged with abnormal ¹⁸F-FDG uptake (long arrow). Third, the small bowel wall was thickened focally with abnormal ¹⁸F-FDG uptake. A small bowel resection was performed, and the results showed monomorphic PTLD (short arrow). (B) Non-PTLD lymphadenopathy. A 2-year-old boy presented with lymphadenopathy alone 2 years after pLT for biliary atresia. The key findings of the ¹⁸F-FDG PET/CT were as follows. First, the posterior wall of the nasopharynx was thickened with abnormal ¹⁸F-FDG uptake. Second, the cervical and retroperitoneal lymph nodes were enlarged with abnormal ¹⁸F-FDG uptake (short arrow). A cervical lymph node biopsy was performed, and the results showed reactive hyperplasia (short arrow). PTLD, post-transplant lymphoproliferative disorder; pLT, pediatric liver transplantation; ¹⁸F-FDG PET, fluorine-18 fluorodeoxyglucose positron emission tomography.

This study had some limitations. First, this study conducted a retrospective analysis at a single center with a limited sample size. Second, as this was a retrospective study, many parameters that may be helpful for diagnosis, such as lactate dehydrogenase and levels of inflammatory proteins (interleukin 6 or interleukin 10), were not included (24). In the future, we will add additional parameters to enhance the discriminative power of our model. Third, while we had strict standards for the diagnosis of PTLD and non-PTLD lesions, there is still a possibility of omission or overdiagnosis, especially for PTLD patients, which might have led to a bias. Based on these limitations, it is recommended that a multi-center prospective study with a larger sample size be conducted in the future to further investigate this topic.

In conclusion, our study found that ¹⁸F-FDG PET/CT is an efficient technique for the differential diagnosis between PTLD and non-PTLD lymphadenopathy in pLT recipients. Among the multiple parameters examined, TLG was the most effective parameter. The diagnostic model that combined clinical characteristics and metabolic parameters showed excellent performance in the differential diagnosis of PTLD and non-PTLD lymphadenopathy. Our results might improve the diagnosis of PTLD in pLT recipients and result in fewer children having to undergo unnecessary invasive examinations and treatments.

Acknowledgments

Funding: This study was supported by grants from Beijing Municipal Natural Science Foundation (No. 7232031) and National Natural Science Foundation of China (No. 82272034).

Footnote

Reporting Checklist: The authors have completed the STARD reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-23-1059/rc

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-23-1059/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. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by the Research Ethics Committee of the Beijing Friendship Hospital, Capital Medical University, 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/.

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