Multiparametric Magnetic Resonance Imaging for Assessing Thymic Epithelial Tumors: Correlation With Pathological Subtypes and Clinical Stages.

BACKGROUND: World Health Organization classification and Masaoka-Koga stage are widely used for thymic epithelial tumors (TETs). Reduced field-of-view (rFOV) diffusion-weighed imaging (DWI) proved to improve the image quality. Dynamic contrast-enhanced (DCE) MRI was commonly used in evaluating tumors. PURPOSE: To investigate the value of multiparametric MRI in evaluating TETs. STUDY TYPE: Retrospective. SUBJECTS: Eighty-seven participants including 38 low risk (52.08 ± 14.19 years), 30 high risk (52.40 ± 11.35 years), and 19 thymic carcinoma patients (59.76 ± 10.78 years). FIELD STRENGTH/SEQUENCE: A 3 T, turbo spin echo imaging, echo planar imaging, volumetric interpolated breath-hold examination with radial acquisition trajectory. ASSESSMENT: DCE-MRI and apparent diffusion coefficient (ADC) variables were compared. Diagnostic performances of single significant factor and combined model were compared. STATISTICAL TESTS: Parameters were compared using one-way ANOVA or independent-samples t test. Logistic regression was employed to investigate the combined model. Receiver operating curves (ROC) and DeLong's test were used to compare the diagnostic efficiency. RESULTS: ADC, Ktrans , and kep values were significantly different among low-risk, high-risk and carcinoma group (ADC, 1.279 ± 0.345 × 10-3  mm2 /sec, 0.978 ± 0.260 × 10-3  mm2 /sec, 0.661 ± 0.134 × 10-3  mm2 /sec; Ktrans 0.167 ± 0.071 min-1 , 0.254 ± 0.136 min-1 , 0.393 ± 0.110 min-1 ; kep 0.345 ± 0.113 min-1 , 0.560 ± 0.269 min-1 , 0.872 ± 0.149 min-1 ). They were significantly different for early stage and advanced stage (ADC, 1.270 ± 0.356 × 10-3  mm2 /sec vs. 0.845 ± 0.251 × 10-3  mm2 /sec; Ktrans 0.179 ± 0.092 min-1 vs. 0.304 ± 0.142 min-1 ; kep 0.370 ± 0.181 min-1 vs. 0.674 ± 0.362 min-1 ). The combination of them had highest diagnostic efficiency for WHO classification (AUC, 0.925; sensitivity, 83.7%; specificity, 89.5%), clinical stage (AUC, 0.879; sensitivity, 80.9%; specificity, 82.5%). DATA CONCLUSION: Multiparametric MRI model may be useful for discriminating WHO classification and clinical stage of TETs. EVIDENCE LEVEL: 4 TECHNICAL EFFICIENCY: Stage 2.

Radiomic Analysis of Pulmonary Nodules for Distinguishing Malignancy From Benignancy: The Value of Using Iodine Maps From Dual-Energy Computed Tomography.

OBJECTIVE: The aim of the study is to investigate the diagnostic accuracy of radiomics on iodine maps from dual-energy computed tomography (DECT) in distinguishing lung cancer from benign pulmonary nodules. METHODS: This retrospective study was approved by the institutional review board, and written informed consent was waived. A total of 109 patients with 55 malignant nodules and 62 benign nodules underwent contrast-enhanced DECT. Eight iodine uptake parameters on iodine maps generated by DECT were calculated and established a predictive model. Eighty-seven radiomics features of entire tumor were extracted from iodine maps and established a radiomics model. The iodine uptake model and radiomics model were independently built based on the highly reproducible features using the least absolute shrinkage and selection operator method. The diagnostic accuracy of 2 models were assessed using receiver operating curve analysis. For external validation, 47 patients (25 benign and 22 malignant) from another hospital were assigned to testing data set. RESULTS: All iodine uptake features showed significant association with malignancy ( P < 0.01) and 2 selected features (mean value of virtual noncontrast images and mean value of vital part on contrast-enhanced image) constituted the iodine model. The radiomics model comprised 2 features (original shape sphericity and original glszm small area high gray level emphasis), which showed good discrimination both in the training cohort (area under the curve, 0.957) and validation cohort (area under the curve, 0.800). Radiomics model showed superior performance than iodine uptake model (accuracy, 89.7% vs 80.6%). CONCLUSIONS: Radiomics model extracted from iodine maps provided a robust diagnostic tool for discriminating pulmonary malignant nodules and had high potential in clinical application.

IgG4-related nephritis and interstitial pulmonary disease complicated by invasive pulmonary fungal infection: a case report.

BACKGROUND: IgG4-related kidney disease (IgG4-RKD) can affect multiple organs, which was first reported as a complication or extra-organ manifestation of autoimmune pancreatitis in 2004. It is characterized by abundant IgG4-positive plasma cells infiltration in tissues involved. CASE PRESENTATION: A 69-year-old man presented with cough and renal dysfunction with medical history of hypertension and diabetes. Pathological findings revealed interstitial nephritis and he was initially diagnosed with IgG4-RKD. Prednisone helped the patient to get a remission of cough and an obvious decrease of IgG4 level. However, he developed invasive pulmonary fungal infection while steroid theatment. Anti-fungal therapy was initiated after lung puncture (around cavitary lung lesion). Hemodialysis had been conducted because of renal failure and he got rid of it 2 months later. Methylprednisolone was decreased to 8 mg/day for maintenance therapy. Anti-fungal infection continued for 4 months after discharge home. On the 4th month of follow-up, Chest CT revealed no progression of lung lesions. CONCLUSIONS: The corticosteroids are the first-line therapy of IgG4-RD and a rapid response helps to confirm the diagnosis. This case should inspire clinicians to identify IgG4-related lung disease and secondary pulmonary infection, pay attention to the complications during immunosuppressive therapy for primary disease control.

CT-Based Radiomics Model for Predicting Brain Metastasis in Category T1 Lung Adenocarcinoma.

OBJECTIVE. The purpose of this study is to develop and evaluate an unenhanced CT-based radiomics model to predict brain metastasis (BM) in patients with category T1 lung adenocarcinoma. MATERIALS AND METHODS. A total of 89 eligible patients with category T1 lung adenocarcinoma were enrolled and classified as patients with BM (n = 35) or patients without BM (n = 54). A total of 1160 quantitative radiomic features were extracted from unenhanced CT images of each patient. Three prediction models (the clinical model, the radiomics model, and a hybrid [clinical plus radiomics] model) were established. The ROC AUC value and 10-fold cross-validation were used to evaluate the prediction performance of the models. RESULTS. In terms of predictive performance, the mean AUC value was 0.759 (95% CI, 0.643-0.867; sensitivity, 82.9%; specificity, 57.4%) for the clinical model, 0.847 (95% CI, 0.739-0.915; sensitivity, 80.0%; specificity, 81.5%) for the radiomics model, and 0.871 (95% CI, 0.767-0.933; sensitivity = 82.9%, specificity = 83.3%) for the hybrid model. The hybrid and radiomics models (p = 0.0072 and 0.0492, respectively) performed significantly better than the clinical model. No significant difference was found between the radiomics model and the hybrid model (p = 0.1022). CONCLUSION. A CT-based radiomics model presented good predictive performance and great potential for predicting BM in patients with category T1 lung adenocarcinoma. As a promising adjuvant tool, it can be helpful for guiding BM screening and thus benefiting personalized surveillance for patients with lung cancer.

Radiomics Approach to Prediction of Occult Mediastinal Lymph Node Metastasis of Lung Adenocarcinoma.

OBJECTIVE: The purpose of this study was to evaluate the prognostic impact of radiomic features from CT scans in predicting occult mediastinal lymph node (LN) metastasis of lung adenocarcinoma. MