A Multiparameter Diagnostic Model Based on MRI Volumetric ADC Histogram and Clinical Variables Accurately Differentiate Thymic Epithelial Tumors From Mediastinal Lymphomas

A Multiparameter Diagnostic Model Based on MRI Volumetric ADC Histogram and Clinical Variables Accurately Differentiate Thymic Epithelial Tumors From Mediastinal Lymphomas | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Multiparameter Diagnostic Model Based on MRI Volumetric ADC Histogram and Clinical Variables Accurately Differentiate Thymic Epithelial Tumors From Mediastinal Lymphomas Luna Wang, Huiyuan Zhu, Yu Zhang, Yan Shen, Lin Zhu, Hong Yu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7688743/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 Jan, 2026 Read the published version in BMC Medical Imaging → Version 1 posted 11 You are reading this latest preprint version Abstract Background The management and prognosis of each type of anterior mediastinal mass differ substantially. Radical thymectomy is regarded as the preferred surgical approach for resectable TETs, whereas chemotherapy is the recommended treatment for mediastinal lymphoma after confirming the histological diagnosis through needle biopsy, and surgical procedures should be avoided. Consequently, an accurate diagnosis of mediastinal lymphoma and TETs holds paramount importance in clinical treatment and prognosis for patients with thymic neoplasms. Methods Patients of TETs and mediastinal lymphomas with histopathological proof were included in the present study. The ADC histogram parameters were extracted from ADC maps. Clinical characteristics, radiological features and ADC histogram metrics (incluning ADCmin, ADCmax, and ADCmean; 5th, 10th, 25th, 50th, 75th, 90th and 95th percentiles of ADC; skewness and kurtosis) were evaluated between two groups. Multivariate analyses were performed to identify the significant variables, which were then incorporated into a comprehensive diagnostic model. Receiver operator characteristics (ROC) curve analysis was subsequently carried out to evaluate diagnostic performance. A nomogram was developed to differentiate TETs and mediastinal lymphomas. Results A total of 130 consecutive patients, with 93 TET patients and 37 mediastinal lymphoma patients, were enrolled. It was observed that patients with mediastinal lymphomas exhibited a significantly younger age (38.11 ± 13.51 years vs. 53.66 ± 12.99 years, P < 0.001) and a significantly higher serum lactate dehydrogenase (LDH) elevation rate (54.1% vs. 2.2%, P < 0.001) compared to those with TETs. Furthermore, the maximal diameter of lesions and skewness were significantly larger in patients with mediastinal lymphoma, whereas 25th -95th percentile of ADC, ADCmax and ADCmean were significantly smaller compared to patients with TETs (all P < 0.05). The comprehensive diagnostic model was established based on forward stepwise regression, including age, serum LDH level and skewness, with higher AUC than skewness alone (0.914, 95%CI: 0.850–0.977 vs. 0.785, 95%CI: 0.701–0.869, P < 0.01). The predictive C-index nomogram performance was 0.917 (95%CI: 0.915–0.918). Conclusion The comprehensive diagnostic model which takes into account both ADC histogram parameters and clinical characteristics showed a promising value in the differential diagnosis of TETs and mediastinal lymphomas. Thymic epithelial tumors Mediastinal lymphoma Magnetic resonance imaging Diffusion weighted imaging Histogram analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Background Mediastinal neoplasms are the most frequently encountered primary tumors in the anterior mediastinum and mainly contain thymic epithelial tumors (TETs) (including thymomas, thymic carcinomas, thymic neuroendocrine neoplasms), lymphomas, and germ cell tumors (GCTs) and others [ 1 ]. The management and prognosis of each type of anterior mediastinal mass differ substantially [ 2 , 3 ]. Radical thymectomy is regarded as the preferred surgical approach for resectable TETs, whereas chemotherapy is the recommended treatment for mediastinal lymphoma after confirming the histological diagnosis through needle biopsy, and surgical procedures should be avoided [ 4 , 5 ]. Furthermore, it is recommended to refrain from performing transpleural biopsy in cases of suspected TETs due to the significant potential of advancing the stage of thymoma from stage I to IV thymoma by disseminating the tumor within the pleural space [ 6 ]. Consequently, an accurate diagnosis of mediastinal lymphoma and TETs holds paramount importance in clinical treatment and prognosis for patients with thymic neoplasms. Magnetic resonance imaging (MRI), being a crucial diagnostic tool, is predominantly utilized for presumptive diagnosis, evaluation, and management of thymic neoplasms [ 7 – 10 ]. Previous studies have demonstrated that MRI can offer more comprehensive information compared to computed tomography (CT) when assessing a mediastinal mass [ 10 , 11 ]. There were considerable overlaps between the CT and MRI findings of these diseases, it was reported that even when performed by experienced chest radiologists, the diagnostic accuracy of CT for differentiating mediastinal tumors was 61%, while the combination of CT and MRI improved the diagnostic accuracy up to 67%, leaving much room for improvements in the era of advanced technology [ 12 ]. Diffusion-weighted imaging (DWI) is a widely recognized functional imaging technique in MRI that allows for the non-invasive evaluation of tissue microstructure by detecting the diffusion movement of water molecules within living tissues. This technique also provides a wealth of texture information through the automatically generated apparent diffusion coefficient (ADC) [ 13 ]. Recently, more researchers utilized ADC histogram analysis to distinguish between different pathological subtypes and stages [ 14 – 17 ], and even to predict genotyping and prognosis prediction [ 18 , 19 ]. However, the research revealed that the rates of unnecessary thymectomies and invasive surgeries of mediastinal lymphoma were relatively high (12.4%-52.3%) due to a lack of knowledge regarding its distinct radiological characteristics [ 20 , 21 ]. Moreover, limited research has been conducted on the utilization of ADC histogram in distinguishing between TETs and mediastinal lymphomas, primarily due to the rarity of mediastinal neoplasms [ 11 ]. Therefore, the primary objective of our study was to ascertain the differentiating potential of ADC histogram parameters in TETs and mediastinal lymphomas. Furthermore, we aimed to develop a comprehensive diagnostic model by combining ADC histogram metrics with clinical variables to further verify a more effective differentiation of TETs from mediastinal lymphomas. Methods Study design The present retrospective study was approved by the institutional review board, and the requirement for informed consent was waived due to the retrospective design and lack of interventions, the committee concluded that the Medical Research Involving Human Subjects Act (WMO) did not apply. The present study retrospectively enrolled patients from January 2019 to December 2021, pathologically diagnosed with mediastinal lymphomas, and subsequently underwent an MRI examination. Meanwhile, we collected patients with pathologically diagnosed TETs from January 2019 to December 2019. The inclusion criteria were as follows: (1) patients underwent MRI within 2 weeks before treatment; (2) the diagnosis of primary thymic tumors was confirmed by biopsy or surgical resection pathology; (3) complete clinical data. The exclusion criteria were as follows: (1) treatment received before MRI examination; (2) with other malignant tumors and tumor recurrence; (3) MRI with non-standard b values (0, 800 s/mm 2 ); (4) A significant artifact and image quality of DWI were not adequate for processing; (5) lesions that were predominantly cystic necrosis, resulting in an inability to accurately measure ADC values; (6) lesions < 2 cm have fewer than 3 layers on DWI axial scan and a volume of interest (VOI) for the lesion cannot be obtained. Initially, 274 and 84 patients with TETs and mediastinal lymphomas were included. Following the application of our exclusion criteria, 181and 47 patients were excluded, and finally, a total of 93 patients with TETs and 37 patients with mediastinal lymphomas were recruited in the present trial (Figure 1). Magnetic resonance imaging (MRI) All MRI examinations were performed with a 3.0T MR scanner (Ingenia 3.0T, Philips Healthcare, Best, the Netherlands) utilizing a 16-channel Torso coil within 2 weeks before treatment. The routine MR sequences included axial gradient echo T1-weighted (T1WI), axial and coronal turbo spin-echo T2-weighted (T2W) imaging. MR images were obtained during end-inspiration breath-holding. The DWI sequence slice locations corresponded to those from the T2-weighted images to provide anatomic reference. For dynamic contrast-enhanced T1-weight imaging (DCE-T1WI), the contrast agent (gadodiamide, 0.5mmol/ml, GE Healthcare) was administrated intravenously at a dose of 2.0 ml/kg body weight, followed by 20 ml of saline flush to clear the tube at an injection rate of 4.0 ml/s. MRI sequences and parameters are detailed in Appendix Table 1. MRI Image assessment MRI images were reviewed strictly separately by two radiologists in a blinded fashion (WLN with 8 and SY with 25 years of experience, respectively). One radiologist (WLN) evaluated all the 130 patients (numbered 1 to 130), and another radiologist (SY) analyzed a group of patients (n = 26, with identification numbers ending in 0 or 5), they were blinded to clinical information and pathology. ADC histogram metrics were generated with the FireVoxel software (v. Build 432, https://www.firevoxel.org/). For each patient, T1WI, T2WI, T2WI-SPAIR, DCE-T1WI and DWI images (with b values of 0 and 800 s/mm 2 ) were separately imported into FireVoxel software as a single file. Regions of interest (ROIs) were drawn along the tumor border on all slices of DWI scans ( b value = 800 s/mm 2 ) with the reference to conventional MR images (T1WI, T2WI and DCE-T1WI) or CT images. If calcification, obvious necrosis, cystic lesions or hemorrhage were present in the tumor, the ROI was delineated only around the solid parenchymal areas. The calcification areas were defined as hyperdense area on non-enhanced CT. The necrosis and cystic areas were defined as areas with lower signal intensity on T1WI, higher signal intensity on T2WI and no enhancement on DCE images. CT sequences and parameters are detailed in Appendix Note 1. The VOI was then constructed based on all ROI slices. The following parameters were obtained: 5 th percentile, 10 th percentile, 25 th percentile, 50 th percentile, 75 th percentile, 90 th percentil, 95 th percentile, skewness, and kurtosis. Conventional radiological features and interpretation of radiologists Two readers (WLN and SY), who were blinded to the patients’ histopathologic data, independently reviewed the conventional images. The following radiological features were evaluated: (1) maximum diameter; (2) calcification; (3) necrosis and cyst; (4) contour: smooth or lobulated, the details were in Appendix Note2. Then two readers give common results for each patient according to conventional images. Statistical analyses Statistical analyses were performed with R v. 4.2.2 (Chicago, IL, USA) and IBM SPSS Statistic v.25. Qualitative data were described as number of cases and percentage [n (%)] for categorical variables. Continuous variables were presented as mean ± standard deviation (SD) (normal distribution) or median and interquartile range (non-normal distribution). The differences in ADC parameters, age, maximal diameter between TETs and mediastinal lymphomas were compared using the student’s-test (normal distribution) or the Mann-Whitney U test (non-normal distribution). Categorical variables were compared using Fisher’s exact test. Inter-observer agreement was evaluated by κ coefficients for categorical features, and the intraclass correlation coefficients (ICC) for continuous variables, which was interpreted as follows: 0-0.2, poor; 0.2-0.4, fair; 0.4-0.6, moderate; 0.6-0.8, good; 0.8-1.0, excellent. Areas under the curve (AUC) of receiver operator characteristics (ROC) were determined to assess the diagnostic performance of various ADC histogram metrics. The sensitivity, specificity and accuracy (ACC) were calculated. The parameters of single factor analysis P 10 indicating the presence of multicollinearity, and such parameters were excluded from the model. None of the interactions we tested were significant, hence, they are not discussed further in this article. Multivariate logistic regression analysis was performed using forward stepwise approach to identify the independent predictor to further construct a comprehensive diagnostic model for distinguishing TETs from mediastinal lymphomas. The bootstrap test integrated discriminatory improvement (IDI) was calculated for comparison of the diagnostic models ( PredictABEL package). Decision curve analyses (DCA) evaluated the clinical utility and accuracy of the comprehensive diagnostic model by calculating the net benefits for a range of threshold probabilities in ADC histogram parameters with the highest AUC ( rmda package). A nomogram was built using the comprehensive diagnostic model to serve as a graphical representation of results ( rms package). The nomogram’s predictive performance was measured using Harrell’s C-index and calibration with 2000 bootstrap samples to decrease the overfit bias. Calibration curves were calculated by the regression analysis and bootstraps of 1000 resamples were set ( rms package). P < 0.05 was considered statistically significant. Results Participant Characteristics Out of 358 patients with pathologically confirmed TETs and mediastinal lymphomas, a total of 130 patients (36.3%) met the inclusion criteria of this study. Specifically, 93 TETs and 37 mediastinal lymphomas were included in the present study. Based on postoperative pathological proof, 57cases (61.3%) were low-risk thymomas, 20 cases (21.5%) were high-risk thymomas, and 16 cases (17.2%) were thymic carcinomas of TETs. The specific number and proportion of pathological subtypes are shown in Table 1. In the present study, diffuse large B-cell lymphoma had the highest incidence among the patients with lymphoma (54.1%). The proportion of Hodgkin lymphoma and T-lymphoblastic lymphoma were 35.1% and 10.8%, respectively (Table 1). Table 1. Pathological diagnoses of patients. Type Cases/Percentage TETs (93) Low-risk thymom a 57 (61.3%) Type A thymoma 6 (6.5%) Type AB thymoma 36 (38.7%) Type B1 thymoma 15 (16.1%) High-risk thymoma 20 (21.5%) Type B2 thymoma 12 (12.9%) Type B3 thymoma 8 (8.6%) Thymic carcinoma 16 (17.2%) Squamous cell carcinoma 13 (14.0%) others 3 (3.2%) Mediastinal lymphoma (37) Diffuse large B-cell lymphoma 20 (54.1%) Hodgkin lymphoma 13 (35.1%) T-lymphoblastic lymphoma 4 (10.8%) TETs, thymic epithelial tumors. Clinical variables including age, sex and symptoms of included participants were statistically analyzed, and our data showed that the onset age of mediastinal lymphoma is significantly lower than that of TETs (38.11 ± 13.51 years vs. 53.66 ± 12.99 years, P < 0.001). Furthermore, notable differences were observed in symptom prevalence, including a significantly higher proportion of asymptomatic patients in the TET group (82.8% vs. 62.2%, P < 0.01) and a markedly higher incidence of cough in the mediastinal lymphoma group (18.9% vs. 3.2%, P < 0.001). No statistically significant differences were identified in sex distribution (62.2% female in lymphoma vs. 49.5% in TETs, P = 0.190), symptoms such as chest distress (2.7% vs. 1.1%, P = 0.49), chest pain (13.5% vs. 5.4%, P = 0.147), myasthenia gravis (MG) (0% vs. 5.4%, P = 0.321), B symptoms (2.7% vs. 0%, P = 0.285), or other symptoms (0% vs. 2.2%, P = 1.000). Regarding laboratory findings, a substantially greater proportion of mediastinal lymphoma patients exhibited elevated serum lactate dehydrogenase (LDH) levels (>250 U/L) compared to TET patients (54.1% vs. 2.2%, P < 0.001) (Table 2). Table 2. Baseline characteristics and clinical variables. Characteristic TETs (n=93 ) Mediastinal lymphoma (n= 37) P -value Age (y) 53.66 ± 12.99 38.11 ± 13.51 <0.001 *** Male : Female (n, %) 46 (49.5%) 23 (62.2%) 0.190 Symptom <0.01 ** Asymptomatic 77 (82.8%) 23 (62.2%) <0.01 ** Cough 3 (3.2%) 7 (18.9%) 250U/L) 2 (2.2%) 20 (54.1%) <0.001 *** Radiological data Max-diameter (cm) 4.60 (3.50-6.70) 7.30 (5.20-10.60) <0.001 *** Calcification 21 (22.6%) 0 (0.0%) <0.01 ** Necrosis 26 (28.0%) 19 (51.4%) <0.05 * Contour 0.194 Smooth 28 (30.11%) 7 (18.92%) Lobulated 65 (69.89%) 30 (81.08%) TETs, thymic epithelial tumors; MG, myasthenia gravis; LDH, Lactate dehydrogenase. * P < 0.05, ** P < 0.01, *** P < 0.001. Regarding MRI morphological features, calcification, necrosis and contour (such as smooth and lobulated) were evaluated by two readers with κ coefficients in the range of 0.943-1 (Appendix Table 2). It was found that the maximal diameter of TETs was significantly smaller than that of mediastinal lymphomas (4.60 cm [3.50 – 6.70 cm] vs. 7.30 cm [5.20 – 10.60cm]), P < 0.001). Calcification was more common in TETs (22.6% vs. 0%, P < 0.05), while necrosis was significantly less common (28.0% vs. 51.4%, P < 0.05) compared to mediastinal lymphomas. There was no statistical difference in the incidence of different lesion forms between TETs and mediastinal lymphomas ( P = 0.198) (Table 2). The MRI ADC histogram Parameters The inter-observer agreements were good to excellent for the measurements of histogram parameters, with ICCs raging from 0.870-0.998 (Appendix Table 2). Consequently, only the measurements from Radiologist 1 were utilized for further analyses. The ADC histogram parameters were compared between patients with TETs and mediastinal lymphomas (Figure 2). The results demonstrated that the ADC histogram parameters, including 25 th percentile ( P < 0.01), 50 th percentile ( P < 0.001), 75 th percentile ( P < 0.001), 90 th percentil ( P < 0.05), 95 th percentile ( P < 0.01), ADCmax ( P < 0.05), ADCmean ( P < 0.01) of patients with mediastinal lymphomas were significantly lower than those of patients with TETs, while the skewness of patients with mediastinal lymphomas were statistically higher than that of TETs ( P < 0.001). No statistically significant differences were observed between the groups for the 5 th percentile, 10 th percentile, ADCmin, or kurtosis parameters. The complete dataset is presented in Table 3. Table 3. Comparison of ADC histogram parameters between TETs and mediastinal lymphomas. ADC histogram parameters TETs (n=93) Mediastinal lymphoma (n=37) P valve 5 th percentile ADC 0.785 (0.573-1.131) 0.690 (0.568-0.966) 0.430 10 th percentile ADC 1.014 (0.711-1.331) 0.765 (0.689-1.083) 0.095 25 th percentile ADC 1.217 (0.964-1.712) 0.981 (0.811-1.297) <0.01 ** 50 th percentile ADC 1.598 (1.178-2.003) 1.157 (0.959-1.582) <0.001 *** 75 th percentile ADC 1.867 (1.478-2.316) 1.437 (1.130-1.889) <0.001 *** 90 th percentile ADC 2.117(1.710-2.589) 1.716 (1.366-2.243) <0.05 * 95th percentile ADC 2.378(1.850-2.775) 1.890 (1.507-2.432) <0.01** ADCmin 0.246 ± 0.404 0.266 ± 0.340 0.790 ADCmax 3.123 ± 0.772 2.830 ± 0.675 <0.05* ADCmean 1.562(1.199-1.983) 1.168(1.020-1.621) <0.01 ** Skewness 0.029 ± 0.647 0.684 ± 0.630 <0.001 *** Kurtosis 0.780 ± 1.437 1.376 ± 1.813 0.050 TETs, thymic epithelial tumors. * P < 0.05, ** P < 0.01, *** P < 0.001 Date is mean ± standard deviation (normal distribution) or median and interquartile range in parentheses (non-normal distribution)” under table 3. Diagnostic performance of the comprehensive diagnostic model We evaluated the diagnostic efficacy of individual indicators such as the 25 th percentile, 50 th percentile, 75 th percentile, 90 th percentile, 95 th percentile, ADCmax, ADCmean and skewness of the ADC histogram. The ROC curve indicated that the skewness exhibited the highest diagnostic ability among the ADC parameters with acut-off value of 0.014, and the AUC was 0.785 (95%CI: 0.701-0.869), the sensitivity was 0.946 (95%CI: 0.805-0.991), the specificity was 0.559 (95%CI: 0.453-0.661), and the accuracy was 0.669 (95% CI: 0.581-0.749). From the multivariate analysis, the age (odds ratio (OR) = 0.911; 95% CI: 0.868, 0.956; P 250U/L) (OR = 84.427; 95% CI: 10.891, 623.837; P < 0.001), and skewness of ADC histogram (OR = 2.879; 95% CI: 1.104, 7.508; P = 0.031) of the mediastinal mass were independent significant variables associated with lymphomas. The comprehensive diagnostic model which combined age, serum LDH (> 250U/L) and skewness demonstrated that the AUC was 0.914 (95%CI: 0.850-0.977), the sensitivity was 0.865 (95%CI: 0.704-0.949), the specificity was 0.914 (95%CI: 0.833-0.959), and the accuracy was 0.900 (95% CI: 0.835-0.946) (Figure 3). The diagnostic performance of individual parameters and the comprehensive diagnostic model are shown in Table 4. Table 4. Differential diagnostic efficiency of ADC histogram parameters and clinical variables and combined between TETs and mediastinal lymphomas. Parameters Cut-off AUC (95%CI) Sensitivity (95%CI) Specificity (95%CI) ACC 25 th percentile ADC 0.853 0.668 (0.535-0.732) 0.459 (0.299-0.629) 0.828 (0.733-0.896) 0.723 (0.638-0.798) 50 th percentile ADC 1.804 0.701 (0.605-0.797) 0.919 (0.770-0.979) 0.398 (0.299-0.505) 0.546 (0.457-0.634) 75 th percentile ADC 1.522 0.703 (0.607-0.799) 0.595 (0.422-0.748) 0.742 (0.639-0.825) 0.700 (0.613-0.777) 90 th percentile ADC 1.757 0.693 (0.596-0.791) 0.595 (0.422-0.748) 0.742 (0.639-0.825) 0.700 (0.613-0.777) 95 th percentile ADC 2.632 0.678 (0.579-0.777) 0.946 (0.805-0.991) 0.355 (0.260-0.462) 0.523 (0.434-0.611) ADCmax 2.755 0.621 (0.516-0.726) 0.568 (0.396-0.725) 0.667 (0.560-0.759) 0.638 (0.550-0.721) ADCmean 1.260 0.681 (0.582-0.780) 0.568 (0.396-0.725) 0.731 (0.627-0.815) 0.685 (0.597-0.763) Skewness 0.014 0.785 (0.701-0.869) 0.946 (0.805-0.991) 0.559 (0.453-0.661) 0.669 (0.581-0.749) Model - 0.914 (0.850-0.977) 0.865 (0.704-0.949) 0.914 (0.833-0.959) 0.900 (0.835-0.946) Model refers to a comprehensive diagnostic model, which combines age, serum LDH (> 250U/L), and skewness; CI, confidence interval; AUC, area under curve; ACC, accuracy The DeLong test showed that the AUC of the comprehensive diagnostic model was superior to that of skewness alone (Z = 2.862, P < 0.01). According to the bootstrap test, the comprehensive diagnostic model demonstrated a statistically significant improvement in ROC compared to considering skewness alone ( D = 2.903, boot. N = 2000; boot. Stratified = 1, P 250U/L) in the comprehensive diagnostic model led to a significantly better reclassification compared to skewness alone with an IDI of 0.370 (95%CI: 0.269-0.470, P 250U/L) and skewness for differentiating TETs from mediastinal lymphomas (Figure 4). Satisfactory predictive performance of the nomogram was observed, with a C-index value of 0.917 (95%CI: 0.915-0.918). The calibration curve was used to assess the goodness of the fit of the nomogram, with a good agreement ( P < 0.001) (Figure 5). DCA showed that the comprehensive diagnostic model had higher overall net benefit than skewness nearly all risk thresholds, indicating its good clinical usefulness (Figure 6). Discussion Our study provided evidence that MRI histogram parameters, particularly skewness, have significant diagnostic value in distinguishing between TETs and mediastinal lymphoma (AUC = 0.785, 95%CI: 0.701–0.869). Additionally, the integration of ADC histogram metrics (skewness), clinical information (age) and the laboratory indicator (LDH > 250U/L) in a comprehensive diagnostic model (AUC = 0.914, 95%CI: 0.850–0.977) enables accurate differentiation between TETs and mediastinal lymphomas. The present study showed that certain clinical clues can aid in distinguishing between TETs and mediastinal lymphomas. Our data revealed that patients with mediastinal lymphoma were significantly younger than those with TETs, which aligns with the findings of Wang S et al [ 22 ]. Previous laboratory investigations have also identified valuable hematological markers, such as α-fetoprotein (α-FP), β-human chorionic gonadotropin (β- HCG), and LDH, which were consistent with our data [ 23 , 24 ]. Our results indicate that LDH plays a crucial role in distinguishing between TETs and mediastinal lymphomas, with 54.1% of patients with mediastinal lymphomas exhibiting LDH levels exceeding 250U/L. Furthermore, our univariate analyses revealed a significantly higher proportion of asymptomatic cases (82.8% vs . 62.2%, P < 0.01) and cough symptoms (3.2% vs .18.9%, P < 0.001) in mediastinal lymphomas compared to TETs, although these symptoms were not included in the final multivariate regression model. Besides, in our study, patients with myasthenia gravis (MG) were only proven to be TETs, while lymphoma patients only manifested B symptoms in our study, which is similar to the findings of Wang et al [ 22 ]. Statistical analysis indicates no difference in the incidence of these two symptoms between the two groups, however, further investigation is warranted. In comparison to CT, MRI is a radiation-free imaging modality that has demonstrated its significance in characterizing and staging various diseases, particularly the thymic neoplasms, with improved diagnostic accuracy [ 10 , 25 – 27 ]. Our results indicate that mediastinal lymphomas exhibit larger max-diameters and a higher incidence of necrosis mediastinal lymphomas compared to TETs, whereas, calcification was more frequently observed in TETs than in mediastinal lymphomas, which aligns with the previous study conducted by Jeanne et al [ 28 ]. Furthermore, MRI functional imaging has been established as a crucial tool for identifying mediastinal diseases as we found in the present research [ 26 , 29 ]. Several studies have demonstrated that ADC histogram analysis has been applied in mediastinal tumors, which has proven valuable in distinguishing between malignant and benign diseases, tumor staging and prognosis prediction [ 26 , 29 – 31 ]. Importantly, our data demonstrates that skewness is the optimal parameter of the ADC histogram for diagnosing TETs and mediastinal lymphomas. Skewness, as a statistical measure of the asymmetrical distribution of the histogram, can effectively reflect the intralesional heterogeneity [ 32 ]. Several recent studies have used skewness as a diagnostic indicator for various purpose, including to differentiating high-grade from low-grade serous ovarian carcinoma, distinguishing between benign and malignant diseases of the breast, associating with the clinical staging of cervical cancer [ 33 , 34 ]. Lymphomas are a group of highly heterogeneous malignant tumors originating from the lymphohematopoietic system and are generally divided into two main groups: Hodgkin's lymphoma (HL) and non-Hodgkin's lymphoma (NHL). According to the National Cancer Institute (NCI), the 5-year overall survival rates for patients with HL and NHL between 2012 and 2018 were 89% and 74%, respectively [ 35 , 36 ]. Liu et al study contains 907 cases of TETs, with a median follow-up of 52 months, the 10-year overall survival rate was 89.5%. distant and/or locoregional recurrences were noted in 53 patients (5.8%) [ 37 ]. It is justifiable to assert that mediastinal lymphoma, as a hematological disease, exhibits a greater propensity for aggression when compared to TETs, and mediastinal lymphomas display a more pronounced heterogeneity, characterized by higher skewness. Our study emphasizes the significance of skewness as a valuable tool in distinguishing TETs from mediastinal lymphomas, thereby complementing existing evidence in the field of mediastinal tumor research [ 23 , 38 ]. However, in Zhang et al study, they indicate no discernible difference between thymic carcinomas and mediastinal lymphomas [ 31 ]. We speculate on potential causes for this inconsistency as follows, firstly, the sample size of previous research was relatively small (15 patients with thymic carcinomas vs. 13 patients with mediastinal lymphomas) which increased sampling bias in their study. Secondly, the heterogeneity between thymic carcinomas and mediastinal lymphomas may not be as pronounced as that between TET and mediastinal lymphomas, especially since thymomas accounted for the majority of TETs in our study. Remarkably, a comprehensive diagnostic model based on the MRI was constructed and evaluated in the present study. This approach bears similarity to the methodology employed by Wang G et al, who employed PET-CT to distinguish TETs from mediastinal lymphomas, they devised a composite diagnostic model incorporating age, clinical symptoms and standard uptake value ratio (SUVR), which yielded the highest AUC of 0.964 [ 39 ]. In a recent study, Yan et al found that SUVmax on 18F-FDG PET-CT has the potential ability to discriminate lymphomas from TETs in the diagnosis of anterior mediastinal masses, and the combination of SUVmax with clinical parameters increased the predictive accuracy to 87.8% in validation cohort [ 24 ]. Kirienko et al developed and validated a CT-based radiomic model based on non-contrast-enhanced CT that differentiated anterior mediastinal masses as thymic neoplasms or lymphoma with up to 95% sensitivity and up to 89% specificity [ 40 ]. Our results showed that the comprehensive diagnostic model has a higher diagnostic performance than radiologists, and which with high sensitivity and specificity as determined through IDI, bootstrap test and Delong test, could significantly improve the differential diagnostic ability to compared using skewness alone. The results obtained from the DCA curves indicate that the utilization of the combined model for differential diagnosis between TETs and mediastinal lymphomas is more advantageous compared to relying solely on skewness. The calibration curve for the nomogram exhibited favorable predictive identification and capabilities. Moreover, the predictive nomogram showed a high AUC in effectively differentiating between TETs and mediastinal lymphomas. Consequently, our comprehensive diagnostic model aims to boost radiologists' accuracy and confidence, leading to timely and accurate treatment plans and reducing unnecessary surgeries. Our study had some limitations. Firstly, this study was a single-center retrospective study with a relatively small sample size. In the future we will collaborate with multiple centers to enhance participant numbers, thus ensuring more robust statistical analyses and facilitating subgroup analyses and validation across diverse patient groups. Secondly, the study only included patients scanned using the fixed b-value (0 and 800 s/mm 2 ) in the study, which may introduce a certain bias when applied to other b-value scanning data. Future research will include patients scanned with various b-value. Incorporating data from different b-values reflects real-world variations in imaging protocols. It allows for a more comprehensive evaluation of ADC histogram metrics and their diagnostic performance in diverse clinical settings. Indeed, the absence of external validation groups is one of the limitations of our study. To improve the external validity of the diagnostic model future work will incorporate more data with different b-values. Conclusions The volumetric ADC histogram analysis holds significant importance in the differential diagnosis of mediastinal lymphomas and TETs. Furthermore, our diagnostic model, incorporating skewness, age, and LDH > 250U/L, effectively distinguishes these entities, thereby enhancing the diagnostic confidence of radiologists. Abbreviations TETs thymic epithelial tumors GCTs germ cell tumors MRI magnetic resonance imaging CT computed tomography DWI diffusion-weighted imaging ADC apparent diffusion coefficient VOI volume of interest T1WI T1-weighted T2W T2-weighted DCE-T1WI dynamic contrast-enhanced T1-weight imaging ROI regions of interest SD standard deviation ICC intraclass correlation coefficients AUC areas under the curve ROC receiver operator characteristics ACC accuracy VIF variance inflation factor IDI integrated discriminatory improvement DCA decision curve analyses MG myasthenia gravis LDH lactate dehydrogenase OR odds ratio α-FP α-fetoprotein β- HCG β-human chorionic gonadotropin HL Hodgkin's lymphomaand NHL non-Hodgkin's lymphoma NCI National Cancer Institute SUVR standard uptake value ratio Declarations Ethics approval and consent to participate: This retrospective study was conducted in accordance with the Declaration of Helsinki and was approved by the institutional review board of Shanghai Chest Hospital, School of Medicine, Shanghai Jiao Tong University (No. IS23088). Individual consent for this retrospective analysis was waived. Consent for publication The informed consent for publication was obtained from the participants and their legal guardian. Competing interests The authors declare no competing interests. Authors’ contributions Conception and design: Luna Wang, Yan Shen; Administrative support: Hong Yu, Lin Zhu; Provision of study materials or patients: Luna Wang, Huiyuan Zhu, Yu Zhang, Yan Shen; Collection and assembly of data:Luna Wang, Huiyuan Zhu, Yu Zhang, Yan Shen. Data analysis and interpretation:Luna Wang, Huiyuan Zhu, Lin Zhu, Hong Yu. Manuscript writing: All authors. Final approval of manuscript: All authors. Acknowledgements Not applicable. Funding This work was supported by the National Natural Science Foundation of China (8207070786); Young Scientists Fund of the National Natural Science Foundation of China (82302188). 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Supplementary Files Appendices.docx Cite Share Download PDF Status: Published Journal Publication published 07 Jan, 2026 Read the published version in BMC Medical Imaging → Version 1 posted Editorial decision: Revision requested 13 Nov, 2025 Reviews received at journal 10 Nov, 2025 Reviewers agreed at journal 05 Nov, 2025 Reviewers agreed at journal 02 Oct, 2025 Reviews received at journal 01 Oct, 2025 Reviewers agreed at journal 01 Oct, 2025 Reviewers invited by journal 30 Sep, 2025 Editor assigned by journal 30 Sep, 2025 Editor invited by journal 26 Sep, 2025 Submission checks completed at journal 26 Sep, 2025 First submitted to journal 26 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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02:40:32","extension":"html","order_by":39,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":179119,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7688743/v1/cdef437692254a78734fc93e.html"},{"id":93542795,"identity":"dcdf1c30-32dc-4204-878a-467dbd3f936b","added_by":"auto","created_at":"2025-10-15 02:40:31","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":891192,"visible":true,"origin":"","legend":"\u003cp\u003eStudy diagram of the patient selection process.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7688743/v1/e458f77fd8fd474200b31e34.png"},{"id":93543982,"identity":"78978e35-2f39-4f99-a59b-a6a1fe836325","added_by":"auto","created_at":"2025-10-15 02:48:31","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":33889042,"visible":true,"origin":"","legend":"\u003cp\u003eMagnetic resonance imaging (MRI) scans and ADC histograms of thymoma and mediastinal lymphoma cases. \u003cstrong\u003ea~e\u003c/strong\u003e A 54-year-old woman with thymoma. \u003cstrong\u003ef~j \u003c/strong\u003eA 35-year-old woman with mediastinal lymphoma. \u003cstrong\u003ea, f \u003c/strong\u003eAxial fat-suppressed with Spectral Presaturation with Inversion Recovery (SPAIR) scan showing tumors with high signal intensity. \u003cstrong\u003eb, g\u003c/strong\u003e Axial contrast-enhanced fat-suppressed (FS) T1WI scan showing tumors with high enhancement in venous phase. \u003cstrong\u003ec, h\u003c/strong\u003eDiffusion-weight imaging (DWI) scan (b = 800 s/mm2) showing tumors with high-signal intensity with red regions of interest. \u003cstrong\u003ed, i \u003c/strong\u003eADC maps. \u003cstrong\u003ee,j \u003c/strong\u003eADC histogram.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7688743/v1/44b6f04c9ed0562ffd7c4eb0.png"},{"id":93542787,"identity":"c3a453e3-94c1-4a67-80bb-326e229d5a79","added_by":"auto","created_at":"2025-10-15 02:40:31","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":21823752,"visible":true,"origin":"","legend":"\u003cp\u003eThe ROC curve for ADC histogram parameters and the comprehensive diagnostic model.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7688743/v1/d72bd8abce09006250ae608f.png"},{"id":93542788,"identity":"64e01447-c106-47ac-9db8-e046373b8b68","added_by":"auto","created_at":"2025-10-15 02:40:31","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2163480,"visible":true,"origin":"","legend":"\u003cp\u003eA nomogram established by combining the age, LDH \u0026gt; 250U/L and skewness.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7688743/v1/8d3edc3281877c38dbfe89c1.png"},{"id":93542775,"identity":"a3650bd5-15bf-4a3a-8cd2-025a8c26698d","added_by":"auto","created_at":"2025-10-15 02:40:30","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":4667964,"visible":true,"origin":"","legend":"\u003cp\u003eThe calibration curve depicts calibration of the model in terms of the agreement between the predicted probabilities and observed outcomes of mediastinal lymphoma. The dotted black line represents the ideal prediction, whereas the blue line shows the performance of the comprehensive diagnostic model.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-7688743/v1/35f4527e489b176298ac7b26.png"},{"id":93542791,"identity":"1fbbbf78-3271-46e8-ab74-11e90c4a127d","added_by":"auto","created_at":"2025-10-15 02:40:31","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":3951809,"visible":true,"origin":"","legend":"\u003cp\u003eDecision curve analysis of the comprehensive diagnostic model (combines age, LDH \u0026gt; 250U/L and skewness) compared with skewness alone. The x-axis represents the threshold probability, and the y-axis represents the net benefit. DCA showed that the comprehensive diagnostic model had higher overall net benefit than skewness nearly all risk thresholds. Model refers to a comprehensive diagnostic model.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-7688743/v1/8252a006fe0d1bd9d1eb1375.png"},{"id":93542770,"identity":"621ee520-6807-4516-82ff-b1d91ddfa2b1","added_by":"auto","created_at":"2025-10-15 02:40:30","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":422078,"visible":true,"origin":"","legend":"","description":"","filename":"Appendices.docx","url":"https://assets-eu.researchsquare.com/files/rs-7688743/v1/1133b45b8017d4a1bc6cca11.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Multiparameter Diagnostic Model Based on MRI Volumetric ADC Histogram and Clinical Variables Accurately Differentiate Thymic Epithelial Tumors From Mediastinal Lymphomas","fulltext":[{"header":"Background","content":"\u003cp\u003eMediastinal neoplasms are the most frequently encountered primary tumors in the anterior mediastinum and mainly contain thymic epithelial tumors (TETs) (including thymomas, thymic carcinomas, thymic neuroendocrine neoplasms), lymphomas, and germ cell tumors (GCTs) and others [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The management and prognosis of each type of anterior mediastinal mass differ substantially [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Radical thymectomy is regarded as the preferred surgical approach for resectable TETs, whereas chemotherapy is the recommended treatment for mediastinal lymphoma after confirming the histological diagnosis through needle biopsy, and surgical procedures should be avoided [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Furthermore, it is recommended to refrain from performing transpleural biopsy in cases of suspected TETs due to the significant potential of advancing the stage of thymoma from stage I to IV thymoma by disseminating the tumor within the pleural space [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Consequently, an accurate diagnosis of mediastinal lymphoma and TETs holds paramount importance in clinical treatment and prognosis for patients with thymic neoplasms.\u003c/p\u003e\u003cp\u003eMagnetic resonance imaging (MRI), being a crucial diagnostic tool, is predominantly utilized for presumptive diagnosis, evaluation, and management of thymic neoplasms [\u003cspan additionalcitationids=\"CR8 CR9\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Previous studies have demonstrated that MRI can offer more comprehensive information compared to computed tomography (CT) when assessing a mediastinal mass [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. There were considerable overlaps between the CT and MRI findings of these diseases, it was reported that even when performed by experienced chest radiologists, the diagnostic accuracy of CT for differentiating mediastinal tumors was 61%, while the combination of CT and MRI improved the diagnostic accuracy up to 67%, leaving much room for improvements in the era of advanced technology [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Diffusion-weighted imaging (DWI) is a widely recognized functional imaging technique in MRI that allows for the non-invasive evaluation of tissue microstructure by detecting the diffusion movement of water molecules within living tissues. This technique also provides a wealth of texture information through the automatically generated apparent diffusion coefficient (ADC) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Recently, more researchers utilized ADC histogram analysis to distinguish between different pathological subtypes and stages [\u003cspan additionalcitationids=\"CR15 CR16\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], and even to predict genotyping and prognosis prediction [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. However, the research revealed that the rates of unnecessary thymectomies and invasive surgeries of mediastinal lymphoma were relatively high (12.4%-52.3%) due to a lack of knowledge regarding its distinct radiological characteristics [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Moreover, limited research has been conducted on the utilization of ADC histogram in distinguishing between TETs and mediastinal lymphomas, primarily due to the rarity of mediastinal neoplasms [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTherefore, the primary objective of our study was to ascertain the differentiating potential of ADC histogram parameters in TETs and mediastinal lymphomas. Furthermore, we aimed to develop a comprehensive diagnostic model by combining ADC histogram metrics with clinical variables to further verify a more effective differentiation of TETs from mediastinal lymphomas.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStudy design\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe present retrospective study was approved by the institutional review board, and the requirement for informed consent was waived due to the retrospective design and lack of interventions, the committee concluded that the Medical Research Involving Human Subjects Act (WMO) did not apply. The present study retrospectively enrolled patients from January 2019 to December 2021, pathologically diagnosed with mediastinal lymphomas, and subsequently underwent an MRI examination. Meanwhile, we collected patients with pathologically diagnosed TETs from January 2019 to December 2019. The inclusion criteria were as follows: (1) patients underwent MRI within 2 weeks before treatment; (2) the diagnosis of primary thymic tumors was confirmed by biopsy or surgical resection pathology; (3) complete clinical data. The exclusion criteria were as follows: (1) treatment received before MRI examination; (2) with other malignant tumors and tumor recurrence; (3) MRI with non-standard b values (0, 800 s/mm\u003csup\u003e2\u003c/sup\u003e); (4) A significant artifact and image quality of DWI were not adequate for processing; (5) lesions that were predominantly cystic necrosis, resulting in an inability to accurately measure ADC values; (6) lesions \u0026lt; 2 cm have fewer than 3 layers on DWI axial scan and a volume of interest (VOI) for the lesion cannot be obtained. Initially, 274 and 84 patients with TETs and mediastinal\u0026nbsp;lymphomas were included. Following the application of our exclusion criteria, 181and 47 patients were excluded, and finally, a\u0026nbsp;total of 93 patients with TETs and 37 patients with\u0026nbsp;mediastinal\u0026nbsp;lymphomas were recruited in the present trial\u0026nbsp;(Figure 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eMagnetic resonance imaging (MRI)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll MRI examinations were performed with a 3.0T MR scanner (Ingenia 3.0T, Philips Healthcare, Best, the Netherlands) utilizing a 16-channel Torso coil within 2 weeks before treatment. The routine MR sequences included axial gradient echo T1-weighted (T1WI), axial and coronal turbo spin-echo T2-weighted (T2W) imaging. MR images were obtained during end-inspiration breath-holding. The DWI sequence slice locations corresponded to those from the T2-weighted images to provide anatomic reference. For dynamic contrast-enhanced T1-weight imaging (DCE-T1WI), the contrast agent\u0026nbsp;(gadodiamide, 0.5mmol/ml, GE Healthcare)\u0026nbsp;was\u0026nbsp;administrated\u0026nbsp;intravenously\u0026nbsp;at a dose of 2.0 ml/kg body weight, followed by 20 ml of saline flush to clear the tube at an injection rate of\u0026nbsp;4.0 ml/s.\u0026nbsp;MRI sequences and parameters are detailed in Appendix\u0026nbsp;Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eMRI Image assessment\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMRI images were reviewed strictly separately by two radiologists in a blinded fashion (WLN with 8 and SY with 25 years of experience, respectively). One radiologist (WLN) evaluated all the 130 patients (numbered 1 to 130), and another radiologist (SY) analyzed a group of patients (n = 26, with identification numbers ending in 0 or 5), they were blinded to clinical information and pathology. ADC histogram metrics were generated with the FireVoxel software (v. Build 432, https://www.firevoxel.org/). For each patient, T1WI, T2WI, T2WI-SPAIR, DCE-T1WI and DWI images (with \u003cem\u003eb\u003c/em\u003e values of 0 and 800 s/mm\u003csup\u003e2\u003c/sup\u003e) were separately imported into FireVoxel software as a single file. Regions of interest (ROIs) were drawn along the tumor border on all slices of DWI scans (\u003cem\u003eb\u0026nbsp;\u003c/em\u003evalue = 800 s/mm\u003csup\u003e2\u003c/sup\u003e) with the reference to conventional MR images\u0026nbsp;(T1WI, T2WI and DCE-T1WI) or CT images.\u0026nbsp;If calcification, obvious necrosis, cystic lesions or hemorrhage were present in the tumor, the ROI was delineated only around the solid parenchymal areas. The calcification areas were defined as hyperdense area on non-enhanced CT. The necrosis and cystic areas were defined as areas with lower signal intensity on T1WI, higher signal intensity on T2WI and no enhancement on DCE images. CT sequences and parameters are detailed in Appendix Note 1.\u0026nbsp;The VOI was then constructed based on all ROI slices.\u0026nbsp;The following parameters were obtained:\u0026nbsp;5\u003csup\u003eth\u003c/sup\u003epercentile, 10\u003csup\u003eth\u003c/sup\u003epercentile, 25\u003csup\u003eth\u003c/sup\u003epercentile, 50\u003csup\u003eth\u003c/sup\u003epercentile, 75\u003csup\u003eth\u003c/sup\u003epercentile, 90\u003csup\u003eth\u003c/sup\u003epercentil,\u0026nbsp;95\u003csup\u003eth\u003c/sup\u003epercentile, skewness, and kurtosis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConventional radiological features and interpretation of radiologists\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTwo readers (WLN and SY), who were blinded to the patients\u0026rsquo; histopathologic data, independently reviewed the conventional images. The following radiological features were evaluated: (1) maximum diameter; (2) calcification; (3) necrosis and cyst; (4) contour: smooth or lobulated, the details were in Appendix Note2. Then two readers give common results for each patient according to conventional images.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStatistical analyses\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStatistical analyses were performed with R v. 4.2.2 (Chicago, IL, USA) and IBM SPSS Statistic v.25. Qualitative data were described as number of cases and percentage [n (%)] for categorical variables. Continuous variables were presented as mean \u0026plusmn; standard deviation (SD) (normal distribution) or median and interquartile range (non-normal distribution). The differences in ADC parameters, age, maximal diameter between TETs and mediastinal lymphomas were compared using the student\u0026rsquo;s-test (normal distribution) or the Mann-Whitney U test (non-normal distribution). Categorical variables were compared using Fisher\u0026rsquo;s exact test. Inter-observer agreement was evaluated by \u0026kappa; coefficients for categorical features, and the intraclass correlation coefficients (ICC) for continuous variables, which was interpreted as follows: 0-0.2, poor; 0.2-0.4, fair; 0.4-0.6, moderate; 0.6-0.8, good; 0.8-1.0, excellent. Areas under the curve (AUC) of receiver operator characteristics (ROC) were determined to assess the diagnostic performance of various ADC histogram metrics. The sensitivity, specificity and accuracy (ACC) were calculated. The parameters of single factor analysis \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001 were selected to be included in the logistic regression analysis. The collinearity of model parameters was tested using the variance inflation factor (VIF), with VIF \u0026gt; 10 indicating the presence of multicollinearity, and such parameters were excluded from the model. None of the interactions we tested were significant, hence, they are not discussed further in this article. Multivariate logistic regression analysis was performed using forward stepwise approach to identify the independent predictor to further construct a comprehensive diagnostic model for distinguishing TETs from mediastinal lymphomas. The bootstrap test integrated discriminatory improvement (IDI) was calculated for comparison of the diagnostic models (\u003cem\u003ePredictABEL\u003c/em\u003e package). Decision curve analyses (DCA) evaluated the clinical utility and accuracy of the comprehensive diagnostic model by calculating the net benefits for a range of threshold probabilities in ADC histogram parameters with the highest AUC (\u003cem\u003ermda\u003c/em\u003e package). A nomogram was built using the comprehensive diagnostic model to serve as a graphical representation of results (\u003cem\u003erms\u003c/em\u003e package). The nomogram\u0026rsquo;s predictive performance was measured using Harrell\u0026rsquo;s C-index and calibration with 2000 bootstrap samples to decrease the overfit bias. Calibration curves were calculated by the regression analysis and bootstraps of 1000 resamples were set (\u003cem\u003erms\u003c/em\u003e package). \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eParticipant Characteristics\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOut of 358 patients with pathologically confirmed TETs and mediastinal lymphomas, a total of 130 patients (36.3%) met the inclusion criteria of this study. Specifically, 93 TETs and 37 mediastinal lymphomas were included in the present study. Based on postoperative pathological proof, 57cases (61.3%) were low-risk thymomas, 20 cases (21.5%) were high-risk thymomas, and 16 cases (17.2%) were thymic carcinomas of TETs. The specific number and proportion of pathological subtypes are shown in Table 1. In the present study, diffuse large B-cell lymphoma had the highest incidence among the patients with lymphoma (54.1%). The proportion of Hodgkin lymphoma and T-lymphoblastic lymphoma were 35.1% and 10.8%, respectively (Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. Pathological diagnoses of patients.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"378\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52.381%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eType\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47.619%;\"\u003e\n \u003cp\u003eCases/Percentage\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52.381%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTETs (93)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47.619%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52.381%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLow-risk thymom\u003c/strong\u003e\u003cstrong\u003ea\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47.619%;\"\u003e\n \u003cp\u003e57 (61.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52.381%;\"\u003e\n \u003cp\u003eType A thymoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47.619%;\"\u003e\n \u003cp\u003e6 (6.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52.381%;\"\u003e\n \u003cp\u003eType AB thymoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47.619%;\"\u003e\n \u003cp\u003e36 (38.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52.381%;\"\u003e\n \u003cp\u003eType B1 thymoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47.619%;\"\u003e\n \u003cp\u003e15 (16.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52.381%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHigh-risk thymoma\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47.619%;\"\u003e\n \u003cp\u003e20 (21.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52.381%;\"\u003e\n \u003cp\u003eType B2 thymoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47.619%;\"\u003e\n \u003cp\u003e12 (12.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52.381%;\"\u003e\n \u003cp\u003eType B3 thymoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47.619%;\"\u003e\n \u003cp\u003e8 (8.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52.381%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eThymic carcinoma\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47.619%;\"\u003e\n \u003cp\u003e16 (17.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52.381%;\"\u003e\n \u003cp\u003eSquamous cell carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47.619%;\"\u003e\n \u003cp\u003e13 (14.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52.381%;\"\u003e\n \u003cp\u003eothers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47.619%;\"\u003e\n \u003cp\u003e3 (3.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52.381%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMediastinal lymphoma (37)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47.619%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52.381%;\"\u003e\n \u003cp\u003eDiffuse large B-cell lymphoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47.619%;\"\u003e\n \u003cp\u003e20 (54.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52.381%;\"\u003e\n \u003cp\u003eHodgkin lymphoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47.619%;\"\u003e\n \u003cp\u003e13 (35.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52.381%;\"\u003e\n \u003cp\u003eT-lymphoblastic lymphoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47.619%;\"\u003e\n \u003cp\u003e4 (10.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTETs, thymic epithelial tumors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eClinical variables including age, sex and symptoms of included participants were statistically analyzed, and our data showed that the onset age of mediastinal lymphoma is significantly lower than that of TETs (38.11 \u0026plusmn; 13.51 years \u003cem\u003evs.\u003c/em\u003e 53.66 \u0026plusmn; 12.99 years, \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001). Furthermore, notable differences were observed in symptom prevalence, including a significantly higher proportion of asymptomatic patients in the TET group (82.8% vs. 62.2%, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01) and a markedly higher incidence of cough in the mediastinal lymphoma group (18.9% vs. 3.2%, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001). No statistically significant differences were identified in sex distribution (62.2% female in lymphoma vs. 49.5% in TETs, \u003cem\u003eP\u003c/em\u003e = 0.190), symptoms such as chest distress (2.7% vs. 1.1%, \u003cem\u003eP\u003c/em\u003e = 0.49), chest pain (13.5% vs. 5.4%, \u003cem\u003eP\u003c/em\u003e = 0.147), myasthenia gravis (MG) (0% vs. 5.4%, \u003cem\u003eP\u003c/em\u003e = 0.321), B symptoms (2.7% vs. 0%, \u003cem\u003eP\u003c/em\u003e = 0.285), or other symptoms (0% vs. 2.2%, \u003cem\u003eP\u003c/em\u003e = 1.000). Regarding laboratory findings, a substantially greater proportion of mediastinal lymphoma patients exhibited elevated serum lactate dehydrogenase (LDH) levels (\u0026gt;250 U/L) compared to TET patients (54.1% vs. 2.2%, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001) (Table 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Baseline characteristics and clinical variables.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"671\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3582%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.8657%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTETs (n=93\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.5224%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.3881%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMediastinal lymphoma (n= 37)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.8657%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3582%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (y)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.8657%;\"\u003e\n \u003cp\u003e53.66 \u0026plusmn; 12.99\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.5224%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.3881%;\"\u003e\n \u003cp\u003e38.11 \u0026plusmn; 13.51\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.8657%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3582%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale\u003c/strong\u003e: Female (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.8657%;\"\u003e\n \u003cp\u003e46 (49.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.5224%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.3881%;\"\u003e\n \u003cp\u003e23 (62.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.8657%;\"\u003e\n \u003cp\u003e0.190\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3582%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSymptom\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.8657%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.5224%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.3881%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.8657%;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.3582%;\"\u003e\n \u003cp\u003eAsymptomatic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8657%;\"\u003e\n \u003cp\u003e77 (82.8%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.5224%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.3881%;\"\u003e\n \u003cp\u003e23 (62.2%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.8657%;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.3582%;\"\u003e\n \u003cp\u003eCough\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8657%;\"\u003e\n \u003cp\u003e3 (3.2%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.5224%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.3881%;\"\u003e\n \u003cp\u003e7 (18.9%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.8657%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.3582%;\"\u003e\n \u003cp\u003eChest distress\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8657%;\"\u003e\n \u003cp\u003e1 (1.1%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.5224%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.3881%;\"\u003e\n \u003cp\u003e1 (2.7%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.8657%;\"\u003e\n \u003cp\u003e0.490\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.3582%;\"\u003e\n \u003cp\u003eChest pain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8657%;\"\u003e\n \u003cp\u003e5 (5.4%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.5224%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.3881%;\"\u003e\n \u003cp\u003e5 (13.5%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.8657%;\"\u003e\n \u003cp\u003e0.147\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.3582%;\"\u003e\n \u003cp\u003eMG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8657%;\"\u003e\n \u003cp\u003e5 (5.4%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.5224%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.3881%;\"\u003e\n \u003cp\u003e0 (0.0%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.8657%;\"\u003e\n \u003cp\u003e0.321\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.3582%;\"\u003e\n \u003cp\u003eB symptom\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8657%;\"\u003e\n \u003cp\u003e0 (0.0%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.5224%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.3881%;\"\u003e\n \u003cp\u003e1 (2.7%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.8657%;\"\u003e\n \u003cp\u003e0.285\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.3582%;\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.8657%;\"\u003e\n \u003cp\u003e2 (2.2%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.5224%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.3881%;\"\u003e\n \u003cp\u003e0 (0.0%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.8657%;\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3582%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLDH (\u0026gt;250U/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.8657%;\"\u003e\n \u003cp\u003e2 (2.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.5224%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.3881%;\"\u003e\n \u003cp\u003e20 (54.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.8657%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3582%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRadiological data\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.8657%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.5224%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.3881%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.8657%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3582%;\"\u003e\n \u003cp\u003eMax-diameter (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.8657%;\"\u003e\n \u003cp\u003e4.60 (3.50-6.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.5224%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.3881%;\"\u003e\n \u003cp\u003e7.30 (5.20-10.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.8657%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3582%;\"\u003e\n \u003cp\u003eCalcification\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.8657%;\"\u003e\n \u003cp\u003e21 (22.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.5224%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.3881%;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.8657%;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3582%;\"\u003e\n \u003cp\u003eNecrosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.8657%;\"\u003e\n \u003cp\u003e26 (28.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.5224%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.3881%;\"\u003e\n \u003cp\u003e19 (51.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.8657%;\"\u003e\n \u003cp\u003e\u0026lt;0.05\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3582%;\"\u003e\n \u003cp\u003eContour\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.8657%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.5224%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.3881%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.8657%;\"\u003e\n \u003cp\u003e0.194\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3582%;\"\u003e\n \u003cp\u003eSmooth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.8657%;\"\u003e\n \u003cp\u003e28 (30.11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.5224%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.3881%;\"\u003e\n \u003cp\u003e7 (18.92%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.8657%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3582%;\"\u003e\n \u003cp\u003eLobulated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.8657%;\"\u003e\n \u003cp\u003e65 (69.89%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.5224%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.3881%;\"\u003e\n \u003cp\u003e30 (81.08%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.8657%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTETs, thymic epithelial tumors; MG, myasthenia gravis; LDH, Lactate dehydrogenase. \u003csup\u003e*\u0026nbsp;\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, \u003csup\u003e**\u0026nbsp;\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, \u003csup\u003e***\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e\n\u003cp\u003eRegarding MRI morphological features, calcification, necrosis and contour (such as smooth and lobulated) were evaluated by two readers with \u0026kappa; coefficients in the range of 0.943-1 (Appendix Table 2). It was found that the maximal diameter of TETs was significantly smaller than that of mediastinal lymphomas (4.60 cm [3.50 \u0026ndash; 6.70 cm] \u003cem\u003evs.\u0026nbsp;\u003c/em\u003e7.30 cm [5.20 \u0026ndash; 10.60cm]), \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001). Calcification was more common in TETs (22.6% \u003cem\u003evs.\u0026nbsp;\u003c/em\u003e0%, \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.05), while necrosis was significantly less common (28.0% \u003cem\u003evs.\u0026nbsp;\u003c/em\u003e51.4%, \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.05) compared to mediastinal\u0026nbsp;lymphomas. There was no statistical difference in the incidence of different lesion forms between TETs and\u0026nbsp;mediastinal\u0026nbsp;lymphomas (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0.198)\u0026nbsp;(Table\u0026nbsp;2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eThe MRI ADC\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003ehistogram\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003eParameters\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe inter-observer agreements were good to excellent for the measurements of histogram parameters, with ICCs raging from 0.870-0.998 (Appendix Table 2). Consequently, only the measurements from Radiologist 1 were utilized for further analyses. The ADC histogram parameters were compared between patients with TETs and mediastinal lymphomas (Figure 2).\u0026nbsp;The results demonstrated that the ADC histogram parameters,\u0026nbsp;including\u0026nbsp;25\u003csup\u003eth\u003c/sup\u003epercentile\u0026nbsp;(\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.01), 50\u003csup\u003eth\u003c/sup\u003epercentile\u0026nbsp;(\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001), 75\u003csup\u003eth\u003c/sup\u003epercentile\u0026nbsp;(\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001), 90\u003csup\u003eth\u003c/sup\u003epercentil\u0026nbsp;(\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.05),\u0026nbsp;95\u003csup\u003eth\u003c/sup\u003epercentile\u0026nbsp;(\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.01),\u0026nbsp;ADCmax (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.05), ADCmean (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.01)\u0026nbsp;of patients with\u0026nbsp;mediastinal\u0026nbsp;lymphomas were significantly lower than those of patients with TETs,\u0026nbsp;while the\u0026nbsp;skewness of patients with\u0026nbsp;mediastinal\u0026nbsp;lymphomas were statistically higher than that of TETs (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001).\u0026nbsp;No statistically significant differences were observed between the groups for the 5\u003csup\u003eth\u003c/sup\u003e percentile, 10\u003csup\u003eth\u003c/sup\u003e percentile, ADCmin, or kurtosis parameters. The complete dataset is presented in Table 3.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Comparison of ADC histogram parameters between TETs and\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003emediastinal\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;lymphomas.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.5444%;\"\u003e\n \u003cp\u003eADC histogram parameters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.8185%;\"\u003e\n \u003cp\u003eTETs (n=93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.569%;\"\u003e\n \u003cp\u003eMediastinal lymphoma (n=37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.0681%;\"\u003e\n \u003cp\u003eP valve\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.5444%;\"\u003e\n \u003cp\u003e5\u003csup\u003eth\u003c/sup\u003e percentile ADC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.8185%;\"\u003e\n \u003cp\u003e0.785 (0.573-1.131)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31.569%;\"\u003e\n \u003cp\u003e0.690 (0.568-0.966)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0681%;\"\u003e\n \u003cp\u003e0.430\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.5444%;\"\u003e\n \u003cp\u003e10\u003csup\u003eth\u003c/sup\u003e percentile ADC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.8185%;\"\u003e\n \u003cp\u003e1.014 (0.711-1.331)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31.569%;\"\u003e\n \u003cp\u003e0.765 (0.689-1.083)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0681%;\"\u003e\n \u003cp\u003e0.095\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.5444%;\"\u003e\n \u003cp\u003e25\u003csup\u003eth\u003c/sup\u003e percentile ADC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.8185%;\"\u003e\n \u003cp\u003e1.217 (0.964-1.712)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31.569%;\"\u003e\n \u003cp\u003e0.981 (0.811-1.297)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0681%;\"\u003e\n \u003cp\u003e\u0026lt;0.01 \u003csup\u003e**\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.5444%;\"\u003e\n \u003cp\u003e50\u003csup\u003eth\u003c/sup\u003e percentile ADC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.8185%;\"\u003e\n \u003cp\u003e1.598 (1.178-2.003)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31.569%;\"\u003e\n \u003cp\u003e1.157 (0.959-1.582)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0681%;\"\u003e\n \u003cp\u003e\u0026lt;0.001 \u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.5444%;\"\u003e\n \u003cp\u003e75\u003csup\u003eth\u003c/sup\u003e percentile ADC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.8185%;\"\u003e\n \u003cp\u003e1.867 (1.478-2.316)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31.569%;\"\u003e\n \u003cp\u003e1.437 (1.130-1.889)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0681%;\"\u003e\n \u003cp\u003e\u0026lt;0.001 \u003csup\u003e***\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.5444%;\"\u003e\n \u003cp\u003e90\u003csup\u003eth\u003c/sup\u003e percentile ADC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.8185%;\"\u003e\n \u003cp\u003e2.117(1.710-2.589)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31.569%;\"\u003e\n \u003cp\u003e1.716 (1.366-2.243)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0681%;\"\u003e\n \u003cp\u003e\u0026lt;0.05\u003csup\u003e\u0026nbsp;*\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.5444%;\"\u003e\n \u003cp\u003e95th percentile ADC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.8185%;\"\u003e\n \u003cp\u003e2.378(1.850-2.775)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31.569%;\"\u003e\n \u003cp\u003e1.890 (1.507-2.432)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0681%;\"\u003e\n \u003cp\u003e\u0026lt;0.01**\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.5444%;\"\u003e\n \u003cp\u003eADCmin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.8185%;\"\u003e\n \u003cp\u003e0.246 \u0026plusmn; 0.404\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31.569%;\"\u003e\n \u003cp\u003e0.266 \u0026plusmn; 0.340\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0681%;\"\u003e\n \u003cp\u003e0.790\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.5444%;\"\u003e\n \u003cp\u003eADCmax\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.8185%;\"\u003e\n \u003cp\u003e3.123 \u0026plusmn; 0.772\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31.569%;\"\u003e\n \u003cp\u003e2.830 \u0026plusmn; 0.675\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0681%;\"\u003e\n \u003cp\u003e\u0026lt;0.05* \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.5444%;\"\u003e\n \u003cp\u003eADCmean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.8185%;\"\u003e\n \u003cp\u003e1.562(1.199-1.983)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31.569%;\"\u003e\n \u003cp\u003e1.168(1.020-1.621)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0681%;\"\u003e\n \u003cp\u003e\u0026lt;0.01 **\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.5444%;\"\u003e\n \u003cp\u003eSkewness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.8185%;\"\u003e\n \u003cp\u003e0.029 \u0026plusmn; 0.647\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31.569%;\"\u003e\n \u003cp\u003e0.684 \u0026plusmn; 0.630\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0681%;\"\u003e\n \u003cp\u003e\u0026lt;0.001 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.5444%;\"\u003e\n \u003cp\u003eKurtosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.8185%;\"\u003e\n \u003cp\u003e0.780 \u0026plusmn; 1.437\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31.569%;\"\u003e\n \u003cp\u003e1.376 \u0026plusmn; 1.813\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.0681%;\"\u003e\n \u003cp\u003e0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTETs, thymic epithelial tumors. \u003csup\u003e*\u0026nbsp;\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, \u003csup\u003e**\u0026nbsp;\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, \u003csup\u003e***\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001\u003c/p\u003e\n\u003cp\u003eDate is mean \u0026plusmn; standard deviation (normal distribution) or median and interquartile range in parentheses (non-normal distribution)\u0026rdquo; under table 3.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDiagnostic performance of the comprehensive diagnostic model\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe evaluated the diagnostic efficacy of individual indicators such as the 25\u003csup\u003eth\u003c/sup\u003e percentile, 50\u003csup\u003eth\u003c/sup\u003e percentile, 75\u003csup\u003eth\u0026nbsp;\u003c/sup\u003epercentile,\u0026nbsp;90\u003csup\u003eth\u0026nbsp;\u003c/sup\u003epercentile,\u0026nbsp;95\u003csup\u003eth\u0026nbsp;\u003c/sup\u003epercentile, ADCmax, ADCmean and skewness of the ADC histogram. The ROC curve indicated that the skewness exhibited the highest diagnostic ability among the ADC parameters with acut-off value of 0.014, and the AUC was 0.785 (95%CI: 0.701-0.869), the sensitivity was 0.946 (95%CI: 0.805-0.991), the specificity was 0.559 (95%CI: 0.453-0.661), and the accuracy was 0.669 (95% CI: 0.581-0.749). From the multivariate analysis, the age (odds ratio (OR) = 0.911; 95% CI: 0.868, 0.956; \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), surm LDH (\u0026gt; 250U/L) (OR = 84.427; 95% CI: 10.891, 623.837; \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), and skewness of ADC histogram (OR = 2.879; 95% CI: 1.104, 7.508; \u003cem\u003eP\u003c/em\u003e = 0.031) of the mediastinal mass were independent significant variables associated with lymphomas. The comprehensive diagnostic model which combined age, serum LDH (\u0026gt; 250U/L) and skewness demonstrated that the AUC was 0.914 (95%CI: 0.850-0.977), the sensitivity was 0.865 (95%CI: 0.704-0.949), the specificity was 0.914 (95%CI: 0.833-0.959), and the accuracy was 0.900 (95% CI: 0.835-0.946) (Figure 3). The diagnostic performance of individual parameters and the comprehensive diagnostic model are shown in Table 4.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4. Differential diagnostic efficiency of ADC histogram parameters and clinical variables and combined between TETs and mediastinal lymphomas.\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"91%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.4948%;\"\u003e\n \u003cp\u003eParameters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4021%;\"\u003e\n \u003cp\u003eCut-off\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.4948%;\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003cp\u003e(95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.6186%;\"\u003e\n \u003cp\u003eSensitivity (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.4948%;\"\u003e\n \u003cp\u003eSpecificity\u003c/p\u003e\n \u003cp\u003e(95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.4948%;\"\u003e\n \u003cp\u003eACC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e25\u003csup\u003eth\u003c/sup\u003e percentile ADC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e0.853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e0.668\u003c/p\u003e\n \u003cp\u003e(0.535-0.732)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e0.459\u003c/p\u003e\n \u003cp\u003e(0.299-0.629)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e0.828\u003c/p\u003e\n \u003cp\u003e(0.733-0.896)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e0.723\u003c/p\u003e\n \u003cp\u003e(0.638-0.798)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e50\u003csup\u003eth\u003c/sup\u003e percentile ADC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e1.804\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e0.701\u003c/p\u003e\n \u003cp\u003e(0.605-0.797)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e0.919\u003c/p\u003e\n \u003cp\u003e(0.770-0.979)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e0.398\u003c/p\u003e\n \u003cp\u003e(0.299-0.505)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e0.546\u003c/p\u003e\n \u003cp\u003e(0.457-0.634)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e75\u003csup\u003eth\u003c/sup\u003e percentile ADC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e1.522\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e0.703\u003c/p\u003e\n \u003cp\u003e(0.607-0.799)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e0.595\u003c/p\u003e\n \u003cp\u003e(0.422-0.748)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e0.742\u003c/p\u003e\n \u003cp\u003e(0.639-0.825)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e0.700\u003c/p\u003e\n \u003cp\u003e(0.613-0.777)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e90\u003csup\u003eth\u003c/sup\u003e percentile ADC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e1.757\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e0.693\u003c/p\u003e\n \u003cp\u003e(0.596-0.791)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e0.595\u003c/p\u003e\n \u003cp\u003e(0.422-0.748)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e0.742\u003c/p\u003e\n \u003cp\u003e(0.639-0.825)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e0.700\u003c/p\u003e\n \u003cp\u003e(0.613-0.777)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e95\u003csup\u003eth\u003c/sup\u003e percentile ADC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e2.632\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e0.678\u003c/p\u003e\n \u003cp\u003e(0.579-0.777)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e0.946\u003c/p\u003e\n \u003cp\u003e(0.805-0.991)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e0.355\u003c/p\u003e\n \u003cp\u003e(0.260-0.462)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e0.523\u003c/p\u003e\n \u003cp\u003e(0.434-0.611)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.4948%;\"\u003e\n \u003cp\u003eADCmax\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e2.755\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e0.621\u003c/p\u003e\n \u003cp\u003e(0.516-0.726)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e0.568\u003c/p\u003e\n \u003cp\u003e(0.396-0.725)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e0.667\u003c/p\u003e\n \u003cp\u003e(0.560-0.759)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e0.638\u003c/p\u003e\n \u003cp\u003e(0.550-0.721)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.4948%;\"\u003e\n \u003cp\u003eADCmean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e1.260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e0.681\u003c/p\u003e\n \u003cp\u003e(0.582-0.780)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e0.568\u003c/p\u003e\n \u003cp\u003e(0.396-0.725)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e0.731\u003c/p\u003e\n \u003cp\u003e(0.627-0.815)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e0.685\u003c/p\u003e\n \u003cp\u003e(0.597-0.763)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.4948%;\"\u003e\n \u003cp\u003eSkewness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e0.785\u003c/p\u003e\n \u003cp\u003e(0.701-0.869)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e0.946\u003c/p\u003e\n \u003cp\u003e(0.805-0.991)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e0.559\u003c/p\u003e\n \u003cp\u003e(0.453-0.661)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e0.669\u003c/p\u003e\n \u003cp\u003e(0.581-0.749)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.4948%;\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e0.914\u003c/p\u003e\n \u003cp\u003e(0.850-0.977)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6186%;\"\u003e\n \u003cp\u003e0.865\u003c/p\u003e\n \u003cp\u003e(0.704-0.949)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e0.914\u003c/p\u003e\n \u003cp\u003e(0.833-0.959)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4948%;\"\u003e\n \u003cp\u003e0.900\u003c/p\u003e\n \u003cp\u003e(0.835-0.946)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eModel refers to a comprehensive diagnostic model, which combines age, serum LDH (\u0026gt; 250U/L), and skewness; CI, confidence interval; AUC, area under curve; ACC, accuracy\u003c/p\u003e\n\u003cp\u003eThe DeLong test showed that the AUC of the comprehensive diagnostic model was superior to that of skewness alone (Z = 2.862, \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.01). According to the bootstrap test, the comprehensive diagnostic model demonstrated a statistically significant improvement in ROC compared to considering skewness alone (\u003cem\u003eD\u0026nbsp;\u003c/em\u003e= 2.903, boot. N = 2000; boot. Stratified = 1, \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.01). Our results indicated that the inclusion of age and elevated LDH level (\u0026gt; 250U/L) in the comprehensive diagnostic model led to a significantly better reclassification compared to skewness alone with an IDI of 0.370 (95%CI: 0.269-0.470, \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eNomogram development\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe nomogram was constructed based on the multivariate model, which incorporated three independent predictors: age, elevated LDH (\u0026gt; 250U/L) and skewness for differentiating TETs from mediastinal lymphomas (Figure 4). Satisfactory predictive performance of the nomogram was observed, with\u0026nbsp;a C-index\u0026nbsp;value of 0.917 (95%CI: 0.915-0.918). The calibration curve was used to assess the goodness of the fit of the nomogram, with a good agreement (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001) (Figure 5). DCA showed that the comprehensive diagnostic model had higher overall net benefit than skewness nearly all risk thresholds, indicating its good clinical usefulness (Figure 6).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study provided evidence that MRI histogram parameters, particularly skewness, have significant diagnostic value in distinguishing between TETs and mediastinal lymphoma (AUC\u0026thinsp;=\u0026thinsp;0.785, 95%CI: 0.701\u0026ndash;0.869). Additionally, the integration of ADC histogram metrics (skewness), clinical information (age) and the laboratory indicator (LDH\u0026thinsp;\u0026gt;\u0026thinsp;250U/L) in a comprehensive diagnostic model (AUC\u0026thinsp;=\u0026thinsp;0.914, 95%CI: 0.850\u0026ndash;0.977) enables accurate differentiation between TETs and mediastinal lymphomas.\u003c/p\u003e\u003cp\u003eThe present study showed that certain clinical clues can aid in distinguishing between TETs and mediastinal lymphomas. Our data revealed that patients with mediastinal lymphoma were significantly younger than those with TETs, which aligns with the findings of Wang S et al [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Previous laboratory investigations have also identified valuable hematological markers, such as α-fetoprotein (α-FP), β-human chorionic gonadotropin (β- HCG), and LDH, which were consistent with our data [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Our results indicate that LDH plays a crucial role in distinguishing between TETs and mediastinal lymphomas, with 54.1% of patients with mediastinal lymphomas exhibiting LDH levels exceeding 250U/L. Furthermore, our univariate analyses revealed a significantly higher proportion of asymptomatic cases (82.8% \u003cem\u003evs\u003c/em\u003e. 62.2%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and cough symptoms (3.2% \u003cem\u003evs\u003c/em\u003e.18.9%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in mediastinal lymphomas compared to TETs, although these symptoms were not included in the final multivariate regression model. Besides, in our study, patients with myasthenia gravis (MG) were only proven to be TETs, while lymphoma patients only manifested B symptoms in our study, which is similar to the findings of Wang et al [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Statistical analysis indicates no difference in the incidence of these two symptoms between the two groups, however, further investigation is warranted.\u003c/p\u003e\u003cp\u003eIn comparison to CT, MRI is a radiation-free imaging modality that has demonstrated its significance in characterizing and staging various diseases, particularly the thymic neoplasms, with improved diagnostic accuracy [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Our results indicate that mediastinal lymphomas exhibit larger max-diameters and a higher incidence of necrosis mediastinal lymphomas compared to TETs, whereas, calcification was more frequently observed in TETs than in mediastinal lymphomas, which aligns with the previous study conducted by Jeanne et al [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Furthermore, MRI functional imaging has been established as a crucial tool for identifying mediastinal diseases as we found in the present research [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSeveral studies have demonstrated that ADC histogram analysis has been applied in mediastinal tumors, which has proven valuable in distinguishing between malignant and benign diseases, tumor staging and prognosis prediction [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Importantly, our data demonstrates that skewness is the optimal parameter of the ADC histogram for diagnosing TETs and mediastinal lymphomas. Skewness, as a statistical measure of the asymmetrical distribution of the histogram, can effectively reflect the intralesional heterogeneity [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Several recent studies have used skewness as a diagnostic indicator for various purpose, including to differentiating high-grade from low-grade serous ovarian carcinoma, distinguishing between benign and malignant diseases of the breast, associating with the clinical staging of cervical cancer [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Lymphomas are a group of highly heterogeneous malignant tumors originating from the lymphohematopoietic system and are generally divided into two main groups: Hodgkin's lymphoma (HL) and non-Hodgkin's lymphoma (NHL). According to the National Cancer Institute (NCI), the 5-year overall survival rates for patients with HL and NHL between 2012 and 2018 were 89% and 74%, respectively [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Liu et al study contains 907 cases of TETs, with a median follow-up of 52 months, the 10-year overall survival rate was 89.5%. distant and/or locoregional recurrences were noted in 53 patients (5.8%) [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. It is justifiable to assert that mediastinal lymphoma, as a hematological disease, exhibits a greater propensity for aggression when compared to TETs, and mediastinal lymphomas display a more pronounced heterogeneity, characterized by higher skewness. Our study emphasizes the significance of skewness as a valuable tool in distinguishing TETs from mediastinal lymphomas, thereby complementing existing evidence in the field of mediastinal tumor research [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. However, in Zhang et al study, they indicate no discernible difference between thymic carcinomas and mediastinal lymphomas [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. We speculate on potential causes for this inconsistency as follows, firstly, the sample size of previous research was relatively small (15 patients with thymic carcinomas vs. 13 patients with mediastinal lymphomas) which increased sampling bias in their study. Secondly, the heterogeneity between thymic carcinomas and mediastinal lymphomas may not be as pronounced as that between TET and mediastinal lymphomas, especially since thymomas accounted for the majority of TETs in our study.\u003c/p\u003e\u003cp\u003eRemarkably, a comprehensive diagnostic model based on the MRI was constructed and evaluated in the present study. This approach bears similarity to the methodology employed by Wang G et al, who employed PET-CT to distinguish TETs from mediastinal lymphomas, they devised a composite diagnostic model incorporating age, clinical symptoms and standard uptake value ratio (SUVR), which yielded the highest AUC of 0.964 [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. In a recent study, Yan et al found that SUVmax on 18F-FDG PET-CT has the potential ability to discriminate lymphomas from TETs in the diagnosis of anterior mediastinal masses, and the combination of SUVmax with clinical parameters increased the predictive accuracy to 87.8% in validation cohort [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Kirienko et al developed and validated a CT-based radiomic model based on non-contrast-enhanced CT that differentiated anterior mediastinal masses as thymic neoplasms or lymphoma with up to 95% sensitivity and up to 89% specificity [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Our results showed that the comprehensive diagnostic model has a higher diagnostic performance than radiologists, and which with high sensitivity and specificity as determined through IDI, bootstrap test and Delong test, could significantly improve the differential diagnostic ability to compared using skewness alone. The results obtained from the DCA curves indicate that the utilization of the combined model for differential diagnosis between TETs and mediastinal lymphomas is more advantageous compared to relying solely on skewness. The calibration curve for the nomogram exhibited favorable predictive identification and capabilities. Moreover, the predictive nomogram showed a high AUC in effectively differentiating between TETs and mediastinal lymphomas. Consequently, our comprehensive diagnostic model aims to boost radiologists' accuracy and confidence, leading to timely and accurate treatment plans and reducing unnecessary surgeries.\u003c/p\u003e\u003cp\u003eOur study had some limitations. Firstly, this study was a single-center retrospective study with a relatively small sample size. In the future we will collaborate with multiple centers to enhance participant numbers, thus ensuring more robust statistical analyses and facilitating subgroup analyses and validation across diverse patient groups. Secondly, the study only included patients scanned using the fixed b-value (0 and 800 s/mm\u003csup\u003e2\u003c/sup\u003e) in the study, which may introduce a certain bias when applied to other b-value scanning data. Future research will include patients scanned with various b-value. Incorporating data from different b-values reflects real-world variations in imaging protocols. It allows for a more comprehensive evaluation of ADC histogram metrics and their diagnostic performance in diverse clinical settings. Indeed, the absence of external validation groups is one of the limitations of our study. To improve the external validity of the diagnostic model future work will incorporate more data with different b-values.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe volumetric ADC histogram analysis holds significant importance in the differential diagnosis of mediastinal lymphomas and TETs. Furthermore, our diagnostic model, incorporating skewness, age, and LDH\u0026thinsp;\u0026gt;\u0026thinsp;250U/L, effectively distinguishes these entities, thereby enhancing the diagnostic confidence of radiologists.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eTETs thymic epithelial tumors \u003c/p\u003e\n\u003cp\u003eGCTs germ cell tumors \u003c/p\u003e\n\u003cp\u003eMRI magnetic resonance imaging \u003c/p\u003e\n\u003cp\u003eCT computed tomography \u003c/p\u003e\n\u003cp\u003eDWI diffusion-weighted imaging\u003c/p\u003e\n\u003cp\u003eADC apparent diffusion coefficient \u003c/p\u003e\n\u003cp\u003eVOI volume of interest \u003c/p\u003e\n\u003cp\u003eT1WI T1-weighted\u003c/p\u003e\n\u003cp\u003eT2W T2-weighted\u003c/p\u003e\n\u003cp\u003eDCE-T1WI dynamic contrast-enhanced T1-weight imaging\u003c/p\u003e\n\u003cp\u003eROI regions of interest\u003c/p\u003e\n\u003cp\u003eSD standard deviation \u003c/p\u003e\n\u003cp\u003eICC intraclass correlation coefficients \u003c/p\u003e\n\u003cp\u003eAUC areas under the curve\u003c/p\u003e\n\u003cp\u003eROC receiver operator characteristics\u003c/p\u003e\n\u003cp\u003eACC accuracy\u003c/p\u003e\n\u003cp\u003eVIF variance inflation factor\u003c/p\u003e\n\u003cp\u003eIDI integrated discriminatory improvement\u003c/p\u003e\n\u003cp\u003eDCA decision curve analyses \u003c/p\u003e\n\u003cp\u003eMG myasthenia gravis\u003c/p\u003e\n\u003cp\u003eLDH lactate dehydrogenase \u003c/p\u003e\n\u003cp\u003eOR odds ratio \u003c/p\u003e\n\u003cp\u003e\u0026alpha;-FP \u0026alpha;-fetoprotein\u003c/p\u003e\n\u003cp\u003e\u0026beta;- HCG \u0026beta;-human chorionic gonadotropin \u003c/p\u003e\n\u003cp\u003eHL Hodgkin\u0026apos;s lymphomaand \u003c/p\u003e\n\u003cp\u003eNHL non-Hodgkin\u0026apos;s lymphoma\u003c/p\u003e\n\u003cp\u003eNCI National Cancer Institute \u003c/p\u003e\n\u003cp\u003eSUVR standard uptake value ratio \u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis retrospective study was conducted in accordance with the Declaration of Helsinki and was approved by the institutional review board of Shanghai Chest Hospital, School of Medicine, Shanghai Jiao Tong University (No. IS23088). Individual consent for this retrospective analysis was waived.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe informed consent for publication was obtained from the participants and\u003c/p\u003e\n\u003cp\u003etheir legal guardian.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConception and design: Luna Wang, Yan Shen; Administrative support: Hong Yu, Lin Zhu; Provision of study materials or patients: Luna Wang, Huiyuan Zhu, Yu Zhang, Yan Shen; Collection and assembly of data:Luna Wang, Huiyuan Zhu, Yu Zhang, Yan Shen. Data analysis and interpretation:Luna Wang, Huiyuan Zhu, Lin Zhu, Hong Yu. Manuscript writing: All authors. Final approval of manuscript: All authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China (8207070786); Young Scientists Fund of the National Natural Science Foundation of China (82302188).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy regulations regarding patients.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMarx A, Chan JK, Coindre JM, Detterbeck F, Girard N, Harris NL, Jaffe ES, Kurrer MO, Marom EM, Moreira AL, Mukai K, Orazi A, Strobel P. 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Primary Soft-Tissue Lymphomas: MRI Features Help Discriminate From Other Soft-Tissue Tumors. Acad Radiol 2023;30:285-99.\u003c/li\u003e\n\u003cli\u003eAckman JB, Verzosa S, Kovach AE, Louissaint A, Jr., Lanuti M, Wright CD, Shepard JO, Halpern EF. High rate of unnecessary thymectomy and its cause. Can computed tomography distinguish thymoma, lymphoma, thymic hyperplasia, and thymic cysts? Eur J Radiol 2015;84:524-33.\u003c/li\u003e\n\u003cli\u003eAbdel Razek A, Khairy M, Nada N. Diffusion-weighted MR imaging in thymic epithelial tumors: correlation with World Health Organization classification and clinical staging. Radiology 2014;273:268-75.\u003c/li\u003e\n\u003cli\u003eThuy TTM, Trang NTH, Vy TT, Duc VT, Nam NH, Chien PC, Nhi LHH, Minh LHN. Role of diffusion-weighted MRI in differentiation between benign and malignant anterior mediastinal masses. Front Oncol 2022;12:985735.\u003c/li\u003e\n\u003cli\u003eZhang W, Zhou Y, Xu XQ, Kong LY, Xu H, Yu TF, Shi HB, Feng Q. 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Multiparameter diagnostic model based on (18)F-FDG PET and clinical characteristics can differentiate thymic epithelial tumors from thymic lymphomas. BMC Cancer 2022;22:895.\u003c/li\u003e\n\u003cli\u003eKirienko M, Ninatti G, Cozzi L, Voulaz E, Gennaro N, Barajon I, Ricci F, Carlo-Stella C, Zucali P, Sollini M, Balzarini L, Chiti A. Computed tomography (CT)-derived radiomic features differentiate prevascular mediastinum masses as thymic neoplasms versus lymphomas. Radiol Med 2020;125:951-60.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmim","sideBox":"Learn more about [BMC Medical Imaging](http://bmcmedimaging.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmim/default.aspx","title":"BMC Medical Imaging","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Thymic epithelial tumors, Mediastinal lymphoma, Magnetic resonance imaging, Diffusion weighted imaging, Histogram analysis","lastPublishedDoi":"10.21203/rs.3.rs-7688743/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7688743/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eThe management and prognosis of each type of anterior mediastinal mass differ substantially. Radical thymectomy is regarded as the preferred surgical approach for resectable TETs, whereas chemotherapy is the recommended treatment for mediastinal lymphoma after confirming the histological diagnosis through needle biopsy, and surgical procedures should be avoided. Consequently, an accurate diagnosis of mediastinal lymphoma and TETs holds paramount importance in clinical treatment and prognosis for patients with thymic neoplasms.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003ePatients of TETs and mediastinal lymphomas with histopathological proof were included in the present study. The ADC histogram parameters were extracted from ADC maps. Clinical characteristics, radiological features and ADC histogram metrics (incluning ADCmin, ADCmax, and ADCmean; 5th, 10th, 25th, 50th, 75th, 90th and 95th percentiles of ADC; skewness and kurtosis) were evaluated between two groups. Multivariate analyses were performed to identify the significant variables, which were then incorporated into a comprehensive diagnostic model. Receiver operator characteristics (ROC) curve analysis was subsequently carried out to evaluate diagnostic performance. A nomogram was developed to differentiate TETs and mediastinal lymphomas.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eA total of 130 consecutive patients, with 93 TET patients and 37 mediastinal lymphoma patients, were enrolled. It was observed that patients with mediastinal lymphomas exhibited a significantly younger age (38.11\u0026thinsp;\u0026plusmn;\u0026thinsp;13.51 years \u003cem\u003evs.\u003c/em\u003e 53.66\u0026thinsp;\u0026plusmn;\u0026thinsp;12.99 years, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and a significantly higher serum lactate dehydrogenase (LDH) elevation rate (54.1% \u003cem\u003evs.\u003c/em\u003e 2.2%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) compared to those with TETs. Furthermore, the maximal diameter of lesions and skewness were significantly larger in patients with mediastinal lymphoma, whereas 25th -95th percentile of ADC, ADCmax and ADCmean were significantly smaller compared to patients with TETs (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The comprehensive diagnostic model was established based on forward stepwise regression, including age, serum LDH level and skewness, with higher AUC than skewness alone (0.914, 95%CI: 0.850\u0026ndash;0.977 \u003cem\u003evs.\u003c/em\u003e 0.785, 95%CI: 0.701\u0026ndash;0.869, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). The predictive C-index nomogram performance was 0.917 (95%CI: 0.915\u0026ndash;0.918).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThe comprehensive diagnostic model which takes into account both ADC histogram parameters and clinical characteristics showed a promising value in the differential diagnosis of TETs and mediastinal lymphomas.\u003c/p\u003e","manuscriptTitle":"A Multiparameter Diagnostic Model Based on MRI Volumetric ADC Histogram and Clinical Variables Accurately Differentiate Thymic Epithelial Tumors From Mediastinal Lymphomas","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-15 02:40:24","doi":"10.21203/rs.3.rs-7688743/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-13T22:09:15+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-10T17:30:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"98170128420793725894171055455511645467","date":"2025-11-05T05:20:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"162991942460371722049057762713190522828","date":"2025-10-03T03:31:15+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-01T16:14:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"54220054931682191151192523931567445070","date":"2025-10-01T04:00:28+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-30T22:01:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-30T21:17:46+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-26T17:09:17+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-26T14:35:21+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Imaging","date":"2025-09-26T14:30:24+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmim","sideBox":"Learn more about [BMC Medical Imaging](http://bmcmedimaging.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmim/default.aspx","title":"BMC Medical Imaging","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5fe29e50-3bdb-4410-8423-cc96d9b64585","owner":[],"postedDate":"October 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-12T16:08:00+00:00","versionOfRecord":{"articleIdentity":"rs-7688743","link":"https://doi.org/10.1186/s12880-025-02147-5","journal":{"identity":"bmc-medical-imaging","isVorOnly":false,"title":"BMC Medical Imaging"},"publishedOn":"2026-01-07 15:56:59","publishedOnDateReadable":"January 7th, 2026"},"versionCreatedAt":"2025-10-15 02:40:24","video":"","vorDoi":"10.1186/s12880-025-02147-5","vorDoiUrl":"https://doi.org/10.1186/s12880-025-02147-5","workflowStages":[]},"version":"v1","identity":"rs-7688743","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7688743","identity":"rs-7688743","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}