Next Article in Journal
McKittrick–Wheelock Syndrome, a Rare Cause of Nonresponsive Persistent Dyselectrolytemia
Previous Article in Journal
The Role of Imaging in Inflammatory Bowel Diseases: From Diagnosis to Individualized Therapy
Previous Article in Special Issue
Predictive Value of Quantitative Parameters of 18F-FDG PET/CT in Patients with Liposarcoma
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Diagnostics
Volume 15
Issue 19
10.3390/diagnostics15192458
diagnostics-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Systematic Review

The Value of Baseline [18F]FDG-PET in Predicting the Progression-Free Survival in Patients with Thymic Epithelial Tumours: A Systematic Review and Meta-Analysis

by
Alberto Miceli
1,*,
Maria Librando
2,
Francesco Dondi
3,
Lorenzo Jonghi-Lavarini
4,
Adriana D’Antonio
5,
Antonio Mura
6,
Anna Giulia Nappi
7,
Guido Rovera
8,
Maria Silvia De Feo
9,
Giulia Santo
10 and
Francesco Lanfranchi
11
1
Nuclear Medicine Unit, Azienda Ospedaliero-Universitaria SS. Antonio e Biagio e Cesare Arrigo, 15121 Alessandria, Italy
2
Nuclear Medicine Unit, Department of Biomedical and Dental Sciences and Morpho-Functional Imaging, University of Messina, 98122 Messina, Italy
3
Division of Nuclear Medicine, Università degli Studi di Brescia and ASST Spedali Civili di Brescia, 25123 Brescia, Italy
4
Department of Radiotherapy and Nuclear Medicine, ASST-Cremona, 26100 Cremona, Italy
5
Department of Advanced Biomedical Sciences, University Federico II, 80131 Naples, Italy
6
Unit of Nuclear Medicine, Department of Medicine, Surgery and Pharmacy, University of Sassari, 07100 Sassari, Italy
7
Nuclear Medicine Unit, Interdisciplinary Department of Medicine, University of Bari “Aldo Moro”, Piazza Giulio Cesare 11, 70124 Bari, Italy
8
Department of Medical Sciences, University of Turin, 10126 Turin, Italy
9
Department of Radiological Sciences, Oncology and Anatomo Pathology, Sapienza University of Rome, 00151 Rome, Italy
10
Department of Experimental and Clinical Medicine, “Magna Graecia” University of Catanzaro, 88100 Catanzaro, Italy
11
Department of Experimental Medicine (DIMES), University of Genoa, 16132 Genoa, Italy
 
add Show full affiliation list
 
remove Hide full affiliation list
*
Author to whom correspondence should be addressed.
Diagnostics 2025, 15(19), 2458; https://doi.org/10.3390/diagnostics15192458
Submission received: 25 July 2025 / Revised: 11 September 2025 / Accepted: 23 September 2025 / Published: 26 September 2025
(This article belongs to the Special Issue Application of PET/CT in Diagnosis and Treatment of Cancers)
Browse Figures
Versions Notes

Abstract

Background/Objectives: [18F]FDG-PET is often used for staging thymic epithelial tumours (TETs). However, its prognostic role remains uncertain. The aim of this present systematic review and meta-analysis is to assess the prognostic value of baseline [18F]FDG-PET-derived semiquantitative parameters in predicting progression-free survival (PFS) in patients with TETs. Methods: A systematic review and meta-analysis were conducted according to PRISMA guidelines. PubMed, Embase, and Scopus databases were searched up to 30 May 2025. Studies evaluating the prognostic impact of [18F]FDG-PET parameters on PFS in TETs were included. Pooled hazard ratios (HRs) with 95% confidence intervals (CIs) were calculated. Results: Six retrospective studies involving 593 patients were included. Maximum standardized uptake value (SUVmax), analysed as a continuous variable in four studies, significantly predicted worse PFS (HR: 1.18, 95% CI: 1.08–1.29, p < 0.001), with high inter-study heterogeneity (I2 = 79.7%). When dichotomized (two studies), higher SUVmax was associated with significantly poorer PFS (HR: 9.00, 95% CI: 2.93–27.71). Similarly, mean SUV (SUVmean) as a continuous predictor was also significantly associated with impaired PFS (HR: 1.41, 95% CI: 1.25–1.59), but only two studies assessed this parameter. Conversely, metabolic tumour volume (MTV) and total lesion glycolysis (TLG), both assessed as continuous prognosticators, did not show a significant prognostic value. Notably, in both MTV and TLG analyses, two studies contributed a weight of 0%, reflecting limited precision and highlighting the need for larger data. Conclusions: Baseline [18F]FDG-PET parameters such as SUVmax and SUVmean showed a potential prognostic value in patients with TETs. However, these results are based on a limited number of retrospective studies with significant heterogeneity. Prospective multicentre investigations are necessary to confirm the potential role of [18F]FDG-PET for risk stratification in TETs.

1. Introduction

Thymic epithelial tumours (TETs) are a heterogeneous group of thoracic malignancies originating from the thymic epithelium with distinct biological behaviours and prognoses [1]. Although rare, they represent the most common tumours of the anterior mediastinum in adults (approximately 50% of all anterior mediastinal masses) [1,2,3]. The annual incidence is approximately 1 in 769,000, and the male-to-female ratio is 1:1.4. Usually, the age of onset is between 30 and 70 (with a mean age of 50), but in some rare cases they can also appear during childhood [4]. While half of patients can be asymptomatic, others can experience chest symptoms, such as dyspnoea, chest pain, upper respiratory tract infections, fatigue, weight loss, prominent neck veins, and cough or pneumonia. Thymomas are often associated (in 20–40% of patients) with myasthenia gravis, an autoimmune disease that manifests as double vision, ptosis, dysphagia, and weakness [5,6]. Based on histology, thymomas are primarily classified as low-risk thymomas (A, AB and B1), high-risk thymomas (B2, B3), and thymic carcinomas (TC) [1]. In addition, functional thymic neuroendocrine tumours have been also described [7,8,9].
Surgery plays a primary role in the management of patients in whom complete resection is feasible. The benefit of post-operative radiotherapy (PORT) is still controversial, since it could be related to stage, histotype, and preoperative chemotherapy. If the tumour is unresectable at diagnosis, radiotherapy or concurrent chemoradiotherapy is the most used approach. More recently, immune checkpoint inhibitor therapy has shown promising results in TETs patients [10,11,12,13,14].
For these reasons, accurate lesion identification and staging is crucial for treatment planning and prognostication. Currently the Masaoka–Koga and TNM systems are commonly used for this purpose [1,15,16].
In this context, multimodal imaging plays a pivotal role. Usually, after chest radiography, Computed Tomography (CT) is the most common modality used to characterize mediastinal masses and for proper patient staging. However, in some cases, Magnetic Resonance Imaging (MRI) provides high soft tissue contrast and tissue characterization, allowing further evaluation of indeterminate mediastinal lesions. Specifically compared to CT, MRI can better differentiate cystic from solid lesions, as well as thymic hyperplasia from thymic malignancy, thanks to its own ability to identify and detect the fat component [17,18,19,20].
The role of [18F]Fluorodeoxyglucose positron emission tomography ([18F]FDG-PET) in TETs is debated, despite its increased use.
[18F]FDG-PET is not generally recommended in the definition of thymic masses [21], due to its intrinsic limited value in the differential diagnosis from other mediastinal entities, such as primary mediastinal lymphomas [22,23,24]. In addition, increased thymic [18F]FDG uptake could be physiologically detected especially in young people, or alternatively could indicate the presence of thymic hyperplasia, also as a rebound of external stress, like in patients under chemotherapy [25,26]. However, [18F]FDG-PET can provide valuable diagnostic information and it is frequently used to complete the staging work-up, especially in case of tumours with aggressive histology and an advanced stage [21].
Currently, the added value of [18F]FDG-PET lies to the possibility to extract some semi-quantitative data that could potentially play a role as a prognostic marker, as well as help in the evaluation of primary mediastinal masses.
Maximum standardized uptake value (SUVmax), the most used and accessible PET parameter in clinical practice, has already been reported by multiple studies as a potential predictive biomarker for differentiating low-risk thymoma from high-risk thymoma and TC [27,28,29,30,31]. However, above the most quoted SUVmax, other semiquantitative parameters, such as mean SUV (SUVmean), Metabolic Tumour Volume (MTV), and Total Lesion Glycolysis (TLG) are gaining increasing interest in the scientific community to noninvasively differentiate benign tumours from malignant mediastinal ones [32]. In addition, the progressive and rapid evolution of imaging acquisition, processing and analyses, could also open the way for a better understanding of tumour behaviour. In this scenario, machine learning and texture analysis, which comprises a variety of mathematical methods for calculating indices that describe the relationships between grey-level pixel intensity and their spatial distribution within an image, have been increasingly applied in clinical studies. In the setting of TETs, preliminary results showed the potential ability of [18F]FDG-PET to differentiate between different tumour grades, by combining SUVmax and with measures of intratumoural heterogeneity, thus possibly allowing for a more tailored “non-invasive” diagnosis, risk stratification, and staging of these rare tumours [33,34,35].
Despite the increasing clinical use of [18F]FDG-PET, its prognostic value in TETs remains scarcely explored. In fact, the current literature is mainly focused on the diagnostic role of PET or on the histological stratification of TETs, without quantitatively addressing progression-free survival (PFS). Therefore, the present systematic review and meta-analysis specifically aimed to evaluate the prognostic role of baseline [18F]FDG-PET-derived parameters in predicting PFS in patients with TETs, providing a synthesis of the available evidence and identifying current limitations.

2. Materials and Methods

2.1. Research Strategy

The present study was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement [36]. No pre-defined protocol was registered. The literature search was performed in PubMed, Embase, and Scopus databases from inception to January 2025, with an update conducted in May 2025. Adopted research strings are reported in Table 1.

2.2. Study Selection

Two independent reviewers (AM and FL) conducted the screening and selection of studies using the Rayyan platform [37]. Discrepancies were resolved with consensus. We included only studies that evaluated the prognostic value of baseline [18F]FDG-PET-derived semiquantitative parameters in patients with TETs. The outcome considered was PFS, encompassing either disease progression, or recurrence or death as events. In the case of more than one publication with an overlapping patients’ sample, we only considered the most recent work. Only studies providing hazard ratios (HRs) and relative 95% confidence intervals (95% CIs) were included. Sub-group or post-hoc analyses from other studies, case reports and abstract only were excluded. Only manuscripts written in English were considered.

2.3. Data Extraction

For each study, data were independently collected by two reviewers (AM and FL), and discrepancies were solved by consensus. The extracted information included author, year, study design, number of enrolled patients included in survival analyses, follow-up duration, TETs WHO classification, TETs stage, [18F]FDG-PET-derived semiquantitative parameters and their computing modalities, and the events during follow-up (for PFS calculation). For each [18F]FDG-PET parameter investigated as a predictor of PFS in at least two different studies, HRs and respective 95% CIs from univariate and (if performed or available) multivariate analyses were also reported. When imaging variables were tested as protective factors (i.e., lower values associated with better outcomes), HRs and CIs were transformed so that higher values of metabolic parameters would reflect a negative association with PFS.
In studies applying more than one method for image analyses, the PET-derived measures obtained with the more extensively validated approach were collected.

2.4. Statistical Analysis

Forest plots were generated to visualize pooled HRs for the association between PET-based measures and PFS. Statistical heterogeneity between studies included in the meta-analysis was assessed with the Cochrane’s Q statistic [38]. The I2 statistics were used to describe the proportion of interstudy variation caused by heterogeneity [39]. When substantial heterogeneity was detected (p < 0.1), the pooled estimates were calculated with the DerSimonian random-effect model, while the fixed-effect model was adopted in cases of no substantial heterogeneity. When applicable, sub-group analyses were performed and sensitivity analyses were adopted using the leave-one-out approach [40]. The assessment of non-reporting bias and Egger test were not performed as no analysis included more than ten studies [41]. The RevMan software version 5.3 was used for statistical analyses [42]. Statistical significance was set at p < 0.05 (two-tailed).

2.5. Risk of Bias (RoB) Assessment

The quality of the included studies was independently evaluated by two reviewers (AM and FL) with the Newcastle–Ottawa Scale [43]. The two authors resolved any discrepancies by consensus. The robvis tool was used to create RoB graphs [44].

3. Results

As displayed in the PRISMA flow-diagram (Figure 1), 421 records were screened. After removal of duplicate records, screening, and full-text assessment, six manuscripts were considered eligible for systematic review and meta-analysis [45,46,47,48,49,50].
For included studies, precise design, number of patients, follow-up duration, TETs classification, and clinical outcomes defined as events for PFS are summarized in Table 2. The cumulative number of patients was 593. All studies had a retrospective design, with the number of patients ranging from 42 to 177. One single study collected patients form two centres, while all the others were monocentric.
All [18F]FDG-PET semiquantitative measures tested as predictors for PFS are displayed in Table 3, along with the availability of HRs (95% CI) from univariate or multivariate analyses. All studies assessed the prognostic power of the SUVmax, while its SUVmean was evaluated in half of the included works [45,49,50]. MTV and TLG were tested as predictors of PFS in five studies [45,46,47,49,50], but in one of them HRs for MTV and TLG were not reported [50]. For the computation of MTV, and subsequently of TLG, as the product of MTV and SUVmean, a fixed SUV threshold of 2.5 was adopted in three studies [45,47,49], while 40% of the SUVmax was used as threshold in the other two works [48,50]. PET-based findings reported only by a single study were not included in the analysis. PET measures with respective HRs and CIs are shown in Supplementary Table S1.

