Diagnostic and prognostic values of 2-[18F]FDG PET/CT in resectable thymic epithelial tumour

Han, Sangwon; Kim, Yong‐Il; Oh, Jungsu S.; Seo, Soonjoo; Park, Min-Jae; Lee, Geun Dong; Choi, Sehoon; Kim, Hyeong Ryul; Kim, Yong Hee; Kim, Dong Kwan; Park, Seung-Il; Ryu, Jin‐Sook

doi:10.1007/s00330-021-08230-z

Cited by 14 publications

(23 citation statements)

References 36 publications

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“…Conventional CT features such as tumor shape, lymphadenopathy, and pericardial effusion may be the key features in tumor differentiation between TC and HRT [34]. In addition, the maximum standardized uptake value (SUVmax) of PET/CT showed excellent ability for differentiating TC from LRT/HRT [35].…”

Section: Discussionmentioning

confidence: 99%

Optimizing the radiomics-machine-learning model based on non-contrast enhanced CT for the simplified risk categorization of thymic epithelial tumors: A large cohort retrospective study

et al. 2022

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Section: Discussionmentioning

confidence: 99%

Optimizing the radiomics-machine-learning model based on non-contrast enhanced CT for the simplified risk categorization of thymic epithelial tumors: A large cohort retrospective study

et al. 2022

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“…Therefore, 18 F-FDG PET metabolic parameters can be used in the differential diagnosis of TETs and thymic lymphomas. Previous studies have demonstrated that thymic cancer has higher FDG uptake than thymomas [ 22 , 35 , 49 ], SUVmax showed good diagnostic ability for differentiating high-risk thymoma/ thymic carcinoma from low-risk thymoma (AUC = 0.84, 95% CI: 0.76–0.92) and excellent ability for differentiating thymic carcinoma from low-risk thymoma/high-risk thymoma (AUC = 0.94, 95% CI: 0.90–0.98) [ 35 ]. According to our investigation, few previous studies investigated the differential diagnosis of TETs and thymic lymphoma by using 18 F-FDG PET metabolic parameters.…”

Section: Discussionmentioning

confidence: 99%

“…18 F-FDG PET metabolic parameters have certain clinical value in the differential diagnosis of benign and malignant TETs [23,35], staging [19,36], prediction the grade of malignancy [37,38], prediction of pathological response after induction therapy [39,40], and prediction of prognosis [41,42]. For thymic lymphomas, most studies about 18 F-FDG PET mainly focus on staging [43], response evaluation [16,44] and prognosis prediction [45][46][47].…”

Section: Discussionmentioning

confidence: 99%

Multiparameter diagnostic model based on 18F-FDG PET and clinical characteristics can differentiate thymic epithelial tumors from thymic lymphomas

Wang

et al. 2022

BMC Cancer

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Objective To evaluate the diagnostic performance of combined multiparametric 18F-fluorodeoxyglucose positron emission tomography (18FDG PET) with clinical characteristics in differentiating thymic epithelial tumors (TETs) from thymic lymphomas. Patients and methods A total of 173 patients with 80 TETs and 93 thymic lymphomas who underwent 18F-FDG PET/CT before treatment were enrolled in this retrospective study. All patients were confirmed by pathology, and baseline characteristics and clinical data were also collected. The semi-parameters of 18F-FDG PET/CT, including lesion size, SUVmax (maximum standard uptake value), SUVmean (mean standard uptake value), TLG (total lesion glycolysis), MTV (metabolic tumor volume) and SUVR (tumor-to-normal liver standard uptake value ratio) were evaluated. The differential diagnostic efficacy was evaluated using the receiver operating characteristic (ROC) curve. Integrated discriminatory improvement (IDI) and net reclassification improvement (NRI), and Delong test were used to evaluate the improvement in diagnostic efficacy. The clinical efficacy was evaluated by decision curve analysis (DCA). Results Age, clinical symptoms, and metabolic parameters differed significantly between patients with TETs and thymic lymphomas. The ROC curve analysis of SUVR showed the highest differentiating diagnostic value (sensitivity = 0.763; specificity = 0.888; area under the curve [AUC] = 0.881). The combined diagnostics model of age, clinical symptoms and SUVR resulted in the highest AUC of 0.964 (sensitivity = 0.882, specificity = 0.963). Compared with SUVR, the diagnostic efficiency of the model was improved significantly. The DCA also confirmed the clinical efficacy of the model. Conclusions The multiparameter diagnosis model based on 18F-FDG PET and clinical characteristics had excellent value in the differential diagnosis of TETs and thymic lymphomas.

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“…Our local institutional review board approved the study protocol and waived the need for informed patient consent (IRB number 2021-1436). A part of the study cohort was previously reported in other studies from our institution 7,9 . Details of 18 F-FDG PET/CT acquisition are provided in the Supplemental Methods (Supplemental Digital Content 1, http://links.lww.com/CNM/A368).…”

Section: Methodsmentioning

confidence: 99%

“…The anterior mediastinal tumors were delineated using an absolute SUV threshold of ≥2.5. 7,9 The volumes of interest (VOIs) were then refined using the combined CT images with consideration of physiologic activity of adjacent blood pool and myocardium by a nuclear medicine physician with 9 years of experience (S.H.) using Mirada DBX software (version 1.2.0.59; Mirada Medical, Ltd, Oxford, United Kingdom).…”

Section: Definition Of Ground-truth Tumor Volumes Of Interestmentioning

confidence: 99%

Fully Automatic Quantitative Measurement of 18F-FDG PET/CT in Thymic Epithelial Tumors Using a Convolutional Neural Network

Han

Kim

et al. 2022

Clin Nucl Med

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Objectives: The aim of this study was to develop a deep learning (DL)-based segmentation algorithm for automatic measurement of metabolic parameters of 18 F-FDG PET/CT in thymic epithelial tumors (TETs), comparable performance to manual volumes of interest. Patients and Methods: A total of 186 consecutive patients with resectable TETs and preoperative 18 F-FDG PET/CTwere retrospectively enrolled (145 thymomas, 41 thymic carcinomas). A quasi-3D U-net architecture was trained to resemble ground-truth volumes of interest. Segmentation performance was assessed using the Dice similarity coefficient. Agreements between manual and DL-based automated extraction of SUV max , metabolic tumor volume (MTV), total lesion glycolysis (TLG), and 63 radiomics features were evaluated via concordance correlation coefficients (CCCs) and linear regression slopes. Diagnostic and prognostic values were compared in terms of area under the receiver operating characteristics curve (AUC) for thymic carcinoma and hazards ratios (HRs) for freedom from recurrence. Results: The mean Dice similarity coefficient was 0.83 ± 0.34. Automatically measured SUV max (slope, 0.97; CCC, 0.92), MTV (slope, 0.94; CCC, 0.96), and TLG (slope, 0.96; CCC, 0.96) were in good agreement with manual measurements. The mean CCC and slopes were 0.88 ± 0.06 and 0.89 ± 0.05, respectively, for the radiomics parameters. Automatically measured SUV max , MTV, and TLG showed good diagnostic accuracy for thymic carcinoma (AUCs: SUV max , 0.95; MTV, 0.85; TLG, 0.87) and significant prognostic value (HRs: SUV max , 1.31 [95% confidence interval, 1.16-1.48]; MTV,; TLG,). No significant differences in the AUCs or HRs were found between automatic and manual measurements for any of the metabolic parameters. Conclusions: Our DL-based model provides comparable segmentation performance and metabolic parameter values to manual measurements in TETs.

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Diagnostic and prognostic values of 2-[18F]FDG PET/CT in resectable thymic epithelial tumour

Cited by 14 publications

References 36 publications

Optimizing the radiomics-machine-learning model based on non-contrast enhanced CT for the simplified risk categorization of thymic epithelial tumors: A large cohort retrospective study

Optimizing the radiomics-machine-learning model based on non-contrast enhanced CT for the simplified risk categorization of thymic epithelial tumors: A large cohort retrospective study

Multiparameter diagnostic model based on 18F-FDG PET and clinical characteristics can differentiate thymic epithelial tumors from thymic lymphomas

Fully Automatic Quantitative Measurement of 18F-FDG PET/CT in Thymic Epithelial Tumors Using a Convolutional Neural Network

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