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Editor's Note. - RadioGraphics Update articles supplement or update information found in full-length articles previously published in RadioGraphics . These updates, written by at least one author of the previous article, provide a brief synopsis that emphasizes important new information such as technological advances, revised imaging protocols, new clinical guidelines involving imaging, or updated classification schemes.

Purpose: To differentiate mixed epithelial and stromal tumor family (MESTF) of the kidney from predominantly cystic renal cell carcinoma (RCC) using the magnetic resonance imaging (MRI)-based Bosniak classification system version 2019 (v2019)., Materials and Methods: The study included 36 consecutive patients with MESTF and 77 with predominantly cystic RCC who underwent preoperative renal MRI. One radiologist evaluated and documented the clinical and MRI characteristics (age, sex, laterality, R.E.N.A.L. Nephrometry Score [RNS], surgical approach, the signal intensity on T 2 -weighted imaging, restricted diffusion and enhancement features in corticomedullary phase). Blinded to clinical and pathological information, another two radiologists independently evaluated Bosniak category of all masses. Interobserver agreement based on Bosniak classification system v2019 was measured by the weighted Cohen/Conger's Kappa coefficient. Furthermore, predominantly cystic RCCs and MESTFs were divided into low (categories I, II, and IIF) and high-class (categories III, and IV) tumors. The independent sample t test (Mann-Whitney U test) or Pearson Chi-square test (Fisher's exact probability test) was utilized to compare clinical and imaging characteristics between MESTFs and predominantly cystic RCCs. The performance of the Bosniak classification system v2019 in distinguishing MESTF from predominantly cystic RCC was investigated via receiver operating characteristic curve analysis., Results: MESTF and predominantly cystic RCC groups significantly differed in terms of age, lesion size, RNS, restricted diffusion, and obvious enhancement in corticomedullary phase, but not sex, laterality, surgical approach, and the signal intensity on T 2 WI. Interobserver agreement was substantially based on the Bosniak classification system v2019. There were 24 low-class tumors and 12 high-class tumors in the MESTF group. Meanwhile, 13 low-class tumors and 64 high-class tumors were observed in the predominantly cystic RCC group. The distribution of low- or high-class tumors significantly differed between the MESTF and predominantly cystic RCC groups. Bosniak classification system v2019 had excellent discrimination (cutoff value = category III), and an area under curve value was 0.81; accuracy, 80.5%; sensitivity, 87.0%; and specificity, 66.7%., Conclusion: The MRI-based Bosniak classification system v2019 can effectively distinguish MESTF from predominantly cystic RCC if category III was used as a cutoff reference., (© 2024. The Author(s) under exclusive licence to Japan Radiological Society.)

Rationale and Objectives: Fibroblast Activation Protein (FAP) expressing cancer-associated fibroblasts has been a major breakthrough causing a paradigm shift in targeted theranostics focusing on the tumor microenvironment. In this study, a squaric acid derivative DOTA.SA.FAPi (SA.FAPi) has been evaluated as a potential diagnostic probe in diverse epithelial cancers and compared to the standard-of-care 18 F-FDG., Methods: 25 patients enrolled in this prospective study underwent 18 F-FDG and 68 Ga-SA.FAPi PET scans on two different days. For biodistribution, standardized uptake values (SUV) were computed by delineating region-of-interest on various body organs. For comparative analysis in disease identification, lesion tracer uptake was quantified using SUVs corrected for lean body mass (SUL), SUV max , tumor-to-background ratio (TBR) with liver and blood pool as the reference, total lesion glycolysis (TLG for 18 F-FDG) and total lesion FAP expression (TLF for 68 Ga-SA.FAPi)., Results: 25 patients (mean age: 58 ± 8 years) with four types of cancers including hepatocellular carcinoma (HCC, 56% of cohort), gall bladder carcinoma (GB Ca, 12%), adrenocortical carcinoma (ACC, 16%), and breast carcinoma (breast Ca, 16%) were prospectively evaluated. Physiological tracer uptake of 68 Ga-SA.FAPi was noted in the salivary glands, thyroid, liver, pancreas, muscles and kidneys with variable uptake in the lacrimal glands, extra-ocular muscles, oral mucosa and uterus. Lesion-based comparative analysis between both the radiotracers demonstrated complete concordant findings in detection of all primary lesions and distant metastases in liver, bones, adrenals and peritoneum whereas discordant findings were noted in lung nodules (20%) and lymph nodes (13%). In overall analysis, 68 Ga-SA.FAPi exhibited significantly higher SUV max (10.3 vs 8.8, p-0.019), SUL peak (6.8 vs 4.9, p-0.000) and SUL avg (5.4 vs 4.1, p-0.019) in comparison to 18 F-FDG whereas TBR was comparable for both the tracers [TBR Liver : median 1.9 (IQR: 2.6-1.4) vs 1.8 (2.6-1.1), p-0.275; TBR Bloodpool : 2.1 (3.7-1.4) vs 2.0 (2.7-1.4), p-0.207]. In subcategorical analysis, 68 Ga-SA.FAPi demonstrated higher SUV max , SUL peak and SUL avg values for primary disease (SUV max : 14.8 (18.7-9.7) vs (12.9-6.6), p-0.087; SUL peak : 8.2 (11.2-6.8) vs 6.3 (8.5-4.4), p-0.037; SUL avg : 6.9 ± 2.5 vs 5.1 ± 2.2, p-0.023] and distant metastases (8.8 vs 7.2, p-0.038); 6.3 (8.8-4.4) vs 3.6 (4.4-2.0), p-0.000; 5.4 vs 3.5, p-0.000] whereas comparable values were noted for both the tracers in nodal metastases [9 (13.5-4.1) vs 8 (12.7-4.7), p-0.726; 4.5 (6.2-1.8) vs 4.3 (5.7-2.2), p-0.727; 4.1 ± 2.3 vs 3.7 ± 1.8, p-0.129]. In primary disease, highest 68 Ga-SA.FAPi avidity was noted in ACC followed by GB Ca and HCC. In distant metastases, gall bladder, lung and skeletal lesions demonstrated higher 68 Ga-SA.FAPi avidity. Moreover, 68 Ga-SA.FAPi identified five additional lung lesions which were missed by 18 F-FDG in one case of ACC., Conclusion: 68 Ga-SA.FAPi emerged as an effective, versatile diagnostic probe for imaging various epithelial malignancies similar to 18 F-FDG., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Dr. Jaya Shukla reports financial support was provided by Post Graduate Institute of Medical Education and Research., (Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Background: The aim of the present study was to evaluate the impact of intratumoral metabolic heterogeneity and quantitative 18 F-FDG PET/CT imaging parameters in predicting patient outcomes in thymic epithelial tumors (TETs)., Methods: This retrospective study included 100 patients diagnosed with TETs who underwent pretreatment 18 F-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., Results: The 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)., Conclusion: HI-1 and TNM stage were independent prognostic factors for progression-free survival in TETs. HI-1 generated from baseline 18 F-FDG PET/CT might be promising to identify patients with poor prognosis., (© 2024 The Author(s). Thoracic Cancer published by John Wiley & Sons Australia, Ltd.)