3.1. Narrative Description

In 2021 Lee et al. [45] conducted a retrospective study that included 83 patients (21 low-risk thymomas (25.3%), 27 high-risk thymomas (32.5%), and 35 thymic carcinomas (42.2%)) to investigate the prognostic value of volume-based [18F]FDG-PET and clinical parameters. Recurrence or disease progression occurred in 24 patients (28.9%). On univariate analysis, higher Masaoka stage (p < 0.001), aggressive histological type (p = 0.009), treatment modality (Surgery and/or adjuvant therapy vs. non-surgical; p = 0.001), and higher SUVmax, SUVmean, MTV, and TLG (all p < 0.001) were significantly associated with shorter PFS. In addition, only SUVmean (p < 0.001) and Masaoka stage (p = 0.001) resulted as independent prognostic factors for PFS on multivariate analysis.
In the same year, Li et al. [46] investigated clinical and [18F]FDG-PET metabolic parameters in 42 treatment-naive patients with TETs. Data on tumour-infiltrating cells were also collected and analysed. Higher SUVmax, MTV, and TLG were observed in advanced Masaoka–Koga stages compared to early-stage disease, and higher SUVmax was noted in advanced-TNM-stage disease compared to early stage. Interestingly, higher SUVmax was extracted from lesions with lower CD4-positive tumour-infiltrating lymphocytes.
Han et al. [47] retrospectively enrolled a total of 186 consecutive patients with resectable TETs who underwent preoperative [18F]FDG-PET (145 thymomas, 41 thymic carcinomas). Fully Automatic Quantitative Measurement using a Convolutional Neural Network was utilized to analyse the [18F]FDG-PET. Automatically measured SUVmax, MTV, and TLG were in good agreement with manual measurements and showed good diagnostic accuracy for thymic carcinoma (AUCs: SUVmax, 0.95; MTV, 0.85; TLG, 0.87) and significant prognostic value (HRs: SUVmax, 1.31 [95% CI, 1.16–1.48]; MTV, 2.11 [95% CI, 1.09–4.06]; TLG, 1.90 [95%CI, 1.12–3.23]).
In 2024, the group of Akamine [48] retrospectively evaluated 177 patients with resectable TETs who preoperatively underwent [18F]FDG-PET. Among them, 145 (81.9%) had pathological early-stage TET (stage I or II) while 32 (19.1%) had advanced stage (stage III or IV). For the advanced stage group, lymph node (LN) metastases were preoperatively detected by [18F]FDG-PET in 30.8% of patients with a SUVmax > 5.9 of the primary tumour with subsequent confirmed pathological LN positivity, whereas LN metastases were not pathologically detected in patients with a SUVmax < 5.9 of the primary. Moreover, SUVmax > 5.6 was associated with a general worse prognosis for PFS. In addition, in patients with advanced-stage TETs, LN recurrence was significantly more frequent among those classified as N1 at preoperative [18F]FDG-PET (75.0% vs. 7.1%).
Chao et al. [49] evaluated the impact of intratumoural metabolic heterogeneity and quantitative [18F]FDG-PET imaging parameters in predicting patient outcomes in 100 patients with TETs. The univariate analysis showed that Masaoka stage, TNM stage, WHO classification, SUVmax, SUVmean, TLG, and a heterogeneity index (HI: standard deviation [SD] divided by SUVmean) were significant prognostic factors for PFS). Subsequently, multivariate analyses confirmed that HI (p < 0.001) and TNM stage (p = 0.002) were independent prognostic factors for PFS. The authors also, demonstrated that TNM stage, WHO classification, SUVmax, and HI were significant prognostic factors for overall survival (OS) in the univariate analysis, while TNM stage remained an independent prognostic factor for OS even in the multivariate analysis (p = 0.024). Specifically, the Kaplan Meier analyses showed worse prognoses for patients with TNM stages III and IV and HI ≥ 0.16 compared to those with stages I and II and HI < 0.16 (log-rank p < 0.001).
Recently, Pizzuto et al. [50] retrospectively analysed 116 patients (49/67 M/F; mean age 59.5 years) who underwent preoperative [18F]FDG-PET followed by thymectomy. In total, 27 thymic hyperplasia, 41 low-risk thymomas (types A, AB, and B1), and 48 high-risk thymomas (B2, B3 thymoma, and carcinoma) were included. SUVmax, SUVmean, SUVpeak, as well as the ratio between SUVmax of the target lesion and the liver SUVmax (rPET), quotient of SUVpeak in the tumour residual (qPET), and tumour to mediastinum ratio (T/M) were significantly higher in high-risk thymomas than low-risk and hyperplasia (p < 0.001). TLG and MTV were significantly higher in patients with low-risk thymomas (p < 0.001). Moreover, SUVmax, SUVmean, and SUVpeak cutoffs of <4.3, <2.87, and 4.03, respectively, significantly distinguished patients with longer time to recurrence (p = 0.009, p = 0.05, and p = 0.05, respectively).

3.2. Prognostic Value of SUVmax

When considered as a continuous variable, SUVmax resulted in a significant predictor of PFS (random-effect HR: 1.18, 95% CI: 1.08–1.29, p < 0.001) (Figure 2). Significant heterogeneity was detected (χ2 = 14.79, p = 0.002; I2 = 79.7%). This result was confirmed by the leave-one-out sensitivity analyses (Supplementary Figure S1), even when only considering the two studies with a low risk of bias (Supplementary Figure S2).
The analysis of the two studies assessing SUVmax as a dichotomous variable showed that the group of patients with higher values was associated with worse PFS (fixed-effect HR: 9.00, 95% CI: 2.93–27.71, p < 0.001) (Figure 3). No significant heterogeneity was detected (χ2 = 0.51, p = 0.475; I2 = 0.0%).

3.3. Prognostic Value of SUVmean

The two studies evaluating SUVmean as a continuous predictor showed a significant association between this metabolic parameter and a shorter PFS (fixed-effect HR: 1.41,95% CI: 1.25–1.59, p < 0.001) (Figure 4). No significant heterogeneity was detected (χ2 = 0.16, p 0.691; I2 = 0.0%).

3.4. Prognostic Value of MTV and TLG

Four studies evaluated both MTV and TLG as continuous predictors of PFS. These two [18F]FDG-PET-based parameters showed no significant association with PFS (p = 0.292 for MTV; p = 0.209 for TLG) (Figure 5 and Figure 6, respectively). No significant association was detected even considering only studies with a fixed SUV threshold for MTV contouring (Supplementary Figure S3).

4. Quality of the Studies

Pooled RoB data are displayed in Figure 7. The overall RoB was unclear in two studies, due to weaknesses in the Comparability and Outcome domains, while the remaining four publications showed an overall low RoB. As shown in Supplementary Figure S4, four studies had an unclear risk of bias in the Comparability domain, and two studies were unclear in the Outcome domain.

