The Role of [18F]F-FDG PET/CT for Predicting Histology and Prognosis in Patients with Thymic Lesions

Gao C., Yang L., Xu Y., Wang T., Ding H., Gao X., Li L.

Abstract Background This study was designed to develop a combined radiomics nomogram to preoperatively predict the risk categorization of thymomas based on contrast-enhanced computed tomography (CE-CT) images. Materials The clinical and CT data of 178 patients with thymoma (100 patients with low-risk thymomas and 78 patients with high-risk thymomas) collected in our hospital from March 2018 to July 2023 were retrospectively analyzed. The patients were randomly divided into a training set (n = 125) and a validation set (n = 53) in a 7:3 ratio. Qualitative radiological features were recorded, including (a) tumor diameter, (b) location, (c) shape, (d) capsule integrity, (e) calcification, (f) necrosis, (g) fatty infiltration, (h) lymphadenopathy, and (i) enhanced CT value. Radiomics features were extracted from each CE-CT volume of interest (VOI), and the least absolute shrinkage and selection operator (LASSO) algorithm was performed to select the optimal discriminative ones. A combined radiomics nomogram was further established based on the clinical factors and radiomics scores. The differentiating efficacy was determined using receiver operating characteristic (ROC) analysis. Results Only one clinical factor (incomplete capsule) and seven radiomics features were found to be independent predictors and were used to establish the radiomics nomogram. In differentiating low-risk thymomas (types A, AB, and B1) from high-risk ones (types B2 and B3), the nomogram demonstrated better diagnostic efficacy than any single model, with the respective area under the curve (AUC), accuracy, sensitivity, and specificity of 0.974, 0.921, 0.962 and 0.900 in the training cohort, 0.960, 0.892, 0923 and 0.897 in the validation cohort, respectively. The calibration curve showed good agreement between the prediction probability and actual clinical findings. Conclusions The nomogram incorporating clinical factors and radiomics features provides additional value in differentiating the risk categorization of thymomas, which could potentially be useful in clinical practice for planning personalized treatment strategies.

Zhao K., Liu Y., Jing M., Cai W., Jin J., Zhu Z., Shen L., Wen J., Xue Z.

IntroductionWe aimed to assess the impact of myasthenia gravis (MG) on the long-term prognosis in patients with thymoma after surgery and identify related prognostic factors or predictors.MethodsThis retrospective observational study included 509 patients with thymoma (thymoma combined with MG [MG group] and thymoma alone [non-MG group]). Propensity score matching was performed to obtain comparable subsets of 96 patients in each group. A comparative analysis was conducted on various parameters.ResultsBefore matching, the 10-year survival and recurrence-free survival rates in both groups were 93.8 and 98.4%, and 85.9 and 93.4%, respectively, with no statistically significant difference observed in the survival curves between the groups (p > 0.05). After propensity score matching, 96 matched pairs of patients from both groups were created. The 10-year survival and recurrence-free survival rates in these matched pairs were 96.9 and 97.7%, and 86.9 and 91.1%, respectively, with no statistical significance in the survival curves between the groups (p > 0.05). Univariate analysis of patients with thymoma postoperatively revealed that the World Health Organization histopathological classification, Masaoka–Koga stage, Tumor Node Metastasis stage, resection status, and postoperative adjuvant therapy were potentially associated with tumor recurrence after thymoma surgery. Multivariate analysis demonstrated that the Masaoka–Koga stage and postoperative adjuvant therapy independently predicted the risk of recurrence in patients with thymoma after surgery.ConclusionThere was no difference in prognosis in patients with thymoma with or without MG. The Masaoka–Koga stage has emerged as an independent prognostic factor affecting recurrence-free survival in patients with thymoma, while postoperative adjuvant therapy represents a poor prognostic factor.

Chao F., Wang R., Han X., Huang W., Wang R., Yu Y., Lin X., Yuan P., Yang M., Gao J.

AbstractBackgroundThe aim of the present study was to evaluate the impact of intratumoral metabolic heterogeneity and quantitative 18F‐FDG PET/CT imaging parameters in predicting patient outcomes in thymic epithelial tumors (TETs).MethodsThis retrospective study included 100 patients diagnosed with TETs who underwent pretreatment 18F‐FDG PET/CT. The maximum and mean standardized uptake values (SUVmax and SUVmean), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) on PET/CT were measured. Heterogeneity index‐1 (HI‐1; standard deviation [SD] divided by SUVmean) and heterogeneity index‐2 (HI‐2; linear regression slopes of the MTV according with different SUV thresholds), were evaluated as heterogeneity indices. Associations between these parameters and patient survival outcomes were analyzed.ResultsThe univariate analysis showed that Masaoka stage, TNM stage, WHO classification, SUVmax, SUVmean, TLG, and HI‐1 were significant prognostic factors for progression‐free survival (PFS), while MTV, HI‐2, age, gender, presence of myasthenia gravis, and maximum tumor diameter were not. Subsequently, multivariate analyses showed that HI‐1 (p < 0.001) and TNM stage (p = 0.002) were independent prognostic factors for PFS. For the overall survival analysis, TNM stage, WHO classification, SUVmax, and HI‐1 were significant prognostic factors in the univariate analysis, while TNM stage remained an independent prognostic factor in multivariate analyses (p = 0.024). The Kaplan Meier survival analyses showed worse prognoses for patients with TNM stages III and IV and HI‐1 ≥ 0.16 compared to those with stages I and II and HI‐1 < 0.16 (log‐rank p < 0.001).ConclusionHI‐1 and TNM stage were independent prognostic factors for progression‐free survival in TETs. HI‐1 generated from baseline 18F‐FDG PET/CT might be promising to identify patients with poor prognosis.

Klug M., Strange C.D., Truong M.T., Kirshenboim Z., Ofek E., Konen E., Marom E.M.

Urso L., Frantellizzi V., De Vincentis G., Schillaci O., Filippi L., Evangelista L.

To provide a comprehensive overview of the current main applications of long axial field-of-view (LAFOV) PET/CT in oncology. Relevant studies published from 2017 up to September 2022 were selected by searching Scopus, PubMed, and Web of Science. The following data were extracted: characteristics of studies and patients, technical aspects, and usefulness of the LAFOV PET/CT in the oncological setting. Selected imaging studies were analyzed using a modified version of the Critical Appraisal Skills Programme (CASP). Seventeen papers were finally selected: 12 (70.6%) were retrospective, while 5 (29.4%) were prospective, with an overall number of 877 included patients. Most of the studies (14/17, 82.4%) employed [18F]-FDG as a radiopharmaceutical agent, 2 papers used [68Ga]-PSMA-11 and 1 employed mixed tracers. Eleven studies were focused on protocols at low/ultra-low activity or with fast/ultra-fast scanning time, 3 papers compared the performance of LAFOV PET/CT scanners with that of conventional (standard axial field-of-view/SAFOV) devices, 4 were total-body PET dynamic studies. LAFOV PET/CT showed superior diagnostic performance than traditional SAFOV devices, allowing reduced activity/time and dynamic protocols. The applications of this novel technology in some clinical settings, mainly the pediatric population, are promising but should be a topic of future investigations.

Chiappetta M., Mendogni P., Cattaneo M., Evangelista J., Farina P., Pizzuto D.A., Annunziata S., Castello A., Congedo M.T., Tabacco D., Sassorossi C., Castellani M., Nosotti M., Margaritora S., Lococo F.

Background: The usefulness of 18FDG PET/CT scan in the evaluation of thymic epithelial tumours (TETs) has been reported by several authors, but data are still limited and its application in clinical practice is far from being defined. Methods: We performed a narrative review of pertinent literature in order to clarify the role of 18FDG PET/CT in the prediction of TET histology and to discuss clinical implications and future perspectives. Results: There is only little evidence that 18FDG PET/CT scan may distinguish thymic hyperplasia from thymic epithelial tumours. On the other hand, it seems to discriminate well thymomas from carcinomas and, even more, to predict the grade of malignancy (WHO classes). To this end, SUVmax and other PET variables (i.e., the ratio between SUVmax and tumour dimensions) have been adopted, with good results. Finally, however promising, the future of PET/CT and theranostics in TETs is far from being defined; more robust analysis of imaging texture on thymic neoplasms, as well as new exploratory studies with “stromal PET tracers,” are ongoing. Conclusions: PET may play a role in predicting histology in TETs and help physicians in the management of these insidious malignancies.

Nakajo M., Takeda A., Katsuki A., Jinguji M., Ohmura K., Tani A., Sato M., Yoshiura T.

Objective: To examine whether the machine-learning approach using 18-fludeoxyglucose positron emission tomography (18F-FDG-PET)-based radiomic and deep-learning features is useful for predicting the pathological risk subtypes of thymic epithelial tumors (TETs). Methods: This retrospective study included 79 TET [27 low-risk thymomas (types A, AB and B1), 31 high-risk thymomas (types B2 and B3) and 21 thymic carcinomas] patients who underwent pre-therapeutic 18F-FDG-PET/CT. High-risk TETs (high-risk thymomas and thymic carcinomas) were 52 patients. The 107 PET-based radiomic features, including SUV-related parameters [maximum SUV (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG)] and 1024 deep-learning features extracted from the convolutional neural network were used to predict the pathological risk subtypes of TETs using six different machine-learning algorithms. The area under the curves (AUCs) were calculated to compare the predictive performances. Results: SUV-related parameters yielded the following AUCs for predicting thymic carcinomas: SUVmax 0.713, MTV 0.442, and TLG 0.479 or high-risk TETs: SUVmax 0.673, MTV 0.533, and TLG 0.539. The best-performing algorithm was the logistic regression model for predicting thymic carcinomas (AUC 0.900, accuracy 81.0%), and the random forest (RF) model for high-risk TETs (AUC 0.744, accuracy 72.2%). The AUC was significantly higher in the logistic regression model than three SUV-related parameters for predicting thymic carcinomas, and in the RF model than MTV and TLG for predicting high-risk TETs (each; p < 0.05). Conclusion: 18F-FDG-PET-based radiomic analysis using a machine-learning approach may be useful for predicting the pathological risk subtypes of TETs. Advances in knowledge: Machine-learning approach using 18F-FDG-PET-based radiomic features has the potential to predict the pathological risk subtypes of TETs.

Marx A., Chan J.K., Chalabreysse L., Dacic S., Detterbeck F., French C.A., Hornick J.L., Inagaki H., Jain D., Lazar A.J., Marino M., Marom E.M., Moreira A.L., Nicholson A.G., Noguchi M., et. al.

This overview of the fifth edition of the WHO classification of thymic epithelial tumors (including thymomas, thymic carcinomas, and thymic neuroendocrine tumors [NETs]), mediastinal germ cell tumors, and mesenchymal neoplasms aims to (1) list established and new tumor entities and subtypes and (2) focus on diagnostic, molecular, and conceptual advances since publication of the fourth edition in 2015. Diagnostic advances are best exemplified by the immunohistochemical characterization of adenocarcinomas and the recognition of genetic translocations in metaplastic thymomas, rare B2 and B3 thymomas, and hyalinizing clear cell carcinomas. Advancements at the molecular and tumor biological levels of utmost oncological relevance are the findings that thymomas and most thymic carcinomas lack currently targetable mutations, have an extraordinarily low tumor mutational burden, but typically have a programmed death-ligand 1high phenotype. Finally, data underpinning a conceptual advance are illustrated for the future classification of thymic NETs that may fit into the classification scheme of extrathoracic NETs. Endowed with updated clinical information and state-of-the-art positron emission tomography and computed tomography images, the fifth edition of the WHO classification of thymic epithelial tumors, germ cell tumors, and mesenchymal neoplasms with its wealth of new diagnostic and molecular insights will be a valuable source for pathologists, radiologists, surgeons, and oncologists alike. Therapeutic perspectives and research challenges will be addressed as well.

Han S., Kim Y., Oh J.S., Seo S.Y., Park M., Lee G.D., Choi S., Kim H.R., Kim Y., Kim D.K., Park S., Ryu J.

We aimed to evaluate the diagnostic ability for the prediction of histologic grades and prognostic values on recurrence and death of pretreatment 2-[18F]FDG PET/CT in patients with resectable thymic epithelial tumours (TETs). One hundred and fourteen patients with TETs who underwent pretreatment 2-[18F]FDG PET/CT between 2012 and 2018 were retrospectively evaluated. TETs were classified into three histologic subtypes: low-risk thymoma (LRT, WHO classification A/AB/B1), high-risk thymoma (HRT, B2/B3), and thymic carcinoma (TC). Area under the receiver operating characteristics curve (AUC) was used to assess the diagnostic performance of PET/CT variables (maximum standardised uptake value [SUVmax], metabolic tumour volume [MTV], total lesion glycolysis [TLG], maximum diameter). Cox proportional hazards models were built using PET/CT and clinical variables. The tumours included 52 LRT, 33 HRT, and 29 TC. SUVmax showed good diagnostic ability for differentiating HRT/TC from LRT (AUC 0.84, 95% confidence interval [CI] 0.76 − 0.92) and excellent ability for differentiating TC from LRT/HRT (AUC 0.94, 95% CI 0.90 − 0.98), with significantly higher values than MTV, TLG, and maximum diameter. With an optimal cut-off value of 6.4, the sensitivity, specificity, and accuracy for differentiating TC from LRT/HRT were 69%, 96%, and 89%, respectively. In the multivariable Cox proportional hazards analyses for freedom-from-recurrence, SUVmax was an independent prognostic factor (p < 0.001), whereas MTV and TLG were not. SUVmax was a significant predictor for overall survival in conjunction with clinical stage and resection margin. SUVmax showed excellent diagnostic performance for prediction of TC and significant prognostic value in terms of recurrence and survival. • Maximum standardised uptake value (SUVmax) shows excellent performance in the differentiation of thymic carcinoma from low- and high-risk thymoma. • SUVmax is an independent prognostic factor for freedom-from-recurrence in the multivariable Cox proportional hazard model and a significant predictor for overall survival. • 2-[ 18 F]FDG PET/CT can provide a useful diagnostic and prognostic imaging biomarker in conjunction with histologic classification and stage and help choose appropriate management for thymic epithelial tumours.

Lee J., Cho Y.S., Kim J., Shim Y.M., Lee K., Choi J.Y.

Background: Imaging tumor FDG avidity could complement prognostic implication in thymic epithelial tumors. We thus investigated the prognostic value of volume-based 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)/CT parameters in thymic epithelial tumors with other clinical prognostic factors. Methods: This is a retrospective study that included 83 patients who were diagnosed with thymic epithelial tumors and underwent pretreatment 18F-FDG PET/CT. PET parameters, including maximum and average standardized uptake values (SUVmax, SUVavg), metabolic tumor volume (MTV), and total lesion glycolysis (TLG), were measured with a threshold of SUV 2.5. Univariate and multivariate analysis of PET parameters and clinicopathologic variables for time-to-progression was performed by using a Cox proportional hazard regression model. Results: There were 21 low-risk thymomas (25.3%), 27 high-risk thymomas (32.5%), and 35 thymic carcinomas (42.2%). Recurrence or disease progression occurred in 24 patients (28.9%). On univariate analysis, Masaoka stage (p < 0.001); histologic types (p = 0.009); treatment modality (p = 0.001); and SUVmax, SUVavg, MTV, and TLG (all p < 0.001) were significant prognostic factors. SUVavg (p < 0.001) and Masaoka stage (p = 0.001) were independent prognostic factors on multivariate analysis. Conclusion: SUVavg and Masaoka stage are independent prognostic factors in thymic epithelial tumors.

Annunziata S., Pelliccioni A., Hohaus S., Maiolo E., Cuccaro A., Giordano A.

To evaluate the prognostic role of end-of-treatment (EoT) FDG-PET/CT parameters in diffuse large B cell lymphoma (DLBCL), and then to explore a pilot application of Neural Networks (NN) in predicting time-to-relapse. For conventional survival analysis, parameters as Deauville score (DS) and quantitative extension of DS (qPET) were correlated to adverse events as relapse or progression in the follow-up. To build NN and conventional multi-regression models (MM) for time-to-event prediction, patients with residual FDG uptake (DS ≥ 2) and an adverse event were divided into a training and a test group. Models developed on the training group were evaluated in the test group. Pearson correlation coefficient (R) and mean relative error between observed and forecasted time-to-event were calculated. FDG-PET/CT data of 308 patients with DLBCL were analyzed. DS and qPET were prognostic factors in conventional univariate analysis. Positive and negative predictive values, respectively, were 55% and 83% for DS 4–5, 89% and 82% for positive qPET. Focusing on 37 relapsed patients with a residual FDG uptake, R between observed and forecasted time-to-event was of 0.63 in the NN model and 0.49 in the MM. Mean relative error in predicting time-to-event was of 58% for NN and 67% for MM. EoT FDG-PET/CT visual score (DS) is a strong outcome predictor in DLBCL in a large monocentric cohort. The semi-quantitative parameter qPET may increase this prognostic performance. A pilot NN model applied on residual FDG uptake parameters seems to predict time-to-event in the follow-up.

Ito T., Suzuki H., Sakairi Y., Wada H., Nakajima T., Yoshino I.

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

Ruffini E., Fang W., Guerrera F., Huang J., Okumura M., Kim D.K., Girard N., Billè A., Boubia S., Cangir A.K., Detterbeck F., Falkson C., Filosso P.L., Giaccone G., Kondo K., et. al.

Objectives The International Association for the Study of Lung Cancer Staging and Prognostic Factors Committee-Thymic Domain conducted a web-based cross-sectional survey to assess the acceptance of the TNM thymic staging system in the thoracic community. Methods A 50-item, web-based questionnaire was circulated among the members of the major thymic organizations worldwide from September to December 2018. The survey consisted of six sections (general information; overall perception of the TNM system; pretreatment staging; T category; N category; and perioperative treatments). Results In total, 217 responses were collected from 37 countries in four continents. The TNM classification was considered useful by 78% of the responders (N = 169); the Masaoka-Koga staging system was being used by 87% of the responders (N = 189). With regard to the T category, most responders (mostly surgeons) felt that the capsular and mediastinal pleural involvements should be considered separate T categories. As for the N category, 48% of the responders (N = 105) used the International Thymic Malignancies Interest Group/International Association for the Study of Lung Cancer thymic nodal map, and lymph node dissection (N1/N2) was performed for 50%/21% thymomas and 66%/41% thymic carcinomas. While analyzing the results by the three continents (Europe, Asia, and Americas), responders in Asia were found to report the largest use of the TNM system, the greatest attention to the N category, and the best participation in international thymic databases. Conclusions The survey indicates that the Union for International Cancer Control/American Joint Committee on Cancer TNM stage classification of thymic tumors is gaining acceptance among the scientific community. The present results will guide the work of the Staging and Prognostic Factors Committee-Thymic Domain for the revision of the ninth edition of the TNM stage classification of thymic tumors.

Annunziata S., Cuccaro A., Tisi M.C., Hohaus S., Rufini V.

To retrospectively investigate the prognostic role of the ratio between target lesion and liver SUVmax (rPET) in patients with follicular lymphoma (FL) submitted to FDG-PET/CT at the end of immuno-chemotherapy (PI-PET), and to compare rPET with International Harmonization Project criteria (IHP), Deauville Score (5p-DS) and FL International Prognostic Index at diagnosis (FLIPI). Eighty-nine patients with FL undergoing PI-PET were evaluated. The receiver operating characteristic (ROC) approach was applied to identify the optimal cut-point of rPET with respect to 5-years progression free survival (PFS). The prognostic significance of rPET was compared with IHP, DS and FLIPI. Positive predictive value (PPV) and negative predictive value (NPV) were calculated using the presence of adverse events as gold standard. The ROC analysis for rPET as predictor of progression showed an optimal rPET cut-point of 0.98. Patients with positive values of IHP, DS and rPET had a PFS of 50, 30 and 31%. PPV were of 56, 80 and 80%, NPV of 83, 86 and 88%, respectively. DS and rPET differed only in two patients. FLIPI was not predictive of progression and relapse. rPET is a prognostic factor in patients with FL submitted to PI-PET. Although it has a similar prognostic power as DS, it can have methodological advantages over visual analysis. PI-PET with different evaluation systems has a stronger prognostic power than FLIPI at diagnosis, so it could be useful to identify patients with FL at risk for early relapse after immuno-chemotherapy.

Im H., Bradshaw T., Solaiyappan M., Cho S.Y.

Numerous methods to segment tumors using 18F-fluorodeoxyglucose positron emission tomography (FDG PET) have been introduced. Metabolic tumor volume (MTV) refers to the metabolically active volume of the tumor segmented using FDG PET, and has been shown to be useful in predicting patient outcome and in assessing treatment response. Also, tumor segmentation using FDG PET has useful applications in radiotherapy treatment planning. Despite extensive research on MTV showing promising results, MTV is not used in standard clinical practice yet, mainly because there is no consensus on the optimal method to segment tumors in FDG PET images. In this review, we discuss currently available methods to measure MTV using FDG PET, and assess the advantages and disadvantages of the methods.