Doubling time of thymic epithelial tumours on CT: correlation with histological subtype

Extracellular vesicles derived from bovine adipose-derived mesenchymal stromal cells enhance in vitro embryo production from lesioned ovaries

BACKGROUND AND AIMS

Ovum pick-up (OPU) is an intrinsic step of in vitro fertilization procedures. Nevertheless, it can cause ovarian lesions and compromise female fertility in bovines. Recently, we have shown that intraovarian injection of adipose-derived mesenchymal stromal cells (AD-MSCs) effectively preserves ovarian function in bovines. Given that MSC-derived extracellular vesicles (MSC-EVs) have been shown to recapitulate several therapeutic effects attributed to AD-MSCs and that they present logistic and regulatory advantages compared to AD-MSCs, we tested whether MSC-EVs would also be useful to treat OPU-induced lesions.

METHODS

MSC-EVs were isolated from the secretome of bovine AD-MSCs, using ultrafiltration (UF) and ultracentrifugation methods. The MSC-EVs were characterized according to concentration and mean particle size, morphology, protein concentration and EV markers, miRNA, mRNA, long noncoding RNA profile, total RNA yield and potential for induction of the proliferation and migration of bovine ovarian stromal cells. We then investigated whether intraovarian injection of MSC-EVs obtained by UF would reduce the negative effects of acute OPU-induced ovarian lesions in bovines. To do so, 20 animals were divided into 4 experimental groups (n = 5), submitted to 4 OPU cycles and different experimental treatments including vehicle only (G1), MSC-EVs produced by 7.5 × 106 AD-MSCs (G2), MSC-EVs produced by 2.5 × 106 AD-MSCs (G3) or 3 doses of MSC-EVs produced by 2.5 × 106 AD-MSCs, injected after OPU sessions 1, 2 and 3 (G4).

RESULTS

Characterization of the MSC-EVs revealed that the size of the particles was similar in the different isolation methods; however, the UF method generated a greater MSC-EV yield. MSC-EVs processed by both methods demonstrated a similar ability to promote cell migration and proliferation in ovarian stromal cells. Considering the higher yield and lower complexity of the UF method, UF-MSC-EVs were used in the in vivo experiment. We evaluated three therapeutic regimens for cows subjected to OPU, noting that the group treated with three MSC-EV injections (G4) maintained oocyte production and increased in vitro embryo production, compared to G1, which presented compromised embryo production following the OPU-induced lesions.

CONCLUSIONS

MSC-EVs have beneficial effects both on the migration and proliferation of ovarian stromal cells and on the fertility of bovines with follicular puncture injury in vivo.

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

AIM

To investigate the diagnostic performance of computed tomography (CT)-guided percutaneous transthoracic needle biopsy (PTNB) for thymic epithelial tumours (TETs) and the complication rate after PTNB including seeding after PTNB.

MATERIALS AND METHODS

This retrospective study identified PTNBs for anterior mediastinal lesions between May 2007 and September 2021. The diagnostic performance for TETs and complications were investigated. The concordance of the histological grades of TETs between PTNB and surgery was evaluated. The factors associated with pleural seeding after PTNB were determined using Cox regression analysis.

RESULTS

Of 387 PTNBs, 235 PTNBs from 225 patients diagnosed as TETs (124 thymomas and 101 thymic carcinomas) and 150 PTNBs from 133 patients diagnosed as other than TETs were included. The sensitivity, specificity, and accuracy for TETs were 89.4% (210/235), 100% (210/210), and 93.5% (360/385), respectively, with an immediate complication rate of 4.4% (17/385). The concordance rate of the histological grades between PTNB and surgery was 73.3% (77/105) after excluding uncategorised types of thymomas. During follow-up after PTNB (median duration, 38.8 months; range, 0.3-164.6 months), no tract seeding was observed. Pleural seeding was observed in 26 patients. Thymic carcinoma (hazard ratio [HR], 5.94; 95% confidence interval [CI], 2.07-17.08; p=0.001) and incomplete resection (HR, 3.29; 95% CI, 1.20-9.02; p=0.02) were associated with pleural seeding, while the biopsy approach type (transpleural versus parasternal) was not associated (p=0.12).

CONCLUSIONS

Pretreatment biopsy for TETs was accurate and safe and may be considered for diagnosing TETs, particularly when the diagnosis is challenging and histological diagnosis is mandatory.

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

BACKGROUND

Thymic epithelial tumors (TETs) are the most common primary neoplasms of the anterior mediastinum. Different risk subgroups of TETs have different prognosis and therapeutic strategies, therefore, preoperative identification of different risk subgroups is of high clinical significance. This study aims to explore the diagnostic efficiency of quantitative computed tomography (CT) parameters combined with preoperative systemic inflammatory markers in differentiating low-risk thymic epithelial tumors (LTETs) from high-risk thymic epithelial tumors (HTETs).

METHODS

74 Asian patients with TETs confirmed by biopsy or postoperative pathology between January 2013 and October 2022 were collected retrospectively and divided into two risk subgroups: LTET group (type A, AB and B1 thymomas) and HTET group (type B2, B3 thymomas and thymic carcinoma). Statistical analysis were performed between the two groups in terms of quantitative CT parameters and preoperative systemic inflammatory markers. Multivariate logistic regression analysis was used to determine the independent predictors of risk subgroups of TETs. The area under curve (AUC) and optimal cut-off values were calculated by receiver operating characteristic (ROC) curves.

RESULTS

47 TETs were in LTET group, while 27 TETs were in HTET group. In addition to tumor size and CT value of the tumor on plain scan, there were statistical significance comparing in CT value of the tumor on arterial phase (CTv-AP) and venous phase (CTv-VP), and maximum enhanced CT value (CEmax) of the tumor between the two groups (for all, P < 0.05). For systemic inflammatory markers, HTET group was significantly higher than LTET group (for all, P < 0.05), including platelet-to-lymphocyte ratio (PLR), neutrophil-to-lymphocyte ratio (NLR) and systemic immune-inflammation index (SII). Multivariate logistic regression analysis showed that NLR (odds ratio [OR] = 2.511, 95% confidence interval [CI]: 1.322-4.772, P = 0.005), CTv-AP (OR = 0.939, 95%CI: 0.888-0.994, P = 0.031) and CTv-VP (OR = 0.923, 95%CI: 0.871-0.979, P = 0.008) were the independent predictors of risk subgroups of TETs. The AUC value of 0.887 for the combined model was significantly higher than NLR (0.698), CTv-AP (0.800) or CTv-VP (0.811) alone. The optimal cut-off values for NLR, CTv-AP and CTv-VP were 2.523, 63.44 Hounsfeld Unit (HU) and 88.29HU, respectively.

CONCLUSIONS

Quantitative CT parameters and preoperative systemic inflammatory markers can differentiate LTETs from HTETs, and the combined model has the potential to improve diagnostic efficiency and to help the patient management.

Development and validation of a deep learning radiomics nomogram for preoperatively differentiating thymic epithelial tumor histologic subtypes

OBJECTIVES

Using contrast-enhanced computed tomography (CECT) and deep learning technology to develop a deep learning radiomics nomogram (DLRN) to preoperative predict risk status of patients with thymic epithelial tumors (TETs).

METHODS

Between October 2008 and May 2020, 257 consecutive patients with surgically and pathologically confirmed TETs were enrolled from three medical centers. We extracted deep learning features from all lesions using a transformer-based convolutional neural network and created a deep learning signature (DLS) using selector operator regression and least absolute shrinkage. The predictive capability of a DLRN incorporating clinical characteristics, subjective CT findings and DLS was evaluated by the area under the curve (AUC) of a receiver operating characteristic curve.

RESULTS

To construct a DLS, 25 deep learning features with non-zero coefficients were selected from 116 low-risk TETs (subtypes A, AB, and B1) and 141 high-risk TETs (subtypes B2, B3, and C). The combination of subjective CT features such as infiltration and DLS demonstrated the best performance in differentiating TETs risk status. The AUCs in the training, internal validation, external validation 1 and 2 cohorts were 0.959 (95% confidence interval [CI]: 0.924-0.993), 0.868 (95% CI: 0.765-0.970), 0.846 (95% CI: 0.750-0.942), and 0.846 (95% CI: 0.735-0.957), respectively. The DeLong test and decision in curve analysis revealed that the DLRN was the most predictive and clinically useful model.

CONCLUSIONS

The DLRN comprised of CECT-derived DLS and subjective CT findings showed a high performance in predicting risk status of patients with TETs.

CLINICAL RELEVANCE STATEMENT

Accurate risk status assessment of thymic epithelial tumors (TETs) may aid in determining whether preoperative neoadjuvant treatment is necessary. A deep learning radiomics nomogram incorporating enhancement CT-based deep learning features, clinical characteristics, and subjective CT findings has the potential to predict the histologic subtypes of TETs, which can facilitate decision-making and personalized therapy in clinical practice.

KEY POINTS

• A non-invasive diagnostic method that can predict the pathological risk status may be useful for pretreatment stratification and prognostic evaluation in TET patients. • DLRN demonstrated superior performance in differentiating the risk status of TETs when compared to the deep learning signature, radiomics signature, or clinical model. • The DeLong test and decision in curve analysis revealed that the DLRN was the most predictive and clinically useful in differentiating the risk status of TETs.

Prognosis of Concurrent Versus Sequential Chemo-Radiotherapy Induction Followed by Surgical Resection in Patients with Advanced Thymic Epithelial Tumors: A Retrospective Study

BACKGROUND

This study aimed to evaluate the prognosis of concurrent chemo-radiotherapy (CCRT) versus sequential chemo-radiotherapy (SCRT) induction followed by surgical resection in patients with advanced thymic epithelial tumors (TETs).

METHODS

This retrospective study included patients with advanced TETs who underwent CCRT or SCRT induction followed by surgical resection at the Second General Hospital of Guangdong Province between January 2008 and December 2019. The primary outcomes were induction response rate and surgical complete resection rate. The secondary outcomes were surgery combined resection, post-induction T staging, postoperative TNM staging, postoperative pathological tumor regression grade, progression-free survival (PFS) and overall survival (OS), and adverse events (AEs).

RESULTS

A total of 31 patients were included, 15 of whom received CCRT and the other 16 SCRT. The induction response rates were 80.0 and 62.5%, respectively, the post-induction step-down rates were 46.7 and 31.3%, respectively, and the post-induction R0 resection rates were 80.0 and 68.8%, respectively, without significant differences between CCRT and SCRT groups (all P > 0.05). The 5-year OS rate was 64.2 and 51.6%, respectively, and PFS was 42.3 and 21.4%, respectively, without significant differences between CCRT and SCRT groups (both P > 0.05). AEs in the hematologic system were significantly higher with CCRT compared with SCRT (P = 0.009).

CONCLUSIONS

Patients with advanced TETs might have a good prognosis with both CCRT and SCRT induction therapy, while SCRT induction may result in a lower probability of AEs in the hematologic system.

Characteristics and outcomes of anterior mediastinal cystic lesions diagnosed on chest MRI: implications for management of cystic lesions

BACKGROUND

Chest MRI is a useful diagnostic modality for the evaluation of anterior mediastinal lesions but the outcomes of anterior mediastinal cystic lesions diagnosed on chest MRI are unclear.

METHODS

In this multicenter retrospective study, patients who underwent contrast-enhanced chest MRI in two tertiary centers to assess anterior mediastinal cystic lesions were included after excluding overt solid tumors and thymic hyperplasia. Anterior mediastinal cystic lesions were classified into two categories: probable (simple) cyst or indeterminate lesion (complex cyst). Size and imaging features of lesions during follow-up were evaluated and clinical outcomes were assessed.

RESULTS

A total of 204 patients (mean age, 59 ± 11 years; M:F = 111:93) were studied; 186 (91.2%) were classified as probable cysts and 18 (8.8%) as indeterminate lesions on MRI. Among patients with probable cysts and more than 2 years of follow-up, lesion size was unchanged in 39.6% (36/91), decreased in 16.5% (15/91), and fluctuated in 8.8% (8/91). All patients who underwent surgery were confirmed cysts. None developed mural nodules or irregular wall thickening, suspicious for malignancy during follow-up. In patients with indeterminate lesions, 16.7% (3/18) had pathologically confirmed thymoma and 44.4% (8/18) had proven cysts. Follow-up numbers and intervals after MRI in patients with probable cysts were variable among physicians and institutions in clinical practice (p < 0.05) but more than half were followed for up to 2 years in two centers.

CONCLUSION

Diagnosing anterior mediastinal cysts using MRI is reliable. MRI-based management of anterior mediastinal lesions may reduce the number of unnecessary follow-ups and surgeries.

Using CT to evaluate mediastinal great vein invasion by thymic epithelial tumors: measurement of the interface between the tumor and neighboring structures

OBJECTIVES

For thymic epithelial tumors, simple contact with adjacent structures does not necessarily mean invasion. The purpose of our study was to develop a simple noninvasive technique for evaluating organ invasion using routine pretreatment computed tomography (CT).

METHODS

This retrospective study analyzed the pathological reports on 95 mediastinal resections performed between January 2003 and June 2020. Using CT images, the length of the interface between the primary tumor and neighboring structures (arch distance; Adist) and maximum tumor diameter (Dmax) was measured, after which Adist/Dmax (A/D) ratios were calculated. Receiver operating characteristic (ROC) curves were used to analyze the Adist and A/D ratios.

RESULTS

An Adist cut-off of 37.5 mm best distinguished between invaded and non-invaded mediastinal great veins based on ROC curves. When Adist > 37.5 mm was used for diagnosis of invasion of the brachiocephalic vein (BCV) or superior vena cava (SVC), the sensitivity, specificity, positive predictive value, negative predictive value, accuracy, and area under the ROC curve for diagnosis of invasion were 61.9%, 92.5%, 81.25%, 82.2%, 81.97%, and 0.76429, respectively. Moreover, there were significant differences between BCV/SVC Adist > 37.5 mm and ≤ 37.5 mm for 10-year relapse-free survival and 10-year overall survival (p < 0.01).

CONCLUSIONS

When diagnosing invasion of the mediastinal great veins based on Adist > 37.5 mm, we achieved a higher performance level than the conventional criteria such as irregular interface with an absence of the fat layer. Measurement of Adist is a simple noninvasive technique for evaluating invasion using CT. Key Points • Simple contact between the primary tumor and adjacent structures on CT does not indicate direct invasion. • Using CT images, the length of the interface between the primary tumor and neighboring structures (arch distance; Adist) is a simple noninvasive technique for evaluating invasion. • Adist > 37.5 mm can be a supportive tool to identify invaded mediastinal great veins and surgical indications for T3 and T4 invasion by thymic epithelial tumors.

Acute Angle of Multilobulated Contours Improves the Risk Classification of Thymomas

Background: Computed tomography plays an important role in the identification and characterization of thymomas. It has been mainly used during preoperative evaluation for clinical staging. However, the reliable prediction of histological risk types of thymomas based on CT imaging features requires further study. In this study, we developed and validated a nomogram based on CT imaging and included new indices for individualized preoperative prediction of the risk classification of thymomas.

Methods: We conducted a retrospective, multicenter study that included 229 patients from two Chinese medical centers. All the patients underwent cross-sectional CT imaging within 2 weeks before surgery. The results of pathological assessments were retrieved from existing reports of the excised lesions. The tumor perimeter that contacted the lung (TPCL) was evaluated and a new quantitative indicator, the acute angle (AA) formed by adjacent lobulations, was measured. Two predictive models of risk classification were created using the least absolute shrinkage and selection operator (LASSO) method in a training cohort for features selection. The model with a smaller Akaike information criterion was then used to create an individualized imaging nomogram, which we evaluated regarding its prediction ability and clinical utility.

Results: A new CT imaging-based model incorporating AA was developed and validated, which had improved predictive performance during risk classification of thymomas when compared with a model using traditional imaging predictors. The new imaging nomogram with AA demonstrated its clinical utility by decision curve analysis.

Conclusions: Acute angle can improve the performance of a CT-based predictive model during the preoperative risk classification of thymomas and should be considered a new imaging marker for the evaluation and treatment of patients with thymomas. On the contrary, TPCL is not useful as a predictor for the risk classification of thymomas in this study.

Development and validation of a CT-texture analysis nomogram for preoperatively differentiating thymic epithelial tumor histologic subtypes

BACKGROUND

Thymic epithelial tumors (TETs) are the most common primary tumors in the anterior mediastinum, which have considerable histologic heterogeneity. This study aimed to develop and validate a nomogram based on computed tomography (CT) and texture analysis (TA) for preoperatively predicting the pathological classifications for TET patients.

METHODS

Totally TET 172 patients confirmed by postoperative pathology between January 2011 to April 2019 were retrospectively analyzed and randomly divided into training (n = 120) and validation (n = 52) cohorts. Preoperative clinical factors, CT signs and texture features of each patient were analyzed, and prediction models were developed using the least absolute shrinkage and selection operator (LASSO) regression. The performance of the models was evaluated and compared by the area under receiver-operator characteristic (ROC) curve (AUC) and the DeLong test. The clinical application value of the models was determined via the decision curve analysis (DCA). Then, a nomogram was developed based on the model with the best predictive efficiency and clinical utility and validated using the calibration plots.

RESULTS

Totally 87 patients with low-risk TET (LTET) (types A, AB, B1) and 85 patients with high-risk TET (HTET) (types B2, B3, C) were enrolled in this study. We separately constructed 4 prediction models for differentiating LTET from HTET using clinical, CT, texture features, and their combination. These 4 prediction models achieved AUCs of 0.66, 0.79, 0.82, 0.88 in the training cohort and 0.64, 0.82, 0.86, 0.94 in the validation cohort, respectively. The DeLong test and DCA showed that the Combined model, consisting of 2 CT signs and 2 texture parameters, held the highest predictive efficiency and clinical utility (p < 0.05). A prediction nomogram was subsequently developed using the 4 independently risk factors from the Combined model. The calibration curves indicated a good consistency between the actual observations and nomogram predictions for differentiating TET classifications.

CONCLUSION

A prediction nomogram incorporating both the CT and texture parameters was constructed and validated in our study, which can be conveniently used for the preoperative individualized prediction of the simplified histologic subtypes in TET patients.

Risk stratification of thymic epithelial tumors by using a nomogram combined with radiomic features and TNM staging

OBJECTIVES

To construct and validate a nomogram model that integrated the CT radiomic features and the TNM staging for risk stratification of thymic epithelial tumors (TETs).

METHODS

A total of 136 patients with pathology-confirmed TETs who underwent CT examination were collected from two institutions. According to the WHO pathological classification criteria, patients were classified into low-risk and high-risk groups. The TNM staging was determined in terms of the 8th edition AJCC/UICC staging criteria. LASSO regression was performed to extract the optimal features correlated to risk stratification among the 704 radiomic features calculated. A nomogram model was constructed by combining the Radscore and the TNM staging. The clinical performance was evaluated by ROC analysis, calibration curve, and decision curve analysis (DCA). The Kaplan-Meier (KM) analysis was employed for survival analysis.

RESULTS

Five optimal features identified by LASSO regression were employed to calculate the Radscore correlated to risk stratification. The nomogram model showed a better performance in both training cohort (AUC = 0.84, 95%CI 0.75-0.91) and external validation cohort (AUC = 0.79, 95%CI 0.69-0.88). The calibration curve and DCA analysis indicated a better accuracy of the nomogram model for risk stratification than either Radscore or the TNM staging alone. The KM analysis showed a significant difference between the two groups stratified by the nomogram model (p = 0.02).

CONCLUSIONS

A nomogram model that integrated the radiomic signatures and the TNM staging could serve as a reliable model of risk stratification in predicting the prognosis of patients with TETs.

KEY POINTS

• The radiomic features could be associated with the TET pathophysiology. • TNM staging and Radscore could independently stratify the risk of TETs. • The nomogram model is more objective and more comprehensive than previous methods.

Dual-energy CT perfusion imaging for differentiating WHO subtypes of thymic epithelial tumors

To evaluate the role of conventional contrast-enhanced CT (CECT) imaging and dual-energy spectral CT (DECT) perfusion imaging in differentiating the WHO histological subtypes of thymic epithelial tumours (TETs). Eighty-eight patients with TETs who underwent DECT perfusion scans (n = 51) and conventional CT enhancement scans (n = 37) using a GE Discovery CT750 HD scanner were enrolled in this study. The mean maximal contrast-enhanced range (mean CEmax) and the perfusion and spectral parameters of the lesions were analysed. Among the six WHO subtypes (Type A, AB, B1, B2, and B3 thymoma and thymic carcinoma), the mean CEmax values and most of the perfusion and spectral parameter values of Type A and Type AB were significantly higher than those of the other subtypes (all P < 0.05), and there was no difference among Type B1, B2 and B3 (all P > 0.05). The mean CEmax value was not different between Type B (including Type B1, B2, and B3) and thymic carcinoma (P = 1.000). The PS, IC, NIC and λHU values in the optimal venous phase of thymic carcinoma were higher than those of Type B (all P < 0.05). The parameters of conventional CECT imaging and DECT perfusion imaging can help identify the subtype of TETs, especially those of DECT perfusion imaging in type B thymomas and thymic carcinomas.

A radiomics model to predict the invasiveness of thymic epithelial tumors based on contrast‑enhanced computed tomography

In the present study, we aimed to construct a radiomics model using contrast‑enhanced computed tomography (CT) to predict the pathological invasiveness of thymic epithelial tumors (TETs). We retrospectively reviewed the records of 179 consecutive patients (89 females) with histologically confirmed TETs from two hospitals. The 82 low‑ and 97 high‑risk TETs were assigned to training (90 tumors), internal validation (49 tumors) and external validation (40 tumors) cohorts. Radiomics features extracted from preoperative contrast‑enhanced chest CT were selected using least absolute shrinkage and selection operator logistic regression. Three prediction models were developed using multivariate logistic regression analysis. Their performance and clinical utility were assessed using receiver operating characteristic curves and the DeLong test, respectively. Eight radiomics features with non‑zero coefficients were used to develop a radiomics score, which significantly differed between low‑ and high‑risk TETs (P<0.001). The subjective finding, infiltration, was independently associated with high‑risk TETs. Prediction models based on infiltration alone, the radiomics signature alone, and both these parameters showed diagnostic accuracies of 72.2% [area under curve (AUC), 0.731; 95% confidence interval (CI): 0.627‑0.819; sensitivity, 85.7%; specificity, 60.4%], 88.9% (AUC, 0.944; 95% CI: 0.874‑0.981; sensitivity, 92.9%; specificity, 85.4%), and 90.0% (AUC, 0.953; 95% CI: 0.887‑0.987; sensitivity, 92.9%; specificity, 87.5%), respectively. Decision‑curve analysis showed that the combined model added more net benefit than the single‑parameter models. In conclusion, a radiomics signature based on contrast‑enhanced CT has the potential to differentiate between low‑ and high‑risk TETs. The model incorporating the radiomics signature and subjective finding may facilitate the individualized, preoperative prediction of the pathological invasiveness of TETs.

Relationship Between Computed Tomography Imaging Features and Clinical Characteristics, Masaoka-Koga Stages, and World Health Organization Histological Classifications of Thymoma

Objectives: Computed tomography (CT) is an important technique for evaluating the condition and prognosis of patients with thymomas, and it provides guidance regarding treatment strategies. However, the correlation between CT imaging features, described using standard report terms, and clinical characteristics, Masaoka–Koga stages, and World Health Organization (WHO) classifications of patients with thymomas has not been described in detail nor has risk factor analysis been conducted.

Methods: Overall, 159 patients with thymomas who underwent preoperative contrast-enhanced CT between September 2011 and December 2018 were retrospectively reviewed. We assessed the clinical information, CT imaging features, and pathological findings for each patient. A total of 89 patients were specially used to evaluate postoperative recurrence or metastasis between September 2011 and December 2015 to obtain an appropriate observation period. The relationship between CT imaging features and clinical characteristics, Masaoka–Koga stage, and WHO histological classification were analyzed, and related risk factors based on CT imaging features were identified.

Results: CT imaging features did not significantly differ based on sex or age. Some imaging features demonstrated significant differences between the groups with and without related clinical characteristics. Contour (odds ratio [OR] = 3.711, P = 0.005), abutment ≥50% (OR = 4.277, P = 0.02), and adjacent lung abnormalities (OR = 3.916 P = 0.031) were independent risk factors for relapse or metastasis. Among all imaging features, there were significant differences between stage I/II and III/IV lesions in tumor size, calcification, infiltration of surrounding fat, vascular invasion, pleural nodules, elevated hemidiaphragm, and pulmonary nodules. Tumor size (odds ratio = 1.261, P = 0.014), vascular invasion (OR = 2.526, P = 0.023), pleural nodules (OR = 2.22, P = 0.048), and pulmonary nodules (OR = 3.106, P = 0.006) were identified as independent risk factors. Tumor size, contour, internal density, infiltration of surrounding fat, and pleural effusion significantly differed between low- and high-risk thymomas. Tumor size (OR = 1.183, P = 0.048), contour (OR = 2.288, P = 0.003), internal density (OR = 2.192, P = 0.024), and infiltration of surrounding fat (OR = 2.811 P = 0.005) were independent risk factors.

Conclusions: Some CT imaging features demonstrated significant correlations with clinical characteristics, Masaoka–Koga clinical stages, and WHO histological classifications in patients with thymomas. Familiarity with CT features identified as independent risk factors for these related clinical characteristics can facilitate preoperative evaluation and treatment management for the patients with thymoma.

The tumor doubling time is a useful parameter for predicting the histological type of thymic epithelial tumors

PURPOSE

We assessed the utility of the tumor doubling time (TDT) for predicting the histological type of thymic epithelial tumors.

METHODS

We retrospectively reviewed 130 patients with thymic epithelial tumors who underwent computed tomography two or more times before surgery. The patients were divided into low-risk thymoma (types A, AB and B1), high-risk thymoma (types B2 and B3) and thymic carcinoma (thymic carcinoma and thymic neuroendocrine tumor) groups. In the 96 patients who showed tumor enlargement, the relationship between the histological type and the TDT of the tumor was investigated.

RESULTS

The study population included 55 men and 41 women from 26 to 82 years of age. The TDT of the thymic carcinoma group (median 205 days) was significantly shorter in comparison to the low-risk thymoma (median 607 days) and high-risk thymoma (median 459 days) groups. No significant differences were observed between the low-risk thymoma and high-risk thymoma groups. When we set the cutoff time for differentiating thymic carcinoma group from thymoma at 313 days, the sensitivity and specificity were 83.8% and 82.1%, respectively.

CONCLUSIONS

The TDT is a useful parameter for differentiating between thymoma and thymic carcinoma group.

Screening for Antiviral Medaka Haploid Embryonic Stem Cells by Genome Wide Mutagenesis

Nervous necrosis virus (NNV), one of the most prevalent fish pathogens, has caused significant losses in both yield and economy to the aquaculture. Host factors involved in NNV infection remain to be identified due to the lack of ideal model for the study of NNV and host interaction. Haploid stem cells have proven to be ideal materials in genetic screens. Here, we generated a cell line HX1G1 (simply named G1) with the activity against red-spotted grouper nervous necrosis virus (RGNNV) by N-ethyl-N-nitrosourea (ENU)-mediated whole genome random mutagenesis from the haploid embryonic stem cell HX1a, a cell clone from haploid cell line HX1 that we previously derived from the medaka fish. G1 cells retained the characteristics of haploidy and pluripotency as indicated by the EBs differentiation ability after genetic mutagenesis. Compared with HX1a cells, no typical cytopathic effects were observed, and the expression of RNA-dependent RNA polymerase (RDRP) was significantly reduced in G1 cells post RGNNV infection, indicating the enhanced anti-RGNNV activity of G1. Furthermore, we demonstrated that RGNNV entry into G1 cells was partially inhibited, and this inhibition might be relevant to the induced mutation of heat shock cognate protein 70 (HSC70) which was decisive for NNV entry. Interestingly, G1 cells were to some extent permissive to RGNNV infection, but RGNNV was spontaneously cleared in G1 cells during serial passage. In addition, we also found that the expression levels of interferon (IFN)-related genes were higher in G1 cells than those in HX1a cells, suggesting that viral clearance might be associated with the elevated expression of IFN-related genes in G1 cells.

Solid component tumor doubling time is a prognostic factor in non-small cell lung cancer patients

Background

Recently, several reports investigating tumor doubling times (TDTs) in lung cancer have demonstrated that lung cancer patients with shorter TDTs have poor prognoses. Although data have shown that the solid component of a tumor is clinically more important, relationships between solid component TDTs and lung cancer prognoses remain unclear.

Methods

To evaluate relationships between TDT and survival, we retrospectively evaluated 231 patients who underwent surgical resection for non-small cell lung cancer. The TDTs of whole and solid components were calculated using preoperative thin-slice chest computed tomography scans with a cut-off of 400 d between scans.

Results

Patients with short TDTs (< 400 d) both in the solid and whole components had poor prognoses. Among pathological stage I patients (n = 176), short solid component TDT (< 400 d) significantly influenced prognosis only in pathological stage IB patients. Moreover, we found that patients with shorter solid component TDTs had significantly worse prognosis compared with patients who showed shorter whole component TDTs.

Conclusions

Short solid component TDTs (< 400 d) could be a poor prognostic indicator for non-small cell lung cancer patients undergoing surgical resection; furthermore, clinicians should pay particularly close attention to cases with rapid growth of the solid tumor component.

Growth of thymic epithelial tumors and thymic cysts: Differential radiological points

BACKGROUND

The growth rate of thymic epithelial tumors (TETs) and thymic cysts was investigated to determine whether they can be differentiated and clinico-radiological predictors of interval growth was identified.

METHODS

This retrospective study included 122 patients with pathologically proven thymic cysts (n = 56) or TETs (n = 66) who underwent two serial chest computed tomography scans at least eight weeks apart. Average diameters and attenuation were measured, volume-doubling times (VDTs) were calculated, and clinical characteristics were recorded. VDTs were compared using the log-rank test. Predictors of growth were analyzed using the log-rank test and Cox regression analysis.

RESULTS

The frequency of growth did not differ significantly between TETs and thymic cysts (P = 0.279). The VDT of thymic cysts (median 324 days) was not significantly different from that of the TETs (median 475 days; P = 0.808). Water attenuation (≤ 20 Hounsfield units) predicted growth in thymic cysts (P = 0.016; hazard ratio 13.2, 95% confidence interval 1.6-107.3), while lesion size (> 17.2 mm) predicted growth in TETs (P = 0.008 for size, P = 0.029 for size*time). For the growing lesions, the positive and negative predictive values of water attenuation for thymic cysts were 93% and 80%, respectively.

CONCLUSION

The frequencies of interval growth and VDTs were indistinguishable between TETs and thymic cysts. Water attenuation and lesion size predicted growth in thymic cysts and TETs, respectively. Among the growing lesions, water attenuation was a differential feature of thymic cysts.

Volumetric analysis of the thymic epithelial tumors: correlation of tumor volume with the WHO classification and Masaoka staging

BACKGROUND

The purpose of our study was to investigate the correlation between tumor volume (TV) and each subtype of thymic epithelial tumors (TETs) based on the World Health Organization (WHO) classification and Masaoka staging.

METHODS

Sixty-one consecutive patients (45 thymomas and 16 thymic carcinomas) were studied. All were classified according to Masaoka staging: 31 non-invasive TETs (stage I) and 30 invasive TETs (8 stage II, 11 stage III, 3 stage IVa, and 8 stage IVb). TV on computed tomography (CT) were semi-automatically calculated using our software. The correlation of TV with each WHO subtype and Masaoka staging was analyzed using Mann-Whitney U and Scheffe's F test.

RESULTS

Thymic carcinoma (mean ± SD, 117.5±143.6 cm3) was significantly larger than thymoma (53.4±78.4 cm3) (P=0.0016). Stage IVb tumor (190.8±156.8 cm3) was significantly larger than stage I (33.1±42.6 cm3) (P<0.05). Invasive TETs were significantly larger than non-invasive TETs (P=0.0016). TV >54.3 cm3 indicated invasive TETs.

CONCLUSIONS

TV of invasive TETs may be larger at the time of initial presentation. TV >54.3 cm3 indicates invasive TETs.

Role of the Apparent Diffusion Coefficient as a Predictor of Tumor Progression in Patients with Chordoma

BACKGROUND AND PURPOSE

Diffusion-weighted imaging may aid in distinguishing aggressive chordoma from nonaggressive chordoma. This study explores the prognostic role of the apparent diffusion coefficient in chordomas.

MATERIALS AND METHODS

Sixteen patients with residual or recurrent chordoma were divided postoperatively into those with an aggressive tumor, defined as a growing tumor having a doubling time of <1 year, and those with a nonaggressive tumor on follow-up MR images. The ability of the ADC to predict an aggressive tumor phenotype was investigated by receiver operating characteristic analysis. The prognostic role of ADC was assessed using a Kaplan-Meier curve with a log-rank test.

RESULTS

Seven patients died during a median follow-up of 48 months (range, 4-126 months). Five of these 7 patients were in the aggressive tumor group, and 2 were in the nonaggressive tumor group. The mean ADC was significantly lower in the aggressive tumor group than in the nonaggressive tumor group (P = .002). Receiver operating characteristic analysis showed that a cutoff ADC value of 1.494 × 10^-3 × mm²/s could be used to diagnose aggressive tumors with an area under the curve of 0.983 (95% CI, 0.911-1.000), a sensitivity of 1.000 (95% CI, 0.541-1.000), and a specificity of 0.900 (95% CI, 0.555-0.998). Furthermore, a cutoff ADC of ≤1.494 × 10^-3 × mm²/s was associated with a significantly worse prognosis (P = .006).

CONCLUSIONS

Lower ADC values could predict tumor progression in postoperative chordomas.

Incidental Anterior Mediastinal Nodular Lesions on Chest CT in Asymptomatic Subjects

OBJECTIVE

The aim of this study was to investigate the prevalence and characteristics of nodular lesions in the anterior mediastinum that had been found incidentally on screening chest computed tomography (CT) in asymptomatic subjects.

METHODS

We included 56,358 consecutive participants (mean age 52.4 ± 10.5 years; male-female ratio 35,306:21,052) who underwent a baseline low-dose chest CT scan as part of a health checkup from 2006 through 2013. After the presence of anterior mediastinal nodular lesion had been confirmed, their CT findings, confirmatory diagnosis, and interval CT scan were reviewed. The standardized prevalence ratio for thymic epithelial tumor was calculated on the basis of the Republic of Korea cancer statistics for 2014.

RESULTS

Of the 56,358 participants, 413 (0.73%) had lesions (95% confidence interval: 0.66-0.80%); the prevalence increased with age (p <0.001) and a history of malignancy (p = 0.005). Of the lesions, 85.2% were smaller than 2 cm, 61.3% were round, and 80.2% had CT attenuation higher than 20 Hounsfield units. Among 51 proven cases, 39 lesions (76.9%) were benign and 12 (23.1%) were malignant. The standardized prevalence ratio for thymic epithelial tumor was 2.04 (95% confidence interval: 1.01-3.42). Of 11 resected thymic epithelial tumors, five were carcinomas, 10 were stage I or II, and all were completely resected without recurrence. Of the 237 unconfirmed cases with a follow-up CT scan, 82.2% were stable, 8.9% had increased, and the other 8.9% had decreased.

CONCLUSIONS

The prevalence of incidental nodular lesion was 0.73%. Most lesions had CT features that were indistinguishable from thymic epithelial tumors, but a considerable portion of the lesions were suspected to be benign. Incidental thymic epithelial tumors were more prevalent than clinically detected tumors, were early-stage cancer, and showed favorable outcomes.