5. Discussion

Among baseline [18F]FDG-PET semiquantitative parameters in patients with TETs, the present systematic review and meta-analysis identified SUVmax and SUVmean as significant predictors of PFS, while MTV and TLG did not demonstrate a statistically significant prognostic value. The association between higher SUVmax and worse PFS was consistent across most studies, both when treated as a continuous and dichotomous variable.
These findings are in line with previous literature on other cancers [51,52,53,54,55], underscoring this metabolic parameter as a potentially simple and widely available prognostic biomarker in clinical practice. In fact, SUVmax reflects areas of highest metabolic activity, often correlating with increased cellular proliferation and hypoxia, which are known drivers of treatment resistance and poorer outcomes. However, it should be noted that only two studies assessing SUVmax as a dichotomous variable were included [48,50], thus leading to uncertainty in the results of this analysis.
Thymic carcinoma showed higher SUVmax than high-grade thymomas, which in turn presented higher [18F]FDG uptake than low-grade thymomas [29,30]. Among the included studies, one evaluated only patients with thymic carcinoma [46], another two [47,48] presented HRs for SUVmax at multivariate analysis, and the remaining three showed univariate HRs for SUVmax [45,49,50]. Thus, it is not possible to define SUVmax as a prognostic factor independent from other characteristics influencing the clinical outcome in these patients.
SUVmean was identified as an additional metabolic parameter significantly associated with poorer PFS, suggesting that overall [18F]FDG uptake, rather than only the maximum, could hold further prognostic value. Despite few previous findings highlighting the prognostic role of SUVmean in other clinical settings [56,57], in our work only two studies were assessable, and more data are necessary to confirm this finding.
From a clinical standpoint, these results suggest that patients with high baseline SUV values may require intensified treatment strategies as well as tailored follow-up programs with closer surveillance intervals. Potentially, integrating metabolic information with morphologic staging systems could help refine risk stratification and guide clinical decision-making in this rare disease setting.
Conversely, neither MTV nor TLG demonstrated significant associations with PFS. This lack of prognostic relevance disagrees with a wide literature about several solid tumours [58,59,60,61,62], and it might likely be due to some confounding factors. Firstly, these volumetric parameters are more susceptible to inter-study variability in term of segmentation methods and thresholds, which may reduce reproducibility and consistency across studies. In particular, three of the four considered studies used a fixed SUV threshold for volumetric segmentation [45,47,49], while the fourth used a variable threshold set at 40% of SUVmax [46]. Moreover, in both analyses for MTV and TLG, two studies contributed a weight of 0% to the pooled estimates, highlighting the necessity of a larger amount of evidence on this topic to obtain reliable results. Further considerations are that TETs are confined in a rigid structure with little expansion, such as the mediastinum, and that even low-risk thymoma can have a huge volume. Tumour heterogeneity, rather than volumetric parameters, could therefore correlate better with PFS. Imaged tumour heterogeneity is thought to result from regional differences in tumour cellularity, proliferation, hypoxia, angiogenesis, and necrosis, all of which are related to tumour grade [63,64]. At the same volume, aggressive tumours are often associated with intratumoural hypoxia or necrosis [65], both of which can manifest as heterogeneous tumour uptake on [18F]FDG PET, with also areas of low [18F]FDG uptake.
This study has several limitations. Firstly, no protocol was registered, which may undermine the reproducibility of this meta-analysis. Secondly, the limited number of studies did not allow subgroup analyses according to tumour type or overall risk of bias in all cases. Additionally, the small number of studies prevented a robust assessment of non-reporting bias, and the presence of publication bias cannot be excluded. Furthermore, the substantial heterogeneity observed between studies may limit the reliability of our findings. Moreover, all included studies had a retrospective design, and all except one were monocentric. Prospective, multicentric studies are necessary to clarify the prognostic role of [18F]FDG-PET in patients with TETs. The prognostic performance of PET measures was assessed across different histopathological variants, and in most cases multivariable analyses were not performed, leaving the independence of metabolic parameters as predictors of PFS unclear. Finally, baseline [18F]FDG-PET is not always performed in patients with TETs, limiting the generalizability of our findings across all clinical scenarios.
Generally, there are still several challenges in using [18F]FDG-PET semiquantitative parameter assessment for routine clinical practice that need to be addressed, such as variations between instruments and institutions, leading to lower reproducibility. Harmonization efforts should be implemented to promote greater uniformity in clinical practice. The limited literature evidence about the prognostic role of [18F]FDG-PET in TETs highlights the need of larger multi-institutional studies to confirm our preliminary findings, especially considering the rarity of these diseases [66].
Looking ahead, the progressive integration of radiomics and artificial intelligence methods may play a pivotal role in overcoming current limitations. Radiomic approaches allow for the extraction of high-dimensional quantitative features beyond conventional PET parameters, potentially uncovering novel imaging biomarkers with prognostic value. When combined with AI-based algorithms, these data could support risk stratification and personalized treatment strategies. Future multicentre, prospective validation studies should therefore incorporate radiomics and AI-driven analyses to enhance reproducibility, improve prognostic accuracy, and ultimately foster their translation into clinical practice.

6. Conclusions

Our findings support baseline [18F]FDG-PET-derived measures, such as SUVmax and SUVmean, as possible predictors of PFS in patients with TETs, suggesting their role as non-invasive biomarkers for risk stratification, identification of personalized follow-up strategies or more aggressive therapeutic approaches in this setting.
Conversely, the study results challenged the prognostic value of volumetric parameters, such as MTV and TLG, on PFS. Nevertheless, all available studies are limited by a retrospective design and single or bi-centric settings. Further prospective studies in larger multicentric samples are necessary to clearly establish the prognostic impact of [18F]FDG-PET in these patients.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/diagnostics15192458/s1, PRISMA Checklist. Table S1. Hazard ratios (HRs) and 95% confidence intervals (CIs) of [18F]FDG-PET measures as predictors of the progression-free survival (PFS) at uni- or multivariate Cox regression analyses extracted from the included studies [45,46,47,48,49,50]. Supplementary Figure S1. SUVmax (continuous variable) as a predictor of the progression-free survival (PFS) with the leave-one-out approach as a sensitivity analysis [45,46,47,49]. Figure S2. SUVmax (continuous variable) as a predictor of the progression-free survival (PFS) including only studies with low risk of bias as a sensitivity analysis [46,47]. Figure S3. MTV (continuous variable) as a predictor of the progression-free survival (PFS) including only studies with a fixed SUV threshold for contouring [45,47,49]. Figure S4. Newcastle-Ottawa Scale for risk of bias (RoB) assessment of included studies [45,46,47,48,49,50].

Author Contributions

Conceptualization: A.M. (Alberto Miceli) and F.L.; literature search, screening, and data extraction: A.M. (Alberto Miceli) and F.L.; statistical analysis: F.L.; writing—first draft: A.M. (Alberto Miceli), M.L., F.D., L.J.-L., A.M. (Antonio Mura), M.S.D.F., G.S., A.D., A.G.N., G.R. and F.L.; writing—critical review and editing: all authors. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The original contributions presented in this study are included in the article/Supplementary Materials. Further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors have no relevant financial or non-financial interests to disclose.

References

  1. WHO Classification of Tumors. Thoracic Tumors, 5th ed.; The International Agency for Research on Cancer (IARC): Lyon, France, 2021. [Google Scholar]
  2. Gatta, G.; Capocaccia, R.; Botta, L.; Mallone, S.; De Angelis, R.; Ardanaz, E.; Comber, H.; Dimitrova, N.; Leinonen, M.K.; Siesling, S.; et al. Burden and centralised treatment in Europe of rare tumours: Results of RARECAREnet—a population-based study. Lancet Oncol. 2017, 18, 1022–1039, Erratum in Lancet Oncol. 2017, 18, e433. https://doi.org/10.1016/S1470-2045(17)30531-4. [Google Scholar] [CrossRef] [PubMed]
  3. Remon, J.; Bernabé, R.; Diz, P.; Felip, E.; González-Larriba, J.L.; Lázaro, M.; Mielgo-Rubio, X.; Sánchez, A.; Sullivan, I.; Massutti, B. SEOM-GECP-GETTHI Clinical Guidelines for the treatment of patients with thymic epithelial tumours (2021). Clin. Transl. Oncol. 2022, 24, 635–645. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  4. Rich, A.L. Epidemiology of thymoma. J. Thorac. Dis. 2020, 12, 7531–7535. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  5. Mao, Z.-F.; Mo, X.-A.; Qin, C.; Lai, Y.-R.; Hackett, M.L. Incidence of thymoma in myasthenia gravis: A systematic review. J. Clin. Neurol. 2012, 8, 161–169. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  6. Dresser, L.; Wlodarski, R.; Rezania, K.; Soliven, B. Myasthenia Gravis: Epidemiology, Pathophysiology and Clinical Manifestations. J. Clin. Med. 2021, 10, 2235. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  7. Hadoux, J.; Lamarca, A.; Grande, E.; Deandreis, D.; Kaltsas, G.; Janson, E.; Tombal, B.; Pavel, M.; Thariat, J.; van Velthuysen, M.; et al. Neuroendocrine neoplasms of head and neck, genitourinary and gynaecological systems, unknown primaries, parathyroid carcinomas and intrathyroid thymic neoplasms: ESMO Clinical Practice Guideline for diagnosis, treatment and follow-up. ESMO Open 2024, 9, 103664. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  8. Zdrojowy-Wełna, A.; Bolanowski, M.; Syrycka, J.; Jawiarczyk-Przybyłowska, A.; Kuliczkowska-Płaksej, J. Case Report: Thymic neuroendocrine tumor with metastasis to the breast causing ectopic Cushing’s syndrome. Front. Oncol. 2025, 15, 1492187. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  9. Lawrence, L.; Zhang, P.; Choi, H.; Ahmad, U.; Arrossi, V.; Purysko, A.; Makin, V. A unique case of ectopic Cushing’s syndrome from a thymic neuroendocrine carcinoma. Endocrinol. Diabetes Metab. Case Rep. 2019, 2019, 19-0002. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  10. Agrafiotis, A.C.; Berzenji, L.; Koyen, S.; Vermeulen, D.; Winthagen, R.; Hendriks, J.M.H.; Van Schil, P.E. Surgical treatment of thymic epithelial tumors: A narrative review. Mediastinum 2024, 8, 32. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  11. Pavese, V.; Carfì, F.M.; Capelletto, E.; Tabbò, F.; Leo, F.; Passiglia, F.; Righi, L.; Novello, S.; Merlini, A.; Bironzo, P. Therapeutic management of patients with advanced thymic malignancies: A review for clinicians. Lung Cancer 2025, 204, 108554. [Google Scholar] [CrossRef] [PubMed]
  12. Kishi, N.; Matsuo, Y. Postoperative radiotherapy for thymic epithelial tumors: A narrative review. Mediastinum 2024, 8, 40. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  13. Yamamoto, Y.; Iwahori, K.; Shintani, Y. Current immunotherapy for thymic epithelial tumors: A narrative review. Mediastinum 2024, 8, 47. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  14. Kiesewetter, B.; Melhorn, P.; Fuereder, T. Thymic malignancies: Role of immunotherapy and novel approaches. Curr. Opin. Oncol. 2025, 37, 84–94. [Google Scholar] [CrossRef] [PubMed]
  15. Detterbeck, F.C.; Nicholson, A.G.; Kondo, K.; Van Schil, P.; Moran, C. The Masaoka-Koga stage classification for thymic malignancies: Clarification and definition of terms. J. Thorac. Oncol. 2011, 6, S1710–S1716. [Google Scholar] [CrossRef]
  16. Sorin, V.; Kirshenboim, Z.; Klug, M.; Ahuja, J.; Marom, E.M. The Ninth Edition TNM Staging Classification for Thymic Epithelial Tumors. Semin. Ultrasound CT MRI 2024, 45, 420–429. [Google Scholar] [CrossRef] [PubMed]
  17. Archer, J.M.; Ahuja, J.; Strange, C.D.; Shroff, G.S.; Gladish, G.W.; Sabloff, B.S.; Truong, M.T. Multimodality imaging of mediastinal masses and mimics. Mediastinum 2023, 7, 27. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  18. Ohno, Y.; Kishida, Y.; Seki, S.; Koyama, H.; Yui, M.; Aoyagi, K.; Yoshikawa, T. Comparison of Interobserver Agreement and Diagnostic Accuracy for IASLC/ITMIG Thymic Epithelial Tumor Staging Among Co-registered FDG-PET/MRI, Whole-body MRI, Integrated FDG-PET/CT, and Conventional Imaging Examination with and without Contrast Media Administrations. Acad. Radiol. 2022, 29 (Suppl. S3), S122–S131. [Google Scholar] [CrossRef] [PubMed]
  19. Heeger, A.P.; Ackman, J.B. Added Value of Magnetic Resonance Imaging for the Evaluation of Mediastinal Lesions. Radiol Clin. North Am. 2021, 59, 251–277. [Google Scholar] [CrossRef] [PubMed]
  20. Madan, R.; Ratanaprasatporn, L.; Ratanaprasatporn, L.; Carter, B.W.; Ackman, J.B. Cystic mediastinal masses and the role of MRI. Clin. Imaging 2018, 50, 68–77. [Google Scholar] [CrossRef] [PubMed]
  21. Girard, N.; Ruffini, E.; Marx, A.; Faivre-Finn, C.; Peters, S. ESMO Guidelines Committee. Thymic epithelial tumours: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann. Oncol. 2015, 26, v40–v55. [Google Scholar] [CrossRef]
  22. Zhu, L.; Li, X.; Wang, J.; Fu, Q.; Liu, J.; Ma, W.; Xu, W.; Chen, W. Value of metabolic parameters in distinguishing primary mediastinal lymphomas from thymic epithelial tumors. Cancer Biol. Med. 2020, 17, 468–477. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  23. Nappi, A.G.; Dondi, F.; Lazzarato, A.; Jonghi-Lavarini, L.; Gorica, J.; La Torre, F.; Santo, G.; Miceli, A. Primary Mediastinal B-Cell Lymphoma and [18F]FDG PET/CT: What We Learned and What Is New. Hematol. Rep. 2025, 17, 23. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  24. Wang, G.; Du, L.; Lu, X.; Liu, J.; Zhang, M.; Pan, Y.; Meng, X.; Xu, X.; Guan, Z.; Yang, J. Multiparameter diagnostic model based on 18F-FDG PET and clinical characteristics can differentiate thymic epithelial tumors from thymic lymphomas. BMC Cancer 2022, 22, 895. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  25. Ferdinand, B.; Gupta, P.; Kramer, E.L. Spectrum of thymic uptake at 18F-FDG PET. Radiographics 2004, 24, 1611–1616. [Google Scholar] [CrossRef] [PubMed]
  26. Jerushalmi, J.; Frenkel, A.; Bar-Shalom, R.; Khoury, J.; Israel, O. Physiologic thymic uptake of 18F-FDG in children and young adults: A PET/CT evaluation of incidence, patterns, and relationship to treatment. J. Nucl. Med. 2009, 50, 849–853. [Google Scholar] [CrossRef] [PubMed]
  27. Gao, J.; Wang, S.-Y.; Ao, Y.-Q.; Jiang, J.-H.; Lin, M.; Wang, S.; Shi, H.-C.; Ding, J.-Y. Clinical significance of positron emission tomography-computed tomography in the classification of thymic tumors. Interdiscip. Cardiovasc. Thorac. Surg. 2025, 40, ivaf065. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  28. Sung, Y.M.; Lee, K.S.; Kim, B.-T.; Choi, J.Y.; Shim, Y.M.; A Yi, C. 18F-FDG PET/CT of thymic epithelial tumors: Usefulness for distinguishing and staging tumor subgroups. J. Nucl. Med. 2006, 47, 1628–1634. [Google Scholar] [PubMed]
  29. Lococo, F.; Cesario, A.; Okami, J.; Cardillo, G.; Cavuto, S.; Tokunaga, T.; Apolone, G.; Margaritora, S.; Granone, P. Role of combined 18F-FDG-PET/CT for predicting the WHO malignancy grade of thymic epithelial tumors: A multicenter analysis. Lung Cancer 2013, 82, 245–251. [Google Scholar] [CrossRef]
  30. Treglia, G.; Sadeghi, R.; Giovanella, L.; Cafarotti, S.; Filosso, P.; Lococo, F. Is 18F-FDG PET useful in predicting the WHO grade of malignancy in thymic epithelial tumors? A meta-analysis. Lung Cancer 2014, 86, 5–13. [Google Scholar] [CrossRef] [PubMed]
  31. Chiappetta, M.; Mendogni, P.; Cattaneo, M.; Evangelista, J.; Farina, P.; Pizzuto, D.A.; Annunziata, S.; Castello, A.; Congedo, M.T.; Tabacco, D.; et al. Is PET/CT Able to Predict Histology in Thymic Epithelial Tumours? A Narrative Review. Diagnostics 2022, 13, 98. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  32. Morita, T.; Tatsumi, M.; Ishibashi, M.; Isohashi, K.; Kato, H.; Honda, O.; Shimosegawa, E.; Tomiyama, N.; Hatazawa, J. Assessment of Mediastinal Tumors Using SUVmax and Volumetric Parameters on FDG-PET/CT. Asia Ocean. J. Nucl. Med. Biol. 2017, 5, 22–29. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  33. Nakajo, M.; Takeda, A.; Katsuki, A.; Jinguji, M.; Ohmura, K.; Tani, A.; Sato, M.; Yoshiura, T. The efficacy of 18F-FDG-PET-based radiomic and deep-learning features using a machine-learning approach to predict the pathological risk subtypes of thymic epithelial tumors. Br. J. Radiol. 2022, 95, 20211050. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  34. Lee, H.S.; Oh, J.S.; Park, Y.S.; Jang, S.J.; Choi, I.S.; Ryu, J.-S. Differentiating the grades of thymic epithelial tumor malignancy using textural features of intratumoral heterogeneity via 18F-FDG PET/CT. Ann. Nucl. Med. 2016, 30, 309–319. [Google Scholar] [CrossRef] [PubMed]
  35. Wang, X.; Huang, P.; Wang, Z.; Liu, Y.; Fan, B.; Dong, W. Radiomics in thymic epithelial tumors: A scoping review of current status and advances. BMC Cancer 2025, 25, 1–8. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  36. Page, M.J.; McKenzie, J.E.; Bossuyt, P.M.; Boutron, I.; Hoffmann, T.C.; Mulrow, C.D.; Shamseer, L.; Tetzlaff, J.M.; Akl, E.A.; Brennan, S.E.; et al. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. Syst Rev. 2021, 10, 89. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  37. Ouzzani, M.; Hammady, H.; Fedorowicz, Z.; Elmagarmid, A. Rayyan—A web and mobile app for systematic reviews. Syst. Rev. 2016, 5, 210. [Google Scholar] [CrossRef]
  38. Tufanaru, C.; Munn, Z.; Stephenson, M.; Aromataris, E. Fixed or random effects meta-analysis? Common methodological issues in systematic reviews of effectiveness. JBI Evid. Implement. 2015, 13, 196–207. [Google Scholar] [CrossRef]
  39. Higgins, J.P.; Thompson, S.G.; Deeks, J.J.; Altman, D.G. Measuring inconsistency in meta-analyses. BMJ 2003, 327, 557–560. [Google Scholar] [CrossRef]
  40. Willis, B.H.; Riley, R.D. Measuring the statistical validity of summary meta-analysis and meta-regression results for use in clinical practice. Stat. Med. 2017, 36, 3283–3301. [Google Scholar] [CrossRef]
  41. Page, M.J.; Higgins, J.P.T.; Sterne, J.A.C. Chapter 13: Assessing Risk of Bias Due to Missing Evidence in a Meta-Analysis. In Cochrane Handbook for Systematic Reviews of Interventions; Higgins, J.P.T., Thomas, J., Chandler, J., Cumpston, M., Li, T., Page, M.J., Welch, V.A., Eds.; Cochrane: Hamilton, ON, Canada, 2024; version 6.5. [Google Scholar]
  42. Review Manager (RevMan) [Computer Program], Version 5.3; The Nordic Cochrane Centre: Copenhagen, Denmark; The Cochrane Collaboration: London, UK. Available online: https://revman.cochrane.org/info (accessed on 22 May 2025).
  43. Wells, G.A.; Shea, B.; O’Connell, D.; Peterson, J.; Welch, V.; Losos, M.; Tugwell, P. The Newcastle-Ottawa Scale (NOS) for Assessing the Quality of Nonrandomised Studies in Meta-Analyses. 2012. Available online: https://www.ohri.ca/programs/clinical_epidemiology/oxford.asp (accessed on 23 May 2025).
  44. McGuinness, L.A.; Higgins, J.P.T. Risk-of-bias VISualization (robvis): An R package and Shiny web app for visualizing risk-of-bias assessments. Res. Synth. Methods 2021, 12, 55–61. [Google Scholar] [CrossRef]
  45. Lee, J.; Cho, Y.S.; Kim, J.; Shim, Y.M.; Lee, K.-H.; Choi, J.Y. Prognostic Significance of Metabolic Parameters by 18F-FDG PET/CT in Thymic Epithelial Tumors. Cancers 2021, 13, 712. [Google Scholar] [CrossRef] [PubMed]
  46. Li, Y.; Li, Y.; Huang, Y.; Wu, X.; Yang, Z.; Wu, C.; Jiang, L. Usefulness of 18F-FDG PET/CT in treatment-naive patients with thymic squamous cell carcinoma. Ann. Nucl. Med. 2021, 35, 1048–1057. [Google Scholar] [CrossRef] [PubMed]
  47. Han, S.; Oh, J.S.; Kim, Y.I.; Seo, S.Y.; Lee, G.D.; Park, M.J.; Choi, S.; Kim, H.R.; Kim, Y.H.; Kim, D.K.; et al. Fully Automatic Quantitative Measurement of 18F-FDG PET/CT in Thymic Epithelial Tumors Using a Convolutional Neural Network. Clin. Nucl. Med. 2022, 47, 590–598. [Google Scholar] [CrossRef] [PubMed]
  48. Akamine, T.; Nakagawa, K.; Ito, K.; Watanabe, H.; Yotsukura, M.; Yoshida, Y.; Yatabe, Y.; Kusumoto, M.; Watanabe, S.-I. Impact of 18F-FDG PET on TNM Staging and Prognosis in Thymic Epithelial Tumors. Ann. Surg. Oncol. 2024, 31, 192–200. [Google Scholar] [CrossRef]
  49. Chao, F.; Wang, R.; Han, X.; Huang, W.; Wang, R.; Yu, Y.; Lin, X.; Yuan, P.; Yang, M.; Gao, J. Intratumoral metabolic heterogeneity by 18F-FDG PET/CT to predict prognosis for patients with thymic epithelial tumors. Thorac. Cancer 2024, 15, 1437–1445. [Google Scholar] [CrossRef]
  50. Pizzuto, D.A.; Castello, A.; Chiappetta, M.; Castellani, M.; Annunziata, S.; Campanella, A.; Calabrese, G.; Cattaneo, M.; Rosso, L.; Cusumano, G.; et al. The Role of [18F]F-FDG PET/CT for Predicting Histology and Prognosis in Patients with Thymic Lesions. Mol. Diagn. Ther. 2025, 29, 239–248. [Google Scholar] [CrossRef]
  51. Berghmans, T.; Dusart, M.; Paesmans, M.; Hossein-Foucher, C.; Buvat, I.; Castaigne, C.; Scherpereel, A.; Mascaux, C.; Moreau, M.; Roelandts, M.; et al. Primary tumor standardized uptake value (SUVmax) measured on fluorodeoxyglucose positron emission tomography (FDG-PET) is of prognostic value for survival in non-small cell lung cancer (NSCLC): A systematic review and meta-analysis (MA) by the European Lung Cancer Working Party for the IASLC Lung Cancer Staging Project. J. Thorac. Oncol. 2008, 3, 6–12. [Google Scholar] [CrossRef]
  52. Dong, M.; Liu, J.; Sun, X.; Xing, L. Prognositc significance of SUVmax on pretreatment 18F-FDG PET/CT in early-stage non-small cell lung cancer treated with stereotactic body radiotherapy: A meta-analysis. J. Med. Imaging Radiat. Oncol. 2017, 61, 652–659. [Google Scholar] [CrossRef]
  53. Wen, W.; Xu, D.; Piao, Y.; Li, X. Prognostic value of maximum standard uptake value, metabolic tumour volume, and total lesion glycolysis of 18F-FDG PET/CT in patients with malignant pleural mesothelioma: A systematic review and meta-analysis. Cancer Cell Int. 2022, 22, 60. [Google Scholar] [CrossRef]
  54. Zhu, D.; Wang, L.; Zhang, H.; Chen, J.; Wang, Y.; Byanju, S.; Liao, M. Prognostic value of 18F-FDG-PET/CT parameters in patients with pancreatic carcinoma: A systematic review and meta-analysis. Medicine 2017, 96, e7813. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  55. Li, Q.; Zhang, J.; Cheng, W.; Zhu, C.; Chen, L.; Xia, F.; Wang, M.; Yang, F.; Ma, X. Prognostic value of maximum standard uptake value, metabolic tumor volume, and total lesion glycolysis of positron emission tomography/computed tomography in patients with nasopharyngeal carcinoma: A systematic review and meta-analysis. Medicine 2017, 96, e8084. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  56. Tosi, D.; Pieropan, S.; Cattoni, M.; Bonitta, G.; Franzi, S.; Mendogni, P.; Imperatori, A.; Rotolo, N.; Castellani, M.; Cuzzocrea, M.; et al. Prognostic Value of 18F-FDG PET/CT Metabolic Parameters in Surgically Treated Stage I Lung Adenocarcinoma Patients. Clin. Nucl. Med. 2021, 46, 621–626. [Google Scholar] [CrossRef]
  57. Amrane, K.; Thuillier, P.; Bourhis, D.; Le Meur, C.; Quere, C.; Leclere, J.C.; Ferec, M.; Jestin-Le Tallec, V.; Doucet, L.; Alemany, P.; et al. Prognostic value of pre-therapeutic FDG-PET radiomic analysis in gastro-esophageal junction cancer. Sci. Rep. 2023, 13, 5789, Erratum in Sci. Rep. 2023, 13, 6665. https://doi.org/10.1038/s41598-023-33877-7. [Google Scholar] [CrossRef]
