Can 18F-FDG PET predict the grade of malignancy in thymic epithelial tumors? An evaluation of only resected tumors

Distinguishing thymic cysts from low-risk thymomas via [18F]FDG PET/CT

BACKGROUND

Thymic cysts are a rare benign disease that needs to be distinguished from low-risk thymoma. [18F]fluorodeoxyglucose (FDG) positron emission tomography (PET)/computed tomography (CT) is a non-invasive imaging technique used in the differential diagnosis of thymic epithelial tumours, but its usefulness for thymic cysts remains unclear. Our study evaluated the utility of visual findings and quantitative parameters of [18F]FDG PET/CT for differentiating between thymic cysts and low-risk thymomas.

METHODS

Patients who underwent preoperative [18F]FDG PET/CT followed by thymectomy for a thymic mass were retrospectively analyzed. The visual [18F]FDG PET/CT findings evaluated were PET visual grade, PET central metabolic defect, and CT shape. The quantitative [18F]FDG PET/CT parameters evaluated were PET maximum standardized uptake value (SUVmax), CT diameter (cm), and CT attenuation in Hounsfield units (HU). Findings and parameters for differentiating thymic cysts from low-risk thymomas were assessed using Pearson's chi-square test, the Mann-Whitney U-test, and receiver operating characteristics (ROC) curve analysis.

RESULTS

Seventy patients (18 thymic cysts and 52 low-risk thymomas) were finally included. Visual findings of PET visual grade (P < 0.001) and PET central metabolic defect (P < 0.001) showed significant differences between thymic cysts and low-risk thymomas, but CT shape did not. Among the quantitative parameters, PET SUVmax (P < 0.001), CT diameter (P < 0.001), and CT HU (P = 0.004) showed significant differences. In ROC analysis, PET SUVmax demonstrated the highest area under the curve (AUC) of 0.996 (P < 0.001), with a cut-off of equal to or less than 2.1 having a sensitivity of 100.0% and specificity of 94.2%. The AUC of PET SUVmax was significantly larger than that of CT diameter (P = 0.009) and CT HU (P = 0.004).

CONCLUSIONS

Among the [18F]FDG PET/CT parameters examined, low FDG uptake (SUVmax ≤ 2.1, equal to or less than the mediastinum) is a strong diagnostic marker for a thymic cyst. PET visual grade and central metabolic defect are easily accessible findings.

Impact of 18F-FDG PET on TNM Staging and Prognosis in Thymic Epithelial Tumors

BACKGROUND

Preoperative fluorine-18-fluorodeoxyglucose positron emission tomography (18F-FDG PET) of thymic epithelial tumors (TETs) is well known for identifying malignant-grade TETs; however, its predictive power for determining locally advanced tumors, lymph node (LN) metastasis, and prognosis remains unknown.

PATIENTS AND METHODS

We retrospectively evaluated patients with resectable TETs who were preoperatively assessed using 18F-FDG PET from January 2012 to January 2023. The receiver operating characteristic curve was used to evaluate the cutoff value of the maximum standardized uptake value (SUVmax) to predict advanced-stage disease. Recurrence/progression-free survival (RFS/PFS) was analyzed using the Kaplan-Meier method. The staging was classified according to the tumor-node-metastasis system.

RESULTS

Our study included 177 patients; 145 (81.9%) had pathological early-stage TET (stage I or II), and 32 (19.1%) had advanced stage (stage III or IV). The area under the curve value for predicting the advanced stage was 0.903, and the cutoff value was 5.6 (sensitivity 81.3%, specificity 84.8%). SUVmax > 5.6 was associated with worse prognosis for RFS/PFS. LN metastasis was preoperatively detected by FDG uptake in 30.8% of patients with pathological LN positivity, whereas LN metastasis was not pathologically detected in patients with SUVmax < 5.9. In patients with advanced-stage TETs, LN recurrence was more frequent in patients who were preoperatively detected by 18F-FDG PET than those who were not (75.0% versus 7.1%).

CONCLUSIONS

18F-FDG PET is a potentially valuable tool for predicting advanced stage and poor prognosis of recurrence in patients with TETs. SUVmax can help thoracic surgeons to guide them in selecting appropriate therapeutic strategies for TETs.

Differentiating thymic epithelial tumors from mediastinal lymphomas: preoperative nomograms based on PET/CT radiomic features to minimize unnecessary anterior mediastinal surgery

PURPOSE

Clinical feasibility nomograms were developed to facilitate the differentiation between thymic epithelial tumors (TETs) and mediastinal lymphomas (MLs), aiming to minimize the occurrence of non-therapeutic thymectomy.

METHODS

A total of 255 patients diagnosed with TETs or MLs underwent pre-treatment 18F-FDG PET/CT. Comprehensive clinical and imaging data were collected, including age, gender, lactate dehydrogenase (LDH) level, pathological results, presence of myasthenia gravis symptoms, B symptoms, mass size, location, morphology, margins, density, and metabolic parameters derived from PET/CT. Radiomic features were extracted from the region of interest (ROI) of the primary lesion. Feature selection techniques were employed to identify the most discriminative subset of features. Machine learning methods were utilized to build candidate models, which were subsequently evaluated based on their area under the curve (AUC). Finally, nomograms were constructed using the optimal model to provide a clinical tool for improved diagnostic accuracy. The performance of the radiomic models was evaluated by their calibration, discrimination, and clinical utility.

RESULTS

Several independent risk factors were identified for distinguishing TETs from MLs, including average standardized uptake value (SUVavg), LDH, age, mass size, and radiomic score (rad-score). Significance was observed in differentiating the two types of tumors based on these factors. The best clinical efficacy was demonstrated by the combined model, with an impressive AUC of 0.954. Decision curve analysis and calibration curves indicated that the combined model was clinically advantageous for discriminating TETs from MLs. Besides, the results of external validation showed a sensitivity of 0.8 and a specificity of 0.78.

CONCLUSION

Preoperatively, the differentiation of TETs from MLs can be facilitated by the utilization of the combined clinical information and radiomics model. This approach holds promise in minimizing the occurrence of unnecessary anterior mediastinal surgeries.

Imaging evaluation of thymic tumors

An integral part of managing patients with thymoma and thymic carcinoma is imaging. At diagnosis and staging, imaging helps demonstrate the extent of local invasion and distant metastases which allows the proper stratification of patients for therapy. For decades, the predominant staging system for thymic tumors was the Masaoka-Koga staging system. More recently, however, the International Association for the Study of Lung Cancer, the International Thymic Malignancies Interest Group (ITMIG), the European Society of Thoracic Surgeons, the Chinese Alliance for Research on Thymomas, and the Japanese Association of Research on Thymus partnered together to develop a tumor-node-metastasis (TNM) staging system specifically for thymic tumors based on a retrospective database of nearly 10,000 patients. The TNM 8th edition defines specific criteria for thymic tumors. Imaging also serves to assess treatment response and detect recurrent disease after various treatment modalities. The Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1 is currently used to assess response to treatment. ITMIG recommends certain modifications to RECIST version 1.1, however, in thymic tumors due to unique patterns of spread. While there is often overlap, computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography/computed tomography (PET/CT) characteristics can help differentiate thymoma and thymic carcinoma, with newer CT and MRI techniques under evaluation showing encouraging potential.

Characterization of Mediastinal Bulky Lymphomas with FDG-PET-Based Radiomics and Machine Learning Techniques

BACKGROUND

This study tested the diagnostic value of 18F-FDG PET/CT (FDG-PET) volumetric and texture parameters in the histological differentiation of mediastinal bulky disease due to classical Hodgkin lymphoma (cHL), primary mediastinal B-cell lymphoma (PMBCL) and grey zone lymphoma (GZL), using machine learning techniques.

METHODS

We reviewed 80 cHL, 29 PMBCL and 8 GZL adult patients with mediastinal bulky disease and histopathological diagnoses who underwent FDG-PET pre-treatment. Volumetric and radiomic parameters were measured using FDG-PET both for bulky lesions (BL) and for all lesions (AL) using LIFEx software (threshold SUV ≥ 2.5). Binary and multiclass classifications were performed with various machine learning techniques fed by a relevant subset of radiomic features.

RESULTS

The analysis showed significant differences between the lymphoma groups in terms of SUVmax, SUVmean, MTV, TLG and several textural features of both first- and second-order grey level. Among machine learning classifiers, the tree-based ensembles achieved the best performance both for binary and multiclass classifications in histological differentiation.

CONCLUSIONS

Our results support the value of metabolic heterogeneity as an imaging biomarker, and the use of radiomic features for early characterization of mediastinal bulky lymphoma.

PET-CT in Clinical Adult Oncology: II. Primary Thoracic and Breast Malignancies

Simple Summary

Positron emission tomography (PET), typically combined with computed tomography (CT), has become a critical advanced imaging technique in oncology. With PET-CT, a radioactive molecule (radiotracer) is injected in the bloodstream and localizes to sites of tumor because of specific cellular features of the tumor that accumulate the targeting radiotracer. The CT scan, performed at the same time, provides information to facilitate assessment of the amount of radioactivity from deep or dense structures, and to provide detailed anatomic information. PET-CT has a variety of applications in oncology, including staging, therapeutic response assessment, restaging, and surveillance. This series of six review articles provides an overview of the value, applications, and imaging and interpretive strategies of PET-CT in the more common adult malignancies. The second article in this series addresses the use of PET-CT in breast cancer and other primary thoracic malignancies.

Abstract

Positron emission tomography combined with x-ray computed tomography (PET-CT) is an advanced imaging modality with oncologic applications that include staging, therapy assessment, restaging, and surveillance. This six-part series of review articles provides practical information to providers and imaging professionals regarding the best use of PET-CT for the more common adult malignancies. The second article of this series addresses primary thoracic malignancy and breast cancer. For primary thoracic malignancy, the focus will be on lung cancer, malignant pleural mesothelioma, thymoma, and thymic carcinoma, with an emphasis on the use of FDG PET-CT. For breast cancer, the various histologic subtypes will be addressed, and will include ¹⁸F fluorodeoxyglucose (FDG), recently Food and Drug Administration (FDA)-approved ¹⁸F-fluoroestradiol (FES), and ¹⁸F sodium fluoride (NaF). The pitfalls and nuances of PET-CT in breast and primary thoracic malignancies and the imaging features that distinguish between subcategories of these tumors are addressed. This review will serve as a resource for the appropriate roles and limitations of PET-CT in the clinical management of patients with breast and primary thoracic malignancies for healthcare professionals caring for adult patients with these cancers. It also serves as a practical guide for imaging providers, including radiologists, nuclear medicine physicians, and their trainees.

Computed Tomography-Based Radiomics for Differentiation of Thymic Epithelial Tumors and Lymphomas in Anterior Mediastinum

Objective

To investigate the differential diagnostic performance of computed tomography (CT)-based radiomics in thymic epithelial tumors (TETs) and lymphomas in anterior mediastinum.

Methods

There were 149 patients with TETs and 93 patients with lymphomas enrolled. These patients were assigned to a training set (n = 171) and an external validation set (n = 71). Dedicated radiomics prototype software was used to segment lesions on preoperative chest enhanced CT images and extract features. The multivariable logistic regression algorithm was used to construct three models according to clinico-radiologic features, radiomics features, and combined features, respectively. Performance of the three models was compared by using the area under the receiver operating characteristic curves (AUCs). Decision curve analysis was used to evaluate clinical utility of the three models.

Results

For clinico-radiologic model, radiomics signature model, and combined model, the AUCs were 0.860, 0.965, 0.975 and 0.843, 0.961, 0.955 in the training cohort and the test cohort, respectively (all P<0.05). The accuracies of each model were 0.836, 0.895, 0.918 and 0.845, 0.901, 0.859 in the two cohorts, respectively (all P<0.05). Compared with the clinico-radiologic model, better diagnostic performances were found in the radiomics signature model and the combined model.

Conclusions

Radiomics signature model and combined model exhibit outstanding and comparable differential diagnostic performances between TETs and lymphomas. The CT-based radiomics analysis might serve as an effective tool for accurately differentiating TETs from lymphomas before treatment.

Combined clinical and specific positron emission tomography/computed tomography-based radiomic features and machine-learning model in prediction of thymoma risk groups

OBJECTIVES

In this single-center study, we aimed to propose a machine-learning model and assess its ability with clinical data to classify low- and high-risk thymoma on fluorine-18 (18F) fluorodeoxyglucose (FDG) (18F-FDG) PET/computed tomography (CT) images.

METHODS

Twenty-seven patients (14 male, 13 female; mean age: 49.6 ± 10.2 years) who underwent PET/CT to evaluate the suspected anterior mediastinal mass and histopathologically diagnosed with thymoma were included. On 18F-FDG PET/CT images, the anterior mediastinal tumor was segmented. Standardized uptake value (SUV)max, SUVmean, SUVpeak, MTV and total lesion glycolysis of primary mediastinal lesions were calculated. For texture analysis first, second, and higher-order texture features were calculated. Clinical information includes gender, age, myasthenia gravis status; serum levels of lactate dehydrogenase (LDH), alkaline phosphatase, C-reactive protein, hemoglobin, white blood cell, lymphocyte and platelet counts were included in the analysis.

RESULTS

Histopathologic examination was consistent with low risk and high-risk thymoma in 15 cases and 12 cases, respectively. The age and myasthenic syndrome were statistically significant in both groups (P = 0.039 and P = 0.05, respectively). The serum LDH level was also statistically significant in both groups (450.86 ± 487.07 vs. 204.82 ± 59.04; P < 0.001). The highest AUC has been achieved with MLP Classifier (ANN) machine learning method, with a range of 0.830 then the other learning classifiers. Three features were identified to differentiate low- and high-risk thymoma for the machine learning, namely; myasthenia gravis, LDH, SHAPE_Sphericity [only for 3D ROI (nz>1)].

CONCLUSIONS

This small dataset study has proposed a machine-learning model by MLP Classifier (ANN) analysis on 18F-FDG PET/CT images, which can predict low risk and high-risk thymoma. This study also demonstrated that the combination of clinical data and specific PET/CT-based radiomic features with image variables can predict thymoma risk groups. However, these results should be supported by studies with larger dataset.

Imaging Evaluation of Thymoma and Thymic Carcinoma

Imaging is integral in the management of patients with thymoma and thymic carcinoma. At initial diagnosis and staging, imaging provides the clinical extent of local invasion as well as distant metastases to stratify patients for therapy and to determine prognosis. Following various modalities of therapy, imaging serves to assess treatment response and detect recurrent disease. While imaging findings overlap, a variety of CT, MRI, and PET/CT characteristics can help differentiate thymoma and thymic carcinoma, with new CT and MRI techniques currently under evaluation showing potential.

Diagnostic Test Accuracy of 18F-FDG PET or PET/CT for Characterization of Histologic Type of Thymic Epithelial Tumor: A Meta-analysis

PURPOSE

This study investigated diagnostic accuracies of 18F-FDG PET or PET/CT for characterization of histologic type of thymic epithelial tumors (TETs) through a systematic review and meta-analysis.

PATIENTS AND METHODS

The PubMed, Cochrane database, and EMBASE database, from the earliest available date of indexing through August 31, 2020, were searched for studies evaluating diagnostic performance of 18F-FDG PET or PET/CT for characterization of TET. We determined the sensitivities and specificities, calculated positive and negative likelihood ratios (LR+ and LR-), and constructed summary receiver operating characteristic curves.

RESULTS

The pooled sensitivity of 18F-FDG PET or PET/CT was 0.89 (95% confidence interval [CI], 0.80-0.95), and the pooled specificity was 0.77 (95% CI, 0.63-0.87) for differentiation between thymic cancer and thymoma. Likelihood ratio syntheses gave an overall positive likelihood ratio (LR+) of 3.9 and negative likelihood ratio (LR-) of 0.14. The pooled diagnostic odds ratio was 28 (95% CI, 13-63). The pooled sensitivity was 0.90 (95% CI, 0.75-0.96), and the pooled specificity was 0.81 (95% CI, 0.68-0.89) for differential diagnosis of a low-risk or high-risk TET. LR+ was 4.7 and LR- was 0.12. The pooled diagnostic odds ratio was 38 (95% CI, 12-121). In meta-regression analysis, no variable was the source of the study heterogeneity.

CONCLUSIONS

18F-FDG PET or PET/CT has excellent diagnostic performances for characterization of TET. Further large multicenter studies would be necessary to establish the diagnostic accuracy of 18F-FDG PET or PET/CT for differentiation of histologic type of TET.

18F-FDG PET/CT Evaluation of Thymomas: a Pictorial Review

The World Health Organization classification divides thymomas according to morphology, epithelial component, and cell atypia. They are grouped into 3 large subgroups: low-risk thymomas (types A, AB, and B1), high-risk thymomas (types B2 and B3), and thymic carcinomas. Tumor subtype represents an independent prognostic factor, which determines therapeutic decision. All thymomas show some degree of ¹⁸F-FDG uptake, which tends to increase with the grade of malignancy; this is related to glucose transporter 1 (GLUT1) expression. This review collects all types of thymomas with illustrative images and provides a guide to get familiar with histological characteristics of the lesions and have them in mind because, even imaging findings can overlap among subtypes, certain characteristics can be combined to make an accurate diagnosis based on ¹⁸F-FDG PET-CT findings.

The usefulness of fluorodeoxyglucose-positron emission tomography as a preoperative diagnostic tool for thymic epithelial tumors

Background

To investigate the usefulness of ¹⁸F-fluorodeoxy glucose-positron emission tomography (¹⁸F-FDG PET) for the preoperative imaging diagnosis of malignant grade in thymic epithelial tumors (TETs) and the correlation between the maximum standardized uptake value (SUVmax) and tumor size in TETs.

Methods

We retrospectively investigated 51 patients with TETs performed ¹⁸F-FDG PET. The SUVmax was compared between thymic carcinomas and thymomas. We also evaluated the difference in the SUVmax limited to small TETs. In addition, the correlation between the SUVmax and the tumor size was evaluated.

Results

The mean SUVmax of thymic carcinomas (n=12) and thymomas (n=39) was 5.71±2.6 and 3.08±1.4, respectively. The SUVmax of thymic carcinomas was significantly higher than that of thymomas (P<0.001). The mean SUVmax of these small thymic carcinomas (n=3) and thymomas (n=13) was 2.97±0.24 and 1.79±0.47, respectively. The SUVmax of the small thymic carcinomas was significantly higher than that of the thymomas (P=0.001). We found a positive correlation between the SUVmax and the maximum tumor size of TETs (correlation coefficient: 0.632, P<0.001).

Conclusions

¹⁸F-FDG PET might be useful for evaluating the preoperative malignancy of TETs. Of note, the maximum tumor size should be considered when performing assessments by ¹⁸F-FDG PET.

Thymic Epithelial Neoplasms: Radiologic-Pathologic Correlation

Thymic epithelial neoplasms, as classified by the World Health Organization, include thymoma, thymic carcinoma, and thymic carcinoid. They are a rare group of tumors and are often diagnosed incidentally in the work-up of parathymic syndrome, such as myasthenia gravis, or when mass effect or local invasion causes other symptoms. In each of these scenarios, understanding the radiologic-pathologic relationship of these tumors allows clinical imagers to contribute meaningfully to management decisions and overall patient care. Integrating important imaging features, such as local invasion, and pathologic features, such as necrosis and immunohistochemistry, ensures a meaningful contribution by clinical imagers to the care team.

Value of 18F-FDG PET/computed tomography in predicting the simplified WHO grade of malignancy in thymic epithelial tumors

OBJECTIVE

To investigate the value of 18F-fluorodeoxyglucose (F-FDG) PET/computed tomography (CT) in predicting the simplified WHO grade of malignancy in thymic epithelial tumors.

MATERIALS AND METHODS

We retrospectively reviewed 81 patients with pathologically proven thymic epithelial tumors who underwent F-FDG PET/CT before surgical resection. Maximum standardized uptake value (SUVmax) and SUVmax/tumor size were measured on the primary lesion. A receiver operating characteristics (ROC) curve were performed for assessing the ability of F-FDG PET/CT as a predictor of the simplified WHO classification.

RESULTS

There were 43 male patients (53.1%) and 38 female patients (46.9%), and the mean age was 55.6 ± 11.9 years. The mean tumor size was 53.2 ± 21.4 mm. There were 24 low-risk thymomas (29.6%) (A, AB, and B1), 29 high-risk thymomas (35.8%) (B2 and B3), and 28 thymic carcinomas (34.6%). The SUVmax and SUVmax/tumor size were found to be predictive factors that were useful to distinguish thymomas and thymic carcinomas, and area under the ROC curve were 0.820 and 0.691, respectively (P < 0.05), and the cutoff value for discriminating thymomas and thymic carcinomas was 5.34.

CONCLUSION

In conclusion, a significant relationship was observed between SUVmax, SUVmax/tumor size and histological WHO classification of thymic epithelial tumors. F-FDG PET/CT may be useful for predicting the grade of malignancy in thymic epithelial.

18F-FDG-PET/CT predicts grade of malignancy and invasive potential of thymic epithelial tumors

PURPOSE

The aim of this study was to evaluate the ability of fluorine-18-fluorodeoxyglucose positron emission tomography coupled with computed tomography (18F-FDG-PET/CT) to predict the WHO malignancy grade, initial staging, and invasive potential of thymic epithelial tumors.

METHODS

We retrospectively reviewed the medical records of 56 patients with thymic epithelial tumors who were evaluated by PET/CT before surgery and underwent surgical resection. We analyzed the relationship of the maximum standardized uptake value (SUVmax) with the WHO histological classification, tumor invasion, TNM classification, and the Masaoka-Koga classification.

RESULTS

There were differences of SUVmax of the FDG-PET between thymic carcinoma (9.09 ± 3.34) and thymoma (4.86 ± 2.45; p < 0.01), thymic carcinoma (9.09 ± 3.34) and high-grade thymoma (6.01 ± 2.78; p < 0.01), and high-grade thymoma (6.01 ± 2.78) and low-grade thymoma (4.06 ± 1.86; p < 0.01). The cut-off value for the SUVmax was 7.40 and 5.40, and the sensitivity/specificity for predicting the histologic subtype of each group was 0.72/0.79 and 0.61/0.85, respectively. According to T classification, SUVmax was significantly higher in T3 (8.31 ± 2.57) than in T1a (4.45 ± 2.06; p < 0.01). Regarding Masaoka-Koga classification and WHO histological classification, a significantly higher SUVmax was detected in patients with stage III and IV disease than in those with stage I and II diseases (p < 0.01). The cut-off value for SUVmax was 5.40 in Masaoka-Koga stage and 5.60 in the WHO classification; the sensitivity/specificity for predicting the histologic subtype was 0.85/0.80 and 0.89/0.78, respectively.

CONCLUSIONS

FDG-PET is a useful tool to predict aggressiveness of thymic epithelial tumors.

Value of metabolic parameters in distinguishing primary mediastinal lymphomas from thymic epithelial tumors

Objective: A high rate of unnecessary thymectomies has been reported. This study aimed to distinguish primary mediastinal lymphomas (PMLs) from thymic epithelial tumors (TETs) by evaluating volumetric and metabolic parameters with ¹⁸F-FDG PET/CT.

Methods: A total of 136 patients who were pathologically diagnosed with TETs or PMLs were enrolled, and ¹⁸F-FDG PET/CT was performed before therapy. Volumetric parameters, including the mean SUV (SUVmean), metabolic tumor volume (MTV), total lesion glycolysis (TLG), and SUVmax, were determined and compared between the 2 subtypes. The diagnostic performance of these parameters was evaluated with receiver operating characteristic (ROC) curve analysis.

Results: All parameters significantly differed between patients with PMLs and TETs. Patients with lymphomas were younger and had higher SUVmean, SUVmax, TLG, and MTV values than patients with TETs. The MTV and TLG values had similar diagnostic performance. ROC analysis indicated that the areas under the curves of the SUVmean and SUVmax values performed similarly (approximately 0.76) in differentiating patients with PMLs from TETs, and both values were better than the MTV and TLG values. When age was included with the SUVmax in differentiating TETs from PMLs, the AUC was 0.91, and the sensitivity and specificity increased to 80% and 93%, respectively.

Conclusions: The SUVmax and volumetric parameters of ¹⁸F-FDG PET/CT can be used to distinguish patients with PMLs versus TETs, and thus may aid in preventing unnecessary thymectomies or other invasive operations.

Usefulness of Preoperative 18F-FDG PET/CT for Patients with Thymic Epithelial Tumors

Background

The purpose of this study was to investigate the relationship between preoperative FDG-PET parameters and the World Health Organization (WHO) classification or Masaoka staging system of thymic epithelial tumors.

Methods

We retrospectively reviewed 32 patients with histologically proven thymic epithelial tumors who underwent FDG-PET/CT before surgical resection. FDG-PET parameters, including the maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV), and total lesion glycolytic activity (TLG), were measured. These PET parameters were compared in the Masaoka staging system and WHO classification. A receiver operating characteristics (ROC) analysis was performed to identify the cut-off values of PET parameters for the accurate differentiation of early and advanced stages in the Masaoka staging system.

Results

There were 17 low-risk thymomas (1 type A, 9 type AB, and 7 type B1), 8 high-risk thymomas (4 type B2 and 4 type B3), and 7 thymic carcinomas (7 squamous cell carcinoma). Their Masaoka stages were as follows: 24 in the early stage (stages I and II) and 8 in the advanced stage (stage III). Regarding the WHO classification, only SUVmax showed a significant difference (P < 0.05). In the Masaoka stage, all PET parameters were significantly higher in the advanced stage than in the early stage (P < 0.05). In the ROC analysis to predict the early and advanced stages in thymic epithelial tumors, the area under the curve was the highest for TLG among the PET parameters examined and the cut-off value of TLG for discriminating the early from advanced stage with maximal sensitivity and specificity was 30.735.

Conclusion

Although volumetric PET parameters, such as MTV and TLG, did not correlate with the WHO classification, a significant correlation was observed between SUVmax and the WHO classification. In the Masaoka staging system, volumetric PET parameters may achieve more precise staging than SUVmax.