Purpose: The aim of this study was to evaluate the potential role of [ 18 F]FDG positron emission tomography/computed tomography (PET/CT) in the characterization of thymic epithelial tumors (TETs). Materials and Methods: A total of 73 patients who underwent preoperative [ 18 F]FDG PET/CT were included in this study. Visual total score (VTS), maximum standard uptake values (SUV max ), metabolic tumor volume (MTV), total lesion glycolysis (TLG), and heterogeneity index (HI) parameters were analyzed to investigate the prediction of histopathologic grade and advanced stage. Results: The cohort included 26 patients with low-grade thymoma (LGT), 36 patients with high-grade thymoma (HGT), and 11 patients with thymic carcinoma (TC). Ninety-one percent of TC had VTS >2, whereas 31% of LGT and 75% of HGT had VTS >2. SUV max , MTV, and TLG were statistically significantly higher in the TC group than in both thymoma and HGT. Using the cutoff value of 7.25 for SUV max , TC was differentiated from thymomas with 91% sensitivity and 74% specificity. TC had significantly lower HI values than thymomas. HI parameters showed good diagnostic ability to differentiate TC from thymoma and TC from HGT. SUV max , MTV, and TLG were significantly higher in advanced-stage disease than in early-stage disease. Conclusions: Visual and quantitative parameters can reliably predict both advanced disease and the grade of primary tumor in TETs. Therefore, as a promising metabolic imaging method, [ 18 F]FDG PET/CT makes important contributions to preoperative evaluation in routine clinical practice.

Fusing multi-modal radiology and pathology data with complementary information can improve the accuracy of tumor typing. However, collecting pathology data is difficult since it is high-cost and sometimes only obtainable after the surgery, which limits the application of multi-modal methods in diagnosis. To address this problem, we propose comprehensively learning multi-modal radiology-pathology data in training, and only using uni-modal radiology data in testing. Concretely, a Memory-aware Hetero-modal Distillation Network (MHD-Net) is proposed, which can distill well-learned multi-modal knowledge with the assistance of memory from the teacher to the student. In the teacher, to tackle the challenge in hetero-modal feature fusion, we propose a novel spatial-differentiated hetero-modal fusion module (SHFM) that models spatial-specific tumor information correlations across modalities. As only radiology data is accessible to the student, we store pathology features in the proposed contrast-boosted typing memory module (CTMM) that achieves type-wise memory updating and stage-wise contrastive memory boosting to ensure the effectiveness and generalization of memory items. In the student, to improve the cross-modal distillation, we propose a multi-stage memory-aware distillation (MMD) scheme that reads memory-aware pathology features from CTMM to remedy missing modal-specific information. Furthermore, we construct a Radiology-Pathology Thymic Epithelial Tumor (RPTET) dataset containing paired CT and WSI images with annotations. Experiments on the RPTET and CPTAC-LUAD datasets demonstrate that MHD-Net significantly improves tumor typing and outperforms existing multi-modal methods on missing modality situations.

Aim: To investigate the diagnostic performance of computed tomography (CT)-guided percutaneous transthoracic needle biopsy (PTNB) for thymic epithelial tumours (TETs) and the complication rate after PTNB including seeding after PTNB., Materials and Methods: This retrospective study identified PTNBs for anterior mediastinal lesions between May 2007 and September 2021. The diagnostic performance for TETs and complications were investigated. The concordance of the histological grades of TETs between PTNB and surgery was evaluated. The factors associated with pleural seeding after PTNB were determined using Cox regression analysis., Results: Of 387 PTNBs, 235 PTNBs from 225 patients diagnosed as TETs (124 thymomas and 101 thymic carcinomas) and 150 PTNBs from 133 patients diagnosed as other than TETs were included. The sensitivity, specificity, and accuracy for TETs were 89.4% (210/235), 100% (210/210), and 93.5% (360/385), respectively, with an immediate complication rate of 4.4% (17/385). The concordance rate of the histological grades between PTNB and surgery was 73.3% (77/105) after excluding uncategorised types of thymomas. During follow-up after PTNB (median duration, 38.8 months; range, 0.3-164.6 months), no tract seeding was observed. Pleural seeding was observed in 26 patients. Thymic carcinoma (hazard ratio [HR], 5.94; 95% confidence interval [CI], 2.07-17.08; p=0.001) and incomplete resection (HR, 3.29; 95% CI, 1.20-9.02; p=0.02) were associated with pleural seeding, while the biopsy approach type (transpleural versus parasternal) was not associated (p=0.12)., Conclusions: Pretreatment biopsy for TETs was accurate and safe and may be considered for diagnosing TETs, particularly when the diagnosis is challenging and histological diagnosis is mandatory., (Copyright © 2024 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.)

Purpose: To investigate the value of computed tomography (CT) radiomic feature analysis for the differential diagnosis between thymic epithelial tumors (TETs) and thymic cysts, and prediction of histological subtypes of TETs., Materials and Methods: Twenty-four patients with TETs (13 low-risk and 9 high-risk thymomas, and 2 thymic carcinomas) and 12 with thymic cysts were included in this study. For each lesion, the radiomic features of a volume of interest covering the lesion were extracted from non-contrast enhanced CT images. The Least Absolute Shrinkage and Selection Operator (Lasso) method was used for the feature selection. Predictive models for differentiating TETs from thymic cysts (model A), and high risk thymomas + thymic carcinomas from low risk thymomas (model B) were created from the selected features. The receiver operating characteristic curve was used to evaluate the effectiveness of radiomic feature analysis for differentiating among these tumors., Results: In model A, the selected 5 radiomic features for the model A were NGLDM&#95;Contrast, GLCM&#95;Correlation, GLZLM&#95;SZLGE, DISCRETIZED&#95;HISTO&#95;Entropy&#95;log2, and DISCRETIZED&#95;HUmin. In model B, sphericity was the only selected feature. The area under the curve, sensitivity, and specificity of radiomic feature analysis were 1 (95% confidence interval [CI]: 1-1), 100%, and 100%, respectively, for differentiating TETs from thymic cysts (model A), and 0.76 (95%CI: 0.53-0.99), 64%, and 100% respectively, for differentiating high-risk thymomas + thymic carcinomas from low-risk thymomas (model B)., Conclusion: CT radiomic analysis could be utilized as a non-invasive imaging technique for differentiating TETs from thymic cysts, and high-risk thymomas + thymic carcinomas from low-risk thymomas., (© 2023. The Author(s) under exclusive licence to Japan Radiological Society.)

Background: Preoperative fluorine-18-fluorodeoxyglucose positron emission tomography ( 18 F-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 18 F-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 18 F-FDG PET than those who were not (75.0% versus 7.1%)., Conclusions: 18 F-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., (© 2023. Society of Surgical Oncology.)

Background: Thymic epithelial tumors (TETs) are the most common primary neoplasms of the anterior mediastinum. Different risk subgroups of TETs have different prognosis and therapeutic strategies, therefore, preoperative identification of different risk subgroups is of high clinical significance. This study aims to explore the diagnostic efficiency of quantitative computed tomography (CT) parameters combined with preoperative systemic inflammatory markers in differentiating low-risk thymic epithelial tumors (LTETs) from high-risk thymic epithelial tumors (HTETs)., Methods: 74 Asian patients with TETs confirmed by biopsy or postoperative pathology between January 2013 and October 2022 were collected retrospectively and divided into two risk subgroups: LTET group (type A, AB and B1 thymomas) and HTET group (type B2, B3 thymomas and thymic carcinoma). Statistical analysis were performed between the two groups in terms of quantitative CT parameters and preoperative systemic inflammatory markers. Multivariate logistic regression analysis was used to determine the independent predictors of risk subgroups of TETs. The area under curve (AUC) and optimal cut-off values were calculated by receiver operating characteristic (ROC) curves., Results: 47 TETs were in LTET group, while 27 TETs were in HTET group. In addition to tumor size and CT value of the tumor on plain scan, there were statistical significance comparing in CT value of the tumor on arterial phase (CTv-AP) and venous phase (CTv-VP), and maximum enhanced CT value (CE max ) of the tumor between the two groups (for all, P < 0.05). For systemic inflammatory markers, HTET group was significantly higher than LTET group (for all, P < 0.05), including platelet-to-lymphocyte ratio (PLR), neutrophil-to-lymphocyte ratio (NLR) and systemic immune-inflammation index (SII). Multivariate logistic regression analysis showed that NLR (odds ratio [OR] = 2.511, 95% confidence interval [CI]: 1.322-4.772, P = 0.005), CTv-AP (OR = 0.939, 95%CI: 0.888-0.994, P = 0.031) and CTv-VP (OR = 0.923, 95%CI: 0.871-0.979, P = 0.008) were the independent predictors of risk subgroups of TETs. The AUC value of 0.887 for the combined model was significantly higher than NLR (0.698), CTv-AP (0.800) or CTv-VP (0.811) alone. The optimal cut-off values for NLR, CTv-AP and CTv-VP were 2.523, 63.44 Hounsfeld Unit (HU) and 88.29HU, respectively., Conclusions: Quantitative CT parameters and preoperative systemic inflammatory markers can differentiate LTETs from HTETs, and the combined model has the potential to improve diagnostic efficiency and to help the patient management., (© 2023. The Author(s).)

Purpose: The study was aimed to develop and evaluate a deep learning-based radiomics to predict the histological risk categorization of thymic epithelial tumors (TETs), which can be highly informative for patient treatment planning and prognostic assessment., Method: A total of 681 patients with TETs from three independent hospitals were included and separated into derivation cohort and external test cohort. Handcrafted and deep learning features were extracted from preoperative contrast-enhanced CT images and selected to build three radiomics signatures (radiomics signature [Rad&#95;Sig], deep learning signature [DL&#95;Sig] and deep learning radiomics signature [DLR&#95;Sig]) to predict risk categorization of TETs. A deep learning-based radiomic nomogram (DLRN) was then depicted to visualize the classification evaluation. The performance of predictive models was compared using the receiver operating characteristic and decision curve analysis (DCA)., Results: Among three radiomics signatures, DLR&#95;Sig demonstrated optimum performance with an AUC of 0.883 for the derivation cohort and 0.749 for the external test cohort. Combining DLR&#95;Sig with age and gender, DLRN was depict and exhibited optimum performance among all radiomics models with an AUC of 0.965, accuracy of 0.911, sensitivity of 0.921 and specificity of 0.902 in the derivation cohort, and an AUC of 0.786, accuracy of 0.774, sensitivity of 0.778 and specificity of 0.771 in the external test cohort. The DCA showed that DLRN had greater clinical benefit than other radiomics signatures., Conclusions: Our study developed and validated a DLRN to accurately predict the risk categorization of TETs, which has potential to facilitate individualized treatment and improve patient prognosis evaluation., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Objective: To explore the value of maximal contrast-enhanced (CEmax) range using contrast-enhanced CT (CECT) imaging in differentiating the pathological subtypes and risk subgroups of thymic epithelial tumors (TETs)., Methods: The pre-treatment-CECT images of 319 TET patients from May 2012 to November 2021 were analyzed retrospectively. The CEmax was defined as the maximum difference between the CT value of the solid tumor on pre-contrast and contrast-enhanced images. The mean CEmax value was calculated at three different tumor levels., Results: There was a significant difference in the CEmax among the eight main pathological subtypes [types A, AB, B1, B2, and B3 thymoma, thymic carcinoma (TC), low-grade neuroendocrine tumor (NET) and high-grade NET] ( p < 0.001). Among the eight subtypes, the CEmax values of types A, AB, and low-risk NET were higher than those of the other subtypes (all p < 0.001), and there was no difference among types B1-B3 and high-risk NET (all p > 0.05). There was no difference for CEmax values between NET and TC ( p = 0.491). For the risk subgroups, the CEmax of TC (including NET) was 35.35 ± 11.41 HU, which was lower than that of low-risk thymoma (A and AB) (57.73±21.24 HU) ( P < 0.001) and was higher than that of high-risk thymoma (B1-B3) (27.37±8.27 HU) ( P < 0.001). The CEmax cut-off values were 38.5 HU and 30.5 HU respectively (AUC: 0.829 and 0.712; accuracy, 72.4% and 67.7%)., Conclusion: The tumor CEmax on CECT helps differentiate the pathological subtypes and risk subgroups of TETs., Advances in Knowledge: In this study, an improved simplified risk grouping method was proposed based on the traditional (2004 edition) simplified risk grouping method for TETs. If Type B1 thymoma is classified as high-risk, radiologists using this improved method may improve the accuracy in differentiating risk level of TETs compared with the traditional method.

Background: Incidental thymus region masses during thoracic examinations are not uncommon. The clinician's decision-making for treatment largely depends on imaging findings. Due to the lack of specific indicators, it may be of great value to explore the role of radiomics in risk categorization of the thymic epithelial tumors (TETs)., Methods: Four databases (PubMed, Web of Science, EMBASE and the Cochrane Library) were screened to identify eligible articles reporting radiomics models of diagnostic performance for risk categorization in TETs patients. The quality assessment of diagnostic accuracy studies 2 (QUADAS-2) and radiomics quality score (RQS) were used for methodological quality assessment. The pooled area under the receiver operating characteristic curve (AUC), sensitivity and specificity with their 95% confidence intervals were calculated., Results: A total of 2134 patients in 13 studies were included in this meta-analysis. The pooled AUC of 11 studies reporting high/low-risk histologic subtypes was 0.855 (95% CI, 0.817-0.893), while the pooled AUC of 4 studies differentiating stage classification was 0.826 (95% CI, 0.817-0.893). Meta-regression revealed no source of significant heterogeneity. Subgroup analysis demonstrated that the best diagnostic imaging was contrast enhanced computer tomography (CECT) with largest pooled AUC (0.873, 95% CI 0.832-0.914). Publication bias was found to be no significance by Deeks' funnel plot., Conclusions: This present study shows promise for preoperative selection of high-risk TETs patients based on radiomics signatures with current available evidence. However, methodological quality in further studies still needs to be improved for feasibility confirmation and clinical application of radiomics-based models in predicting risk categorization of the thymic epithelial tumors., (© 2023. BioMed Central Ltd., part of Springer Nature.)

Purpose: The aim of this study was to clarify the frequency of thoracic recurrence and identify associated pathological features in postoperative patients with borderline or malignant ovarian epithelial tumors (BMOT) in stage I versus higher stages., Materials and Methods: A total of 368 consecutive patients with a single primary BMOT were treated at our hospital. This study included the 217 patients with no residual disease on the first CT after standard treatment. The timing and pattern of recurrence on follow-up CT images with a scan range from chest to pelvis were evaluated retrospectively. Patient characteristics, tumor histology, and stage were recorded from electronic medical records., Results: After a median follow-up period of 48 months, recurrence was detected by CT in 9 patients in stage I (n = 159) and 15 in stage II/III (n = 58) (p = 0.0001). Thoracic recurrence was detected in four patients in stage I and four in stage II/III (p = 0.15). Abdominal recurrence was identified as a factor associated with thoracic recurrence (P < 0.001). Clear cell carcinomas accounted for three out of four thoracic recurrences in stage I and two out of four in stage II/III, and had the highest rates of thoracic recurrence (7.7% in stage I and 22.2% in stage II/III) among all histological types associated with thoracic recurrence. Among patients with recurrence, thoracic recurrence-free probability (p = 0.38), median abdominal recurrence-free interval (18 vs 16 months; p = 0.55) and thoracic recurrence-free interval (16.5 vs 23 months; p = 0.89) did not differ significantly between stage I and stage II/III., Conclusion: The frequency and timing of thoracic recurrence did not differ significantly in postoperative patients with BMOT in stage I versus stage II/III. Abdominal recurrence and a histological type of clear cell carcinoma were most often associated with thoracic recurrence in stage I., (© 2022. The Author(s).)

Objectives: The objective of this study is to preoperatively investigate the value of multiphasic contrast-enhanced computed tomography (CT)-based radiomics signatures for distinguishing high-risk thymic epithelial tumors (HTET) from low-risk thymic epithelial tumors (LTET) compared with conventional CT signatures., Materials and Methods: Pathologically confirmed 305 thymic epithelial tumors (TETs), including 147 LTET (Type A/AB/B1) and 158 HTET (Type B2/B3/C), were retrospectively analyzed, and were randomly divided into training (n = 214) and validation cohorts (n = 91). All patients underwent nonenhanced, arterial contrast-enhanced, and venous contrast-enhanced CT analysis. The least absolute shrinkage and selection operator regression with 10-fold cross-validation was performed for radiomic models building, and multivariate logistic regression analysis was performed for radiological and combined models building. The performance of the model was evaluated by the area under the receiver operating characteristic curve (AUC of ROC), and the AUCs were compared using the Delong test. Decision curve analysis was used to evaluate the clinical value of each model. Nomogram and calibration curves were plotted for the combined model., Results: The AUCs for radiological model in the training and validation cohorts were 0.756 and 0.733, respectively. For nonenhanced, arterial contrast-enhanced, venous contrast-enhanced CT and 3-phase images combined radiomics models, the AUCs were 0.940, 0.946, 0.960, and 0.986, respectively, in the training cohort, whereas 0.859, 0.876, 0.930, and 0.923, respectively, in the validation cohort. The combined model, including CT morphology and radiomics signature, showed AUCs of 0.990 and 0.943 in the training and validation cohorts, respectively. Delong test and decision curve analysis showed that the predictive performance and clinical value of the 4 radiomics models and combined model were greater than the radiological model ( P < 0.05)., Conclusions: The combined model, including CT morphology and radiomics signature, greatly improved the predictive performance for distinguishing HTET from LTET. Radiomics texture analysis can be used as a noninvasive method for preoperative prediction of the pathological subtypes of TET., Competing Interests: The authors declare no conflict of interest., (Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.)

To determine the prognostic CT features in patients with untreated thymic epithelial tumors (TETs). Clinical data and CT imaging features of 194 patients with pathologically confirmed TETs were retrospectively reviewed. The subjects included 113 male and 81 female patients between 15 and 78 years of age, with a mean age of 53.8 years. Clinical outcomes were categorized according to whether relapse, metastasis or death occurred within 3 years after the first diagnosis. Associations between clinical outcomes and CT imaging features were determined using univariate and multivariate logistic regression analyses, while the survival status was analyzed by Cox regression. In this study, we analyzed 110 thymic carcinomas, 52 high-risk thymomas and 32 low-risk thymomas. Percentages of poor outcome and patient death in thymic carcinomas were much higher than those in patients with high-risk and low-risk thymomas. In the thymic carcinomas groups, 46 patients (41.8%) experienced tumor progression, local relapse or metastasis and were categorized as having poor outcomes; vessel invasion and pericardial mass were confirmed to be independent predictors by logistic regression analysis (p < 0.01). In the high-risk thymoma group, 11 patients (21.2%) were categorized as having poor outcomes, and the CT feature pericardial mass was confirmed to be an independent predictor (p < 0.01). In survival analysis, Cox regression showed that CT features of lung invasion, great vessel invasion, lung metastasis and distant organ metastasis were independent predictors for worse survival in the thymic carcinoma group (p < 0.01), while lung invasion and pericardial mass were independent predictors for worse survival in high-risk thymoma group. No CT features were related to poor outcome and worse survival in the low-risk thymoma group. Patients with thymic carcinoma had poorer prognosis and worse survival than those with high-risk or low-risk thymoma. CT can serve as an important tool for predicting the prognosis and survival of patients with TETs. In this cohort, CT features of vessel invasion and pericardial mass were related to poorer outcomes in those with thymic carcinoma and pericardial mass in those with high-risk thymoma. Features including lung invasion, great vessel invasion, lung metastasis and distant organ metastasis indicate worse survival in thymic carcinoma, whereas lung invasion and pericardial mass indicate worse survival in high-risk thymoma., (© 2023. The Author(s).)

Purpose: The purpose of this study was to differentiate cases without transcapsular invasion (Masaoka-Koga stage I) from cases with transcapsular invasion (Masaoka-Koga stage II or higher) in patients with thymic epithelial tumors (TETs) using tumoral and peritumoral computed tomography (CT) features., Methods: This retrospective study included 116 patients with pathological diagnoses of TETs. Two radiologists evaluated clinical variables and CT features, including size, shape, capsule integrity, presence of calcification, internal necrosis, heterogeneous enhancement, pleural effusion, pericardial effusion, and vascularity grade. Vascularity grade was defined as the extent of peritumoral vascular structures in the anterior mediastinum. The factors associated with transcapsular invasion were analyzed using multivariable logistic regression. In addition, the interobserver agreement for CT features was assessed using Cohen's or weighted kappa coefficients. The difference between the transcapsular invasion group and that without transcapsular invasion was evaluated statistically using the Student's t-test, Mann-Whitney U test, chi-square test, and Fisher's exact test., Results: Based on pathology reports, 37 TET cases without and 79 with transcapsular invasion were identified. Lobular or irregular shape [odds ratio (OR): 4.19; 95% confidence interval (CI): 1.53-12.09; P = 0.006], partial complete capsule integrity (OR: 5.03; 95% CI: 1.85-15.13; P = 0.002), and vascularity grade 2 (OR: 10.09; 95% CI: 2.59-45.48; P = 0.001) were significantly associated with transcapsular invasion. The interobserver agreement for shape classification, capsule integrity, and vascularity grade was 0.840, 0.526, and 0.752, respectively ( P < 0.001 for all)., Conclusion: Shape, capsule integrity, and vascularity grade were independently associated with transcapsular invasion of TETs. Furthermore, three CT TET features demonstrated good reproducibility and help differentiate between TET cases with and without transcapsular invasion.

Purpose: To assess the diagnostic feasibility of iodine concentration (IC) and extracellular volume (ECV) fraction measurement using the equilibrium phase dual-energy CT (DECT) for the evaluation of thymic epithelial tumors (TETs)., Materials and Methods: This study included 33 TETs (11 low-risk thymomas, 11 high-risk thymomas, and 11 thymic carcinomas) that were assessed by pretreatment DECT. IC was measured during the equilibrium phases and ECV fraction was calculated using IC of the thymic lesion and the aorta. IC and ECV fraction were compared among TET subtypes using the Kruskal-Wallis H test and Mann-Whitney U test. Receiver-operating characteristic (ROC) curve analysis was performed to evaluate the ability of IC and ECV fraction to diagnose thymic carcinoma., Results: IC during the equilibrium phase and ECV fraction differed among the three TET groups (both p < 0.001). IC during the equilibrium phase and ECV fraction was significantly higher in thymic carcinomas than in thymomas (1.9 mg/mL vs. 1.2 mg/mL, p < 0.001; 38.2% vs. 25.9%, p < 0.001; respectively). The optimal cutoff values of IC during the equilibrium phase and of ECV fraction to diagnose thymic carcinoma were 1.5 mg/mL (AUC, 0.955; sensitivity, 100%; specificity, 90.9%) and 26.8% (AUC, 0.888; sensitivity, 100%; specificity, 72.7%), respectively., Conclusion: IC and ECV fraction measurement using DECT are helpful in diagnosing TETs. High IC during the equilibrium phase and high ECV fraction are suggestive of thymic carcinoma., (© 2022. The Author(s).)

Objectives: We recently reported a high rate of nontherapeutic thymectomy. Mediastinal lymphomas (MLs) are the malignancies most likely to be confused with thymic epithelial tumours (TETs). This study aimed to establish a predictive model by evaluating clinical variables and positron emission tomography to distinguish those diseases., Methods: From 2018 to 2021, consecutive patients who were pathologically diagnosed with TETs or MLs were retrospectively reviewed. Univariable and multivariable analyses were used to identify association factors. The Akaike information criterion was used to select variables. A nomogram was developed and validated to differentiate MLs from TETs., Results: A total of 198 patients were included. Compared with TETs, patients with MLs were more likely to be younger with higher metabolic tumour volume (154.1 vs 74.6 cm3), total lesion glycolysis (1388.8 vs 315.2 g/ml cm3), SUVmean (9.2 vs 4.8), SUVpeak (12.9 vs 6.3) and SUVmax (14.8 vs 7.5). A nomogram was established based on the stepwise regression results and the final model containing age and SUVmax had minimal Akaike information criterion value of 72.28. Receiver operating characteristic analyses indicated that the area under the curve of predictive nomogram in differentiating MLs from TETs was 0.842 (95% CI: 0.754-0.907). The internal bootstrap resampling and calibration plots demonstrated good consistence between the prediction and the observation., Conclusions: Combination of age and SUVmax appears to be a useful tool to differentiate MLs from TETs. The novel predictive model prevents more patients from receiving nontherapeutic thymectomy., (© The Author(s) 2022. Published by Oxford University Press on behalf of the European Association for Cardio-Thoracic Surgery. All rights reserved.)

Background: World Health Organization classification and Masaoka-Koga stage are widely used for thymic epithelial tumors (TETs). Reduced field-of-view (rFOV) diffusion-weighed imaging (DWI) proved to improve the image quality. Dynamic contrast-enhanced (DCE) MRI was commonly used in evaluating tumors., Purpose: To investigate the value of multiparametric MRI in evaluating TETs., Study Type: Retrospective., Subjects: Eighty-seven participants including 38 low risk (52.08 ± 14.19 years), 30 high risk (52.40 ± 11.35 years), and 19 thymic carcinoma patients (59.76 ± 10.78 years)., Field Strength/sequence: A 3 T, turbo spin echo imaging, echo planar imaging, volumetric interpolated breath-hold examination with radial acquisition trajectory., Assessment: DCE-MRI and apparent diffusion coefficient (ADC) variables were compared. Diagnostic performances of single significant factor and combined model were compared., Statistical Tests: Parameters were compared using one-way ANOVA or independent-samples t test. Logistic regression was employed to investigate the combined model. Receiver operating curves (ROC) and DeLong's test were used to compare the diagnostic efficiency., Results: ADC, K trans , and k ep values were significantly different among low-risk, high-risk and carcinoma group (ADC, 1.279 ± 0.345 × 10 -3  mm 2 /sec, 0.978 ± 0.260 × 10 -3  mm 2 /sec, 0.661 ± 0.134 × 10 -3  mm 2 /sec; K trans 0.167 ± 0.071 min -1 , 0.254 ± 0.136 min -1 , 0.393 ± 0.110 min -1 ; k ep 0.345 ± 0.113 min -1 , 0.560 ± 0.269 min -1 , 0.872 ± 0.149 min -1 ). They were significantly different for early stage and advanced stage (ADC, 1.270 ± 0.356 × 10 -3  mm 2 /sec vs. 0.845 ± 0.251 × 10 -3  mm 2 /sec; K trans 0.179 ± 0.092 min -1 vs. 0.304 ± 0.142 min -1 ; k ep 0.370 ± 0.181 min -1 vs. 0.674 ± 0.362 min -1 ). The combination of them had highest diagnostic efficiency for WHO classification (AUC, 0.925; sensitivity, 83.7%; specificity, 89.5%), clinical stage (AUC, 0.879; sensitivity, 80.9%; specificity, 82.5%)., Data Conclusion: Multiparametric MRI model may be useful for discriminating WHO classification and clinical stage of TETs., Evidence Level: 4 TECHNICAL EFFICIENCY: Stage 2., (© 2022 International Society for Magnetic Resonance in Medicine.)

Purposes: This study aimed to evaluate the diagnostic capacity of apparent diffusion coefficient (ADC) in predicting pathological Masaoka and T stages in patients with thymic epithelial tumors (TETs)., Methods: Medical records of 62 patients who were diagnosed with TET and underwent diffusion-weighted imaging (DWI) prior to surgery between August 2017 and July 2021 were retrospectively analyzed. ADC values were calculated from DWI images using b values of 0, 400, and 800 s/mm 2 . Pathological stages were determined by histological examination of surgical specimens. Cut-off points of ADC values were calculated via receiver operating characteristic (ROC) analysis., Results: Patients had a mean age of 56.3 years. Mean ADC values were negatively correlated with pathological Masaoka and T stages. Higher values of the area under the ROC curve suggested that mean ADC values more accurately predicated pathological T stages than pathological Masaoka stages. The optimal cut-off points of mean ADC were 1.62, 1.31, and 1.48 × 10 -3 mm 2 /sec for distinguishing pathological T2-T4 from pathological T1, pathological T4 from pathological T1-T3, and pathological T3-T4 from pathological T2, respectively., Conclusion: ADC seems to more precisely predict pathological T stages, compared to pathological Masaoka stage. The cut-off values of ADC identified may be used to preoperatively predict pathological T stages of TETs., (© 2022. The Author(s).)

Background: Thymic epithelial tumors (TETs) are clinically the most frequently encountered neoplasm of the prevascular mediastinum in adults. The role of chest magnetic resonance (MR) imaging has been increasingly stressed thanks to its excellent contrast resolution, freedom from ionizing radiation, and capability to provide additional information regarding tumors' cellular structure and vascularity. Methods: This study aimed to establish the relationship between the MR findings and pathological classification of TETs, focusing on diffusion-weighted (DW) and dynamic contrast-enhanced (DCE) imaging. This retrospective cross-sectional study included 44 TET patients who underwent chest MR scanning. The tumors were classified into three groups according to the WHO classification: low-risk thymoma (LRT), high-risk thymoma (HRT), and non-thymoma (NT). Along with morphological characteristics, the apparent diffusion coefficient (ADC) value, time-intensity curve (TIC) pattern, and time to peak enhancement (TTP) of the tumors were recorded and compared between the three groups. Results: A smooth contour and complete or almost complete capsule were suggestive of LRTs. The median ADC value of the 44 tumors was 0.95 × 10 -3 mm 2 /sec. Among the three groups, LRTs had the highest ADC values, while NTs had the lowest. The differences between the ADC values of the three groups were statistically significant (p = 0.006). Using an ADC cutoff of 0.82 × 10 -3 mm 2 /sec to differentiate between LRTs and tumors of the two remaining groups, the area under the curve was 0.775, sensitivity was 100%, specificity was 50%, and accuracy was 65.91%. The washout (type 3) TIC pattern was the most prevalent, accounting for 45.45% of the population; this pattern was also predominantly observed in LRTs (71.43%). Although the median TTP of LRTs was lower than that of HRTs or NTs, no statistically significant differences were found between the TTPs of the three groups (p = 0.170). Conclusions: MR is a good imaging modality to preoperatively assess TETs. Morphological features, ADC value, TIC pattern, and TTP are helpful in preoperatively predicting TET pathology., Competing Interests: Competing Interests: The authors have declared that no competing interest exists., (© The author(s).)

Objective: To evaluate the diagnostic performance of combined multiparametric 18 F-fluorodeoxyglucose positron emission tomography ( 18 FDG 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 18 F-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 18 F-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 18 F-FDG PET and clinical characteristics had excellent value in the differential diagnosis of TETs and thymic lymphomas., (© 2022. The Author(s).)

Objectives: The aim of this study was to develop a deep learning (DL)-based segmentation algorithm for automatic measurement of metabolic parameters of 18F-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 18F-FDG PET/CT were 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 SUVmax, 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 SUVmax (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 SUVmax, MTV, and TLG 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% confidence interval, 1.16-1.48]; MTV, 2.11 [1.09-4.06]; TLG, 1.90 [1.12-3.23]). 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., Competing Interests: Conflicts of interest and sources of funding: This work was supported by the National Research Foundation of Korea grant funded by the Korean government (Ministry of Science and ICT; numbers NRF-2020M2D9A1094074 and 2021R1A2C3009056), and by a grant from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute, funded by the Ministry of Health & Welfare, Republic of Korea (HI18C2383). The funders had no role in the conceptualization or design of the study; in the collection, analysis, and interpretation of the data; in the writing of the manuscript; or in the decision to submit the manuscript for publication. The authors declare no competing interests., (Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.)

Background: It is often difficult to distinguish between thymoma and thymic carcinoma by preoperative radiological tests. While there have been some reports that the maximum standardized uptake value (SUV max ) in positron emission tomography-computed tomography (PET-CT) is useful to this end, no large-scale analysis has been performed. We therefore analyzed the usefulness of the SUV max and tumor size (TS) for differentiating thymic epithelial tumors., Methods: From 2011 to 2019, 129 patients with thymic epithelial tumor who underwent PET-CT before surgical treatment were enrolled. The relevance of the SUV max to the World Health Organization (WHO) histological type was assessed. To reduce the impact of the TS, the ratio of the SUV max to the TS was also investigated., Results: A total of 99 thymoma cases and 30 thymic carcinoma cases were enrolled into the study. The SUV max and SUV max /TS of thymic carcinoma were significantly higher than those of thymoma (SUV max : 7.7 ± 3.4 vs. 3.3 ± 1.3, p < 0.01; SUV max /TS: 1.5 ± 0.7 vs. 0.6 ± 0.4, p < 0.01). Focusing on the patients with a moderate SUV max of ≤5 (84 thymoma and 4 thymic carcinoma), the SUV max /TS values of thymic carcinoma were still significantly higher than those of thymoma (1.6 ± 0.8 vs. 0.6 ± 0.4, p < 0.01)., Conclusions: PET-CT might provide significant information for differentiating images of thymoma and thymic carcinoma. We experienced several cases of thymic carcinoma with a moderate SUV max of ≤5, and SUV max /TS was considered a useful parameter for differentiating such cases., (© 2022 The Authors. Thoracic Cancer published by China Lung Oncology Group and John Wiley & Sons Australia, Ltd.)

Objectives: Our goal was to investigate the correlation of clinical and computed tomography (CT) features of thymic epithelial tumours (TET) with the World Health Organization classification and the Masaoka-Koga staging system., Methods: Clinical and CT imaging data from 159 patients surgically and pathologically diagnosed with TET (82 men, 77 women; mean [± standard deviation] age, 52.08 ± 11.76 years) were retrospectively collected and reviewed. CT features were evaluated by radiologists. Tumour size, morphology, margin, density, calcification, cystic necrosis, density of the fat layer around the tumour, invasion of surrounding tissues, mediastinal lymph node enlargement, pleural/pericardial effusion, metastasis, plain CT scans and enhanced CT values were analysed., Results: Of the 159 patients with TET, 76 had low-risk thymoma, 55 had high-risk thymoma and 28 had thymic carcinomas. Age, maximum tumour diameter, myasthenia gravis, morphology, edges, density, fat around the lesion, mediastinal vascular, pericardial and lung tissue invasion, pleural/pericardial effusion, metastasis and arterial phase CT values were statistically different among the 3 groups (P < 0.05). Multivariate regression analysis revealed that edges, fat around the lesion, mediastinal vascular invasion and pericardial effusion were most relevant to TET classification. The 159 patients with TET were categorized into the non-invasion group (stage I; n = 58); the invasion of surrounding fat (stage II; n = 46); and the invasion of surrounding structures and metastasis group (stages III and IV; n = 55). Tumour diameter, morphology, margins, density, cystic degeneration and necrosis, invasion of surrounding fat and structure, pleural and pericardial effusion and lymph node enlargement were statistically different among the 3 groups (P < 0.05). Multivariate regression analysis revealed that edges, fat around the lesion, mediastinal vascular invasion and pleura invasion were the most relevant CT signs in relation to TET staging., Conclusions: Analysis of clinical and CT features before surgery may facilitate the preliminary classification and stage diagnosis of TET., (© The Author(s) 2021. Published by Oxford University Press on behalf of the European Association for Cardio-Thoracic Surgery. All rights reserved.)

Background: To validate a contrast-enhanced CT (CECT)-based radiomics model (RM) for differentiating various risk subgroups of thymic epithelial tumors (TETs)., Methods: A retrospective study was performed on 164 patients with TETs who underwent CECT scans before treatment. A total of 130 patients (approximately 79%, from 2012 to 2018) were designated as the training set, and 34 patients (approximately 21%, from 2019 to 2021) were designated as the testing set. The analysis of variance and least absolute shrinkage and selection operator algorithm methods were used to select the radiomics features. A logistic regression classifier was constructed to identify various subgroups of TETs. The predictive performance of RMs was evaluated based on receiver operating characteristic (ROC) curve analyses., Results: Two RMs included 16 and 13 radiomics features to identify three risk subgroups of traditional risk grouping [low-risk thymomas (LRT: Types A, AB and B1), high-risk thymomas (HRT: Types B2 and B3), thymic carcinoma (TC)] and improved risk grouping [LRT* (Types A and AB), HRT* (Types B1, B2 and B3), TC], respectively. For traditional risk grouping, the areas under the ROC curves (AUCs) of LRT, HRT, and TC were 0.795, 0.851, and 0.860, respectively, the accuracy was 0.65 in the training set, the AUCs were 0.621, 0.754, and 0.500, respectively, and the accuracy was 0.47 in the testing set. For improved risk grouping, the AUCs of LRT*, HRT*, and TC were 0.855, 0.862, and 0.869, respectively, and the accuracy was 0.72 in the training set; the AUCs were 0.778, 0.716, and 0.879, respectively, and the accuracy was 0.62 in the testing set., Conclusions: CECT-based RMs help to differentiate three risk subgroups of TETs, and RM established according to improved risk grouping performed better than traditional risk grouping., (© 2022. The Author(s).)

Objectives: For thymic epithelial tumors, simple contact with adjacent structures does not necessarily mean invasion. The purpose of our study was to develop a simple noninvasive technique for evaluating organ invasion using routine pretreatment computed tomography (CT)., Methods: This retrospective study analyzed the pathological reports on 95 mediastinal resections performed between January 2003 and June 2020. Using CT images, the length of the interface between the primary tumor and neighboring structures (arch distance; Adist) and maximum tumor diameter (Dmax) was measured, after which Adist/Dmax (A/D) ratios were calculated. Receiver operating characteristic (ROC) curves were used to analyze the Adist and A/D ratios., Results: An Adist cut-off of 37.5 mm best distinguished between invaded and non-invaded mediastinal great veins based on ROC curves. When Adist > 37.5 mm was used for diagnosis of invasion of the brachiocephalic vein (BCV) or superior vena cava (SVC), the sensitivity, specificity, positive predictive value, negative predictive value, accuracy, and area under the ROC curve for diagnosis of invasion were 61.9%, 92.5%, 81.25%, 82.2%, 81.97%, and 0.76429, respectively. Moreover, there were significant differences between BCV/SVC Adist > 37.5 mm and ≤ 37.5 mm for 10-year relapse-free survival and 10-year overall survival (p < 0.01)., Conclusions: When diagnosing invasion of the mediastinal great veins based on Adist > 37.5 mm, we achieved a higher performance level than the conventional criteria such as irregular interface with an absence of the fat layer. Measurement of Adist is a simple noninvasive technique for evaluating invasion using CT. Key Points • Simple contact between the primary tumor and adjacent structures on CT does not indicate direct invasion. • Using CT images, the length of the interface between the primary tumor and neighboring structures (arch distance; Adist) is a simple noninvasive technique for evaluating invasion. • Adist > 37.5 mm can be a supportive tool to identify invaded mediastinal great veins and surgical indications for T3 and T4 invasion by thymic epithelial tumors., (© 2021. European Society of Radiology.)

Background: To explore the performance of a computed tomography based radiomics model in the preoperative prediction of resectability status and TNM staging in thymic epithelial tumors., Methods: We reviewed the last preoperative computed tomography scan of patients with thymic epithelial tumors prior to resection and pathology evaluation at our institution between February 2008 and June 2019. A total of 101 quantitative features were extracted and a radiomics model was trained using elastic net penalized logistic regressions for each aim. In the set-aside testing sets, discriminating performance of each model was assessed with area under receiver operating characteristic curve., Results: Our final population consisted of 243 patients with: 153 (87%) thymomas, 23 (9%) thymic carcinomas, and 9 (4%) thymic carcinoids. Incomplete resections (R1 or R2) occurred in 38 (16%) patients, and 67 (28%) patients had more advanced stage tumors (stage III or IV). In the set-aside testing sets, the radiomics model achieved good performance in preoperatively predicting incomplete resections (area under receiver operating characteristic curve: 0.80) and advanced stage tumors (area under receiver operating characteristic curve: 0.70)., Conclusions: Our computed tomography radiomics model achieved good performance to predict resectability status and staging in thymic epithelial tumors, suggesting a potential value for the evaluation of radiomic features in the preoperative prediction of surgical outcomes in thymic malignancies., (Copyright © 2022 The Society of Thoracic Surgeons. Published by Elsevier Inc. All rights reserved.)

Objectives: We aimed to evaluate the diagnostic ability for the prediction of histologic grades and prognostic values on recurrence and death of pretreatment 2-[ 18 F]FDG PET/CT in patients with resectable thymic epithelial tumours (TETs)., Methods: One hundred and fourteen patients with TETs who underwent pretreatment 2-[ 18 F]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., Results: 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., Conclusion: SUVmax showed excellent diagnostic performance for prediction of TC and significant prognostic value in terms of recurrence and survival., Key Points: • 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., (© 2021. European Society of Radiology.)

Aim: To explore the utility of multiple energy spectrum computed tomography (CT) parameters in distinguishing thymic epithelial tumours (TETs) from thymic cysts among lesions <5 cm in diameter., Materials and Methods: Data pertaining to 56 patients with TETs and thymic cysts <5 cm in diameter were assessed retrospectively. All patients underwent surgical resection and the diagnosis was confirmed histopathologically. Thirty-five patients with TETs (average age, 51.97 years) and 21 patients with thymic cysts (average age, 50.54 years) were included. The region of interest for the lesion on the energy spectrum CT was delineated on the post-processing workstation, and multiple parameters of the energy spectrum CT were obtained. The diagnostic efficacies of the parameters were analysed using receiver operating characteristic (ROC) curves., Results: To distinguish small TETs from thymic cysts, a single-energy CT value of 60 keV showed good differential diagnostic performance in the arterial phase (cut-off value = 68.42 HU; area under the curve [AUC] = 0.978), a single-energy CT value of 70 keV showed good differential diagnostic performance in the venous phase (cut-off value = 59.77 HU; AUC = 0.956). In the arterial and venous phases, effective atomic numbers of 8.065 and 8.175, respectively, were used as cut-off values to distinguish small TETs from thymic cysts (AUC = 0.972 and AUC = 0.961, respectively). Iodine concentrations of 10.99 and 11.05 were used as cut-off values to distinguish small TETs from thymic cysts (AUC = 0.956 and AUC = 0.924, respectively)., Conclusion: According to the present study, energy spectrum CT parameters may have clinical value in the differential diagnosis of TETs and thymic cysts., (Copyright © 2021 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.)

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., Competing Interests: Conflicts of interest and sources of funding: none declared., (Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.)

Objectives: To evaluate CT-derived radiomics for machine learning-based classification of thymic epithelial tumor (TET) stage (TNM classification), histology (WHO classification) and the presence of myasthenia gravis (MG)., Methods: Patients with histologically confirmed TET in the years 2000-2018 were retrospectively included, excluding patients with incompatible imaging or other tumors. CT scans were reformatted uniformly, gray values were normalized and discretized. Tumors were segmented manually; 15 scans were re-segmented after 2 weeks by two readers. 1316 radiomic features were calculated (pyRadiomics). Features with low intra-/inter-reader agreement (ICC<0.75) were excluded. Repeated nested cross-validation was used for feature selection (Boruta algorithm), model training, and evaluation (out-of-fold predictions). Shapley additive explanation (SHAP) values were calculated to assess feature importance., Results: 105 patients undergoing surgery for TET were identified. After applying exclusion criteria, 62 patients (28 female; mean age, 57±14 years; range, 22-82 years) with 34 low-risk TET (LRT; WHO types A/AB/B1), 28 high-risk TET (HRT; WHO B2/B3/C) in early stage (49, TNM stage I-II) or advanced stage (13, TNM III-IV) were included. 14(23%) of the patients had MG. 334(25%) features were excluded after intra-/inter-reader analysis. Discriminatory performance of the random forest classifiers was good for histology(AUC, 87.6%; 95% confidence interval, 76.3-94.3) and TNM stage(AUC, 83.8%; 95%CI, 66.9-93.4) but poor for the prediction of MG (AUC, 63.9%; 95%CI, 44.8-79.5)., Conclusions: CT-derived radiomic features may be a useful imaging biomarker for TET histology and TNM stage., Competing Interests: The authors have declared that no competing interests exist.

Abstract: Although differentiating benign and malignant thymic epithelial lesions is important to avoid unnecessary treatment and predict prognosis, it is challenging because of overlaps in the chest computed tomography (CT) findings. In this study, we investigated whether the diameter of the thymic vein and other CT findings could differentiate between benign (thymoma and thymic cysts) and malignant (thymic carcinoma, [TCa]) lesions.We conducted a retrospective study across two tertiary referral hospitals in Japan between November 2009 and June 2018. We included 12 patients with TCa, 34 patients with thymomas, and 17 patients with thymic cysts. We analyzed the receiver operating characteristic (ROC) curve to determine the best cut-off values and performed univariate and multivariate analyses of CT findings to distinguish TCa from other benign lesions. Post-hoc analysis was performed for the maximum short axis of the thymic vein using the Mann-Whitney U test, and the number of the maximum short axis of the thymic vein ≥ the cutoff was determined using the Fisher exact test with a family-wise error-correction using Bonferroni's method.ROC analysis showed that a maximum short axis of the thymic vein ≥2 mm was considerably more frequent in TCa than in the other lesions (P < .001 for both), with 83% sensitivity and 86% specificity. Univariate and multivariate analyses revealed the association with TCa of the number of the maximum short axis of the thymic vein ≥2 mm (P = .005, multivariate generalized linear model analysis), ill-defined margin (P = .001), and mediastinal lymphadenopathy (P < .001). Thymic vein diameter was in descendimg order of TCa > thymoma > thymic cysts with statistically significant differences between the groups (Ps < .05).Thymic vein diameter was significantly longer in TCa than in thymoma and thymic cysts. Measurement of the maximum short axis of the thymic vein could be a powerful diagnostic tool to differentiate TCa from thymoma and thymic cysts., Competing Interests: The authors have no funding and conflicts of interests to disclose., (Copyright © 2021 the Author(s). Published by Wolters Kluwer Health, Inc.)

Background and Aim: Although the frequency of endoscopic diagnosis of superficial non-ampullary duodenal epithelial tumors (SNADETs) has been increasing in recent years, no criteria for the endoscopic diagnosis of these tumors have been established yet. The aim of this study was to assess the usefulness of endocytoscopy for diagnosis SNADETs and to establish new criteria., Methods: This prospective study was conducted at the NTT Medical Center Tokyo from May 2019 to July 2020, and a total of 100 consecutive SNADETs were enrolled. All the endocytoscopic images of the lesions and surrounding normal mucosa were classified into three groups according to the degree of structural atypia and the nuclear morphology and size. The endocytoscopic diagnoses using endocytoscopic classification was compared with the final histopathological diagnoses., Results: Data of 93 patients with 98 lesions were included in the analysis. The preoperative diagnosis by endocytoscopy coincided with the final histopathological diagnosis in 85 (86.7%) of 98 SNADETs. In addition, the sensitivity and specificity for VCL 4/5 were 87.7% and 85.4%, respectively. In contrast, the accuracy, sensitivity, and specificity of preoperative diagnosis by biopsy were 64.3%, 50.9%, and 82.9%, respectively. Preoperative diagnosis by endocytoscopy showed significantly superior accuracy and sensitivity as compared with preoperative biopsy diagnosis (P < 0.001, respectively)., Conclusions: This new classification (endocytoscopic classification) allows prediction of the tumor histopathology in real time, during endocytoscopy without biopsy, and is expected to be of help in determining the appropriate therapeutic strategies for individual cases of SNADETs. (Clinical trial registration number: UMIN000038643.)., (© 2021 Journal of Gastroenterology and Hepatology Foundation and John Wiley & Sons Australia, Ltd.)