  58. Choi, H.J.; Lee, J.W.; Kang, B.; Song, S.Y.; Lee, J.D.; Lee, J.-H. Prognostic significance of volume-based FDG PET/CT parameters in patients with locally advanced pancreatic cancer treated with chemoradiation therapy. Yonsei Med. J. 2014, 55, 1498–1506. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  59. Dholakia, A.S.; Chaudhry, M.; Leal, J.P.; Chang, D.T.; Raman, S.P.; Hacker-Prietz, A.; Su, Z.; Pai, J.; Oteiza, K.E.; Griffith, M.E.; et al. Baseline metabolic tumor volume and total lesion glycolysis are associated with survival outcomes in patients with locally advanced pancreatic cancer receiving stereotactic body radiation therapy. Int. J. Radiat. Oncol. Biol. Phys. 2014, 89, 539–546. [Google Scholar] [CrossRef]
  60. Pak, K.; Cheon, G.J.; Nam, H.-Y.; Kim, S.-J.; Kang, K.W.; Chung, J.-K.; Kim, E.E.; Lee, D.S. Prognostic value of metabolic tumor volume and total lesion glycolysis in head and neck cancer: A systematic review and meta-analysis. J. Nucl. Med. 2014, 55, 884–890. [Google Scholar] [CrossRef] [PubMed]
  61. Bauckneht, M.; Rebuzzi, S.E.; Signori, A.; Donegani, M.I.; Murianni, V.; Miceli, A.; Borea, R.; Raffa, S.; Damassi, A.; Ponzano, M.; et al. The Prognostic Role of Baseline Metabolic Tumor Burden and Systemic Inflammation Biomarkers in Metastatic Castration-Resistant Prostate Cancer Patients Treated with Radium-223: A Proof of Concept Study. Cancers 2020, 12, 3213. [Google Scholar] [CrossRef] [PubMed]
  62. Agarwal, A.; Wehrle, C.J.; Satish, S.; Mahajan, P.; Kamath, S.; Koyfman, S.; Ma, W.W.; Linganna, M.; Esfeh, J.M.; Miller, C.; et al. PET-Assessed Metabolic Tumor Volume Across the Spectrum of Solid-Organ Malignancies: A Review of the Literature. Biomedicines 2025, 13, 123. [Google Scholar] [CrossRef]
  63. Cook, G.J.; Yip, C.; Siddique, M.; Goh, V.; Chicklore, S.; Roy, A.; Marsden, P.; Ahmad, S.; Landau, D. Are pretreatment 18F-FDG PET tumor textural features in non-small cell lung cancer associated with response and survival after chemoradiotherapy? J. Nucl. Med. 2013, 54, 19–26. [Google Scholar] [CrossRef] [PubMed]
  64. Ganeshan, B.; Goh, V.; Mandeville, H.C.; Ng, Q.S.; Hoskin, P.J.; Miles, K.A. Non-small cell lung cancer: Histopathologic correlates for texture parameters at CT. Radiology 2013, 266, 326–336. [Google Scholar] [CrossRef] [PubMed]
  65. Höckel, M.; Vaupel, P. Tumor hypoxia: Definitions and current clinical, biologic, and molecular aspects. J. Natl. Cancer Inst. 2001, 93, 266–276. [Google Scholar] [CrossRef] [PubMed]
  66. Nakagawa, K. Practical value of fluorodeoxyglucose positron emission tomography in treatment strategies for thymic epithelial tumors: Implications for more specific use in routine clinical practice. Mediastinum 2025, 9, 7. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
Diagnostics 15 02458 g001
Figure 1. PRISMA flow-diagram.
Figure 1. PRISMA flow-diagram.
Diagnostics 15 02458 g001
Diagnostics 15 02458 g002
Figure 2. SUVmax (continuous variable) as a predictor of PFS [45,46,47,49].
Figure 2. SUVmax (continuous variable) as a predictor of PFS [45,46,47,49].
Diagnostics 15 02458 g002
Diagnostics 15 02458 g003
Figure 3. SUVmax (dichotomous variable) as a predictor of PFS [48,50].
Figure 3. SUVmax (dichotomous variable) as a predictor of PFS [48,50].
Diagnostics 15 02458 g003
Diagnostics 15 02458 g004
Figure 4. SUVmean (continuous variable) as a predictor of PFS [45,49].
Figure 4. SUVmean (continuous variable) as a predictor of PFS [45,49].
Diagnostics 15 02458 g004
Diagnostics 15 02458 g005
Figure 5. MTV (continuous variable) as a predictor of PFS [45,46,47,49].
Figure 5. MTV (continuous variable) as a predictor of PFS [45,46,47,49].
Diagnostics 15 02458 g005
Diagnostics 15 02458 g006
Figure 6. TLG (continuous variable) as a predictor of PFS [45,46,47,49].
Figure 6. TLG (continuous variable) as a predictor of PFS [45,46,47,49].
Diagnostics 15 02458 g006
Diagnostics 15 02458 g007
Figure 7. Pooled risk of bias of included studies.
Figure 7. Pooled risk of bias of included studies.
Diagnostics 15 02458 g007
Table 1. Literature search strategy.
Table 1. Literature search strategy.
DatabaseResearch String
PubMed((thymoma) OR ((thymic) AND (cancer OR carcinoma OR adenocarcinoma OR neoplasia OR neoplasm OR tumour OR tumour)) AND ((positron emission tomography OR pet) AND (fdg OR fluorodeoxyglucose)) AND (survival OR progression OR recurrence OR relapse OR prognosis OR prognostic))
Embase((‘thymoma’) OR ((‘thymic’) AND (‘cancer’ OR ‘carcinoma’ OR ‘adenocarcinoma’ OR ‘neoplasia’ OR ‘neoplasm’ OR ‘tumour’ OR ‘tumour’)) AND ((‘positron emission tomography’ OR ‘pet’) AND (‘fdg’ OR ‘fluorodeoxyglucose’)) AND (‘survival’ OR ‘progression’ OR ‘recurrence’ OR ‘relapse’ OR ‘prognosis’ OR ‘prognostic’))
Scopus((thymoma) OR ((thymic) AND (cancer OR carcinoma OR adenocarcinoma OR neoplasia OR neoplasm OR tumour OR tumour)) AND ((“positron emission tomography” OR pet) AND (fdg OR fluorodeoxyglucose)) AND (survival OR progression OR recurrence OR relapse OR prognosis OR prognostic))
Table 2. Characteristics of the included studies.
Table 2. Characteristics of the included studies.
Author, YearStudy DesignEnrolled PatientsFollow-Up (Months)TETs ClassificationEvent for PFS
Lee et al.
2021 [45]		Retrospective, monocentric83Mean: 28.6
SD: 22.2
Range: 0.0–79.0		Thymoma
Carcinoma		Disease recurrence, progression,
or death
Li et al.
2021 [46]		Retrospective, monocentric42Mean: 21
SD: 12
Range: 7–60		CarcinomaDisease progression
Han et al., 2022 [47]Retrospective, monocentric114Median: 39
IQR: 25–58		Thymoma
Carcinoma		Disease recurrence
Akamine et al., 2024 [48]Retrospective, monocentric177Median: 35
IQR: 12–59		Thymoma
Carcinoma
Neuroendocrine neoplasm		Disease recurrence, progression, or
death
Chao et al., 2024 [49]Retrospective, monocentric100Mean: 25.7
SD: 19.8
Range 1–97		Thymoma
Carcinoma		Disease recurrence,
progression, or
death
Pizzuto et al., 2025 [50]Retrospective, bi-centric77Median: 38
Range 14–72		Hyperplasia
Thymoma
Carcinoma		Disease recurrence
Table 3. [18F]FDG-PET parameters reported in the included studies.
Table 3. [18F]FDG-PET parameters reported in the included studies.
Author, Year[18F]FDG-PET ParameterVariable TypeVariable Computation
Lee et al., 2021 [45]SUVmaxContinuous
SUVmeanContinuous
MTVContinuousFixed SUV threshold of 2.5
TLGContinuous
Li et al., 2021 [46]SUVmaxContinuous
Primary tumour MTVContinuousThreshold at 40% of SUVmax
Primary tumour TLGContinuous
Metastases MTVContinuousThreshold at 40% of SUVmax
Metastases TLGContinuous
Han et al., 2022 [47]SUVmaxContinuous
MTVContinuousFixed SUV threshold of 2.5
TLGContinuous
SUVmaxContinuousAutomatic segmentation
MTVContinuousAutomatic segmentation
Fixed SUV threshold of 2.5
TLGContinuousAutomatic segmentation
Akamine et al., 2024 [48]SUVmaxBinarized
Chao et al., 2024 [49]SUVmaxContinuous
SUVmeanContinuous
MTVContinuousFixed SUV threshold of 2.5
TLGContinuous
Heterogeneity index-1Continuous
Heterogeneity index-2Continuous
Pizzuto et al., 2025 [50]SUVmaxBinarized
SUVmeanBinarized
SUVpeakBinarized
MTVBinarizedThreshold at 40% of SUVmax
TLGBinarized
rPETBinarized
qPETBinarized
T/MBinarized
Legend: Standardized uptake value (SUV), metabolic tumour volume (MTV), total lesion glycolis (TLG), Positron emission tomography (PET).
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Miceli, A.; Librando, M.; Dondi, F.; Jonghi-Lavarini, L.; D’Antonio, A.; Mura, A.; Nappi, A.G.; Rovera, G.; De Feo, M.S.; Santo, G.; et al. The Value of Baseline [18F]FDG-PET in Predicting the Progression-Free Survival in Patients with Thymic Epithelial Tumours: A Systematic Review and Meta-Analysis. Diagnostics 2025, 15, 2458. https://doi.org/10.3390/diagnostics15192458

AMA Style

Miceli A, Librando M, Dondi F, Jonghi-Lavarini L, D’Antonio A, Mura A, Nappi AG, Rovera G, De Feo MS, Santo G, et al. The Value of Baseline [18F]FDG-PET in Predicting the Progression-Free Survival in Patients with Thymic Epithelial Tumours: A Systematic Review and Meta-Analysis. Diagnostics. 2025; 15(19):2458. https://doi.org/10.3390/diagnostics15192458

Chicago/Turabian Style

Miceli, Alberto, Maria Librando, Francesco Dondi, Lorenzo Jonghi-Lavarini, Adriana D’Antonio, Antonio Mura, Anna Giulia Nappi, Guido Rovera, Maria Silvia De Feo, Giulia Santo, and et al. 2025. "The Value of Baseline [18F]FDG-PET in Predicting the Progression-Free Survival in Patients with Thymic Epithelial Tumours: A Systematic Review and Meta-Analysis" Diagnostics 15, no. 19: 2458. https://doi.org/10.3390/diagnostics15192458

APA Style

Miceli, A., Librando, M., Dondi, F., Jonghi-Lavarini, L., D’Antonio, A., Mura, A., Nappi, A. G., Rovera, G., De Feo, M. S., Santo, G., & Lanfranchi, F. (2025). The Value of Baseline [18F]FDG-PET in Predicting the Progression-Free Survival in Patients with Thymic Epithelial Tumours: A Systematic Review and Meta-Analysis. Diagnostics, 15(19), 2458. https://doi.org/10.3390/diagnostics15192458

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop