Risk factor analysis for major mediastinal vessel invasion in thymic epithelial tumors based on multi-slice CT Imaging.

Objective: To explore the characteristics and risk factors for major mediastinal vessel invasion in different risk grades of thymic epithelial tumors (TETs) based on computed tomography (CT) imaging, and to develop prediction models of major mediastinal artery and vein invasion. Methods: One hundred and twenty-two TET patients confirmed by histopathological analysis who underwent thorax CT were enrolled in this study. Clinical and CT data were retrospectively reviewed for these patients. According to the abutment degree between the tumor and major mediastinal vessels, the arterial invasion was divided into grade I, II, and III (< 25%, 25 - 49%, and ≥ 50%, respectively); the venous invasion was divided into grade I and II (< 50% and ≥ 50%). The degree of vessel invasion was compared among different defined subtypes or stages of TETs using the chi-square tests. The risk factors associated with TET vascular invasion were identified using multivariate logistic regression analysis. Results: Based on logistic regression analysis, male patients (ß = 1.549; odds ratio, 4.824) and the pericardium or pleural invasion (ß = 2.209; odds ratio, 9.110) were independent predictors of 25% artery invasion, and the midline location (ß = 2.504; odds ratio, 12.234) and mediastinal lymphadenopathy (ß = 2.490; odds ratio, 12.06) were independent predictors of 50% artery invasion. As for 50% venous invasion, the risk factors include midline location (ß = 2.303; odds ratio, 10.0), maximum tumor diameter larger than 5.9 cm (ß = 4.038; odds ratio, 56.736), and pericardial or pleural effusion (ß = 1.460; odds ratio, 4.306). The multivariate logistic model obtained relatively high predicting efficacy, and the area under the curve (AUC), sensitivity, and specificity were 0.944, 84.6%, and 91.7% for predicting 50% artery invasion, and 0.913, 81.8%, and 86.0% for 50% venous invasion in TET patients, respectively. Conclusion: Several CT features can be used as independent predictors of ≥50% artery or venous invasion. A multivariate logistic regression model based on CT features is helpful in predicting the vascular invasion grades in patients with TET.

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

PURPOSE: This study aimed to establish and compare the radiomics machine learning (ML) models based on non-contrast enhanced computed tomography (NECT) and clinical features for predicting the simplified risk categorization of thymic epithelial tumors (TETs). EXPERIMENTAL DESIGN: A total of 509 patients with pathologically confirmed TETs from January 2009 to May 2018 were retrospectively enrolled, consisting of 238 low-risk thymoma (LRT), 232 high-risk thymoma (HRT), and 39 thymic carcinoma (TC), and were divided into training (n = 433) and testing cohorts (n = 76) according to the admission time. Volumes of interest (VOIs) covering the whole tumor were manually segmented on preoperative NECT images. A total of 1218 radiomic features were extracted from the VOIs, and 4 clinical variables were collected from the hospital database. Fourteen ML models, along with varied feature selection strategies, were used to establish triple-classification models using the radiomic features (radiomic models), while clinical-radiomic models were built after combining with the clinical variables. The diagnostic accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC) of radiologist assessment, the radiomic and clinical-radiomic models were evaluated on the testing cohort. RESULTS: The Support Vector Machine (SVM) clinical-radiomic model demonstrated the highest AUC of 0.841 (95% CI 0.820 to 0.861) on the cross-validation result and reached an AUC of 0.844 (95% CI 0.793 to 0.894) in the testing cohort. For the one-vs-rest question of LRT vs HRT + TC, the sensitivity, specificity, and accuracy reached 80.00%, 63.41%, and 71.05%, respectively. For HRT vs LRT + TC, they reached 60.53%, 78.95%, and 69.74%. For TC vs LRT + HRT they reached 33.33%, 98.63%, and 96.05%, respectively. Compared with the radiomic models, superior diagnostic efficacy was demonstrated for most clinical-radiomics models, and the AUC of the Bernoulli Naive Bayes model was significantly improved. Radiologist2's assessment achieved a higher AUC of 0.813 (95% CI: 0.756-0.8761) than other radiologists, which was slightly lower than the SVM clinical-radiomic model. Combined with other evaluation indicators, SVM, as the best ML model, demonstrated the potential of predicting the simplified risk categorization of TETs with superior predictive performance to that of radiologists' assessment. CONCLUSION: Most of the ML models are promising in predicting the simplified TETs risk categorization with superior efficacy to that of radiologists' assessment, especially the SVM models, demonstrated the integration of ML with NECT may be valuable in aiding the diagnosis and treatment planning.

Better efficacy in differentiating WHO grade II from III oligodendrogliomas with machine-learning than radiologist's reading from conventional T1 contrast-enhanced and fluid attenuated inversion recovery images.

BACKGROUND: The medical imaging to differentiate World Health Organization (WHO) grade II (ODG2) from III (ODG3) oligodendrogliomas still remains a challenge. We investigated whether combination of machine leaning with radiomics from conventional T1 contrast-enhanced (T1 CE) and fluid attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI) offered superior efficacy. METHODS: Thirty-six patients with histologically confirmed ODGs underwent T1 CE and 33 of them underwent FLAIR MR examination before any intervention from January 2015 to July 2017 were retrospectively recruited in the current study. The volume of interest (VOI) covering the whole tumor enhancement were manually drawn on the T1 CE and FLAIR slice by slice using ITK-SNAP and a total of 1072 features were extracted from the VOI using 3-D slicer software. Random forest (RF) algorithm was applied to differentiate ODG2 from ODG3 and the efficacy was tested with 5-fold cross validation. The diagnostic efficacy of radiomics-based machine learning and radiologist's assessment were also compared. RESULTS: Nineteen ODG2 and 17 ODG3 were included in this study and ODG3 tended to present with prominent necrosis and nodular/ring-like enhancement (P < 0.05). The AUC, ACC, sensitivity, and specificity of radiomics were 0.798, 0.735, 0.672, 0.789 for T1 CE, 0.774, 0.689, 0.700, 0.683 for FLAIR, as well as 0.861, 0.781, 0.778, 0.783 for the combination, respectively. The AUCs of radiologists 1, 2 and 3 were 0.700, 0.687, and 0.714, respectively. The efficacy of machine learning based on radiomics was superior to the radiologists' assessment. CONCLUSIONS: Machine-learning based on radiomics of T1 CE and FLAIR offered superior efficacy to that of radiologists in differentiating ODG2 from ODG3.

Incidence and radiological pattern of eosinophilic granuloma: a retrospective study in a Chinese tertiary hospital.

BACKGROUND: The incidence and radiological patterns of eosinophilic granuloma (EG) in China is not clear. We described the incidence, presentation, and imaging characteristics of Chinese EG patients in a tertiary hospital. METHODS: A retrospective chart review was performed from January 2004 to October 2017 at a single tertiary general hospital. Seventy-six patients were pathologically identified as EG. Besides, 60 patients with preoperative imaging diagnosis of "EG" were analyzed to reveal the radiological patterns and their diagnostic power. RESULTS: Fifty-three male and 23 female EG patients with a mean age of 18.1 ± 16.7 years (range 1-58 years) were retrospectively included. Significant differences were observed in gender (male to female = 2.3:1) and age (the highest incidence at the age of 0~5 years) for EG. EG predominantly involved the skeletal system: flat bones (31.43%) > irregular bones (24.76%) > long bones (22.86%) > other organs (20.95%). No obvious relationships between season, biochemical markers, and EG incidence were observed. The common presenting symptoms were pain followed with local mass, and most patients underwent surgical resection. Among 60 imagingly diagnosed "EG" patients from April 2009 to October 2017, only 22 were with histological confirmation. The correct diagnosis rates were 37.1% (13 out of 35), 16.7% (5 out of 30), and 22.2% (8 out of 36) for plain radiography, computed tomography (CT), and magnetic resonance imaging (MRI), respectively. CONCLUSIONS: Chinese EG has a varied presentation, age distribution, and gender difference. EG diagnosis is still based on biopsy or histopathology instead of imaging techniques.

Predicting pathological subtypes and stages of thymic epithelial tumors using DWI: value of combining ADC and texture parameters.

OBJECTIVES: To explore the value of combining apparent diffusion coefficients (ADC) and texture parameters from diffusion-weighted imaging (DWI) in predicting the pathological subtypes and stages of thymic epithelial tumors (TETs). METHODS: Fifty-seven patients with TETs confirmed by pathological analysis were retrospectively enrolled. ADC values and optimal texture feature parameters were compared for differences among low-risk thymoma (LRT), high-risk thymoma (HRT), and thymic carcinoma (TC) by one-way ANOVA, and between early and advanced stages of TETs were tested using the independent samples t test. Receiver operating characteristic (ROC) curve analysis was performed to determine the differentiating efficacy. RESULTS: The ADC values in LRT and HRT were significantly higher than the values in TC (p = 0.004 and 0.001, respectively), also in early stage, values were significantly higher than ones in advanced stage of TETs (p < 0.001). Among all texture parameters analyzed in order to differentiate LRT from HRT and TC, the V312 achieved higher diagnostic efficacy with an AUC of 0.875, and combination of ADC and V312 achieved the highest diagnostic efficacy with an AUC of 0.933, for differentiating the LRT from HRT and TC. Furthermore, combination of ADC and V1030 achieved a relatively high differentiating ability with an AUC of 0.772, for differentiating early from advanced stages of TETs. CONCLUSIONS: Combination of ADC and DWI texture parameters improved the differentiating ability of TET grades, which could potentially be useful in clinical practice regarding the TET evaluation before treatment. KEY POINTS: • DWI texture analysis is useful in differentiating TET subtypes and stages. • Combination of ADC and DWI texture parameters may improve the differentiating ability of TET grades. • DWI texture analysis could potentially be useful in clinical practice regarding the TET evaluation before treatment.

Incidence and clinical variable inter-relationships of thymic epithelial tumors in northwest China.

BACKGROUND: Thymic epithelial tumors (TETs) are the most common primary thymus tumors, but neither the possible ethnical/regional differences in the incidence of TETs nor the inter-relationships among the clinical variables has been revealed in northwest China. METHODS: A retrospective chart review was performed among pathologically confirmed TET patients from January 2004 to December 2015 in a tertiary general hospital of northwest China and the incidence, clinical features and the inter-relationships among clinical variables were analyzed. RESULTS: A total of 603 pathologically confirmed TETs patients (age range, 5-78 years; 308 males) were enrolled and the most common lesion location was anterior mediastinum (98.5%), among them, 192 (31.8%) had myasthenia gravis (MG). Twenty-six (5.7%), 112 (24.6%), 83 (18.2%), 137 (30.1%), 74 (16.3%), and 23 (5.1%) patients fell into the World Health Organization (WHO) type A, AB, B1, B2, B3 and thymic carcinoma (TC), respectively. The incidence of TETs was slightly higher in the female population and the age group of 40-60 years old. In addition, MG predominantly coexisted with WHO types A-B3 TETs and the TETs with MG were smaller than those without MG. The correct diagnosis rates were 42.3% (77 out of 182), 61.1% (127 out of 208), 89.3% (250 out of 280) and 75.0% (3 out of 4) for chest X-ray, non-contrast computed tomography (CT), contrast CT scan and magnetic resonance imaging (MRI), respectively. CONCLUSIONS: Distinct gender and age differences exist in the incidence of TETs and the A-B3 TETs are closely related with MG. Contrast CT scan plays more important role in diagnosing TETs.