MHD-Net: Memory-Aware Hetero-Modal Distillation Network for Thymic Epithelial Tumor Typing With Missing Pathology Modality.

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.

CT-guided pretreatment biopsy diagnosis in patients with thymic epithelial tumours: diagnostic accuracy and risk of seeding.

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.

Computed tomography radiomic feature analysis of thymic epithelial tumors: Differentiation of thymic epithelial tumors from thymic cysts and prediction of histological subtypes.

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_Contrast, GLCM_Correlation, GLZLM_SZLGE, DISCRETIZED_HISTO_Entropy_log2, and DISCRETIZED_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.

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

BACKGROUND: Preoperative fluorine-18-fluorodeoxyglucose positron emission tomography (18F-FDG PET) of thymic epithelial tumors (TETs) is well known for identifying malignant-grade TETs; however, its predictive power for determining locally advanced tumors, lymph node (LN) metastasis, and prognosis remains unknown. PATIENTS AND METHODS: We retrospectively evaluated patients with resectable TETs who were preoperatively assessed using 18F-FDG PET from January 2012 to January 2023. The receiver operating characteristic curve was used to evaluate the cutoff value of the maximum standardized uptake value (SUVmax) to predict advanced-stage disease. Recurrence/progression-free survival (RFS/PFS) was analyzed using the Kaplan-Meier method. The staging was classified according to the tumor-node-metastasis system. RESULTS: Our study included 177 patients; 145 (81.9%) had pathological early-stage TET (stage I or II), and 32 (19.1%) had advanced stage (stage III or IV). The area under the curve value for predicting the advanced stage was 0.903, and the cutoff value was 5.6 (sensitivity 81.3%, specificity 84.8%). SUVmax > 5.6 was associated with worse prognosis for RFS/PFS. LN metastasis was preoperatively detected by FDG uptake in 30.8% of patients with pathological LN positivity, whereas LN metastasis was not pathologically detected in patients with SUVmax < 5.9. In patients with advanced-stage TETs, LN recurrence was more frequent in patients who were preoperatively detected by 18F-FDG PET than those who were not (75.0% versus 7.1%). CONCLUSIONS: 18F-FDG PET is a potentially valuable tool for predicting advanced stage and poor prognosis of recurrence in patients with TETs. SUVmax can help thoracic surgeons to guide them in selecting appropriate therapeutic strategies for TETs.

Quantitative CT parameters combined with preoperative systemic inflammatory markers for differentiating risk subgroups of thymic epithelial tumors.

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 (CEmax) 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.

Deep learning-based radiomic nomogram to predict risk categorization of thymic epithelial tumors: A multicenter study.

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_Sig], deep learning signature [DL_Sig] and deep learning radiomics signature [DLR_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_Sig demonstrated optimum performance with an AUC of 0.883 for the derivation cohort and 0.749 for the external test cohort. Combining DLR_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.

Diagnostic performance of radiomics model for preoperative risk categorization in thymic epithelial tumors: a systematic review and meta-analysis.

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.

The maximal contrast-enhanced range of CT for differentiating the WHO pathological subtypes and risk subgroups of thymic epithelial tumors.

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.

Radiomics Analysis of Multiphasic Computed Tomography Images for Distinguishing High-Risk Thymic Epithelial Tumors From Low-Risk Thymic Epithelial Tumors.

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.

Peritumoral imaging features of thymic epithelial tumors for the prediction of transcapsular invasion: beyond intratumoral analysis

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.

Prognostic CT features in patients with untreated thymic epithelial tumors.

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.

Frequency of thoracic recurrence based on pathological features in patients with ovarian epithelial tumors in stage I versus higher stages.

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.

Feasibility of iodine concentration and extracellular volume fraction measurement derived from the equilibrium phase dual-energy CT for differentiating thymic epithelial tumors.

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.

The value of diffusion-weighted and dynamic contrast-enhanced imaging in the diagnosis of thymic epithelial tumors.

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 mm2/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 mm2/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.

Diagnostic value of apparent diffusion coefficient in predicting pathological T stage in patients with thymic epithelial tumor.

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/mm2. 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 mm2/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.

A novel predictive model for distinguishing mediastinal lymphomas from thymic epithelial tumours.

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.

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

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.

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

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.

Usefulness of positron-emission tomography for predicting the World Health Organization grade of thymic epithelial tumors.

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 (SUVmax ) 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 SUVmax 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 SUVmax to the World Health Organization (WHO) histological type was assessed. To reduce the impact of the TS, the ratio of the SUVmax to the TS was also investigated. RESULTS: A total of 99 thymoma cases and 30 thymic carcinoma cases were enrolled into the study. The SUVmax and SUVmax /TS of thymic carcinoma were significantly higher than those of thymoma (SUVmax : 7.7 ± 3.4 vs. 3.3 ± 1.3, p < 0.01; SUVmax /TS: 1.5 ± 0.7 vs. 0.6 ± 0.4, p < 0.01). Focusing on the patients with a moderate SUVmax of ≤5 (84 thymoma and 4 thymic carcinoma), the SUVmax /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 SUVmax of ≤5, and SUVmax /TS was considered a useful parameter for differentiating such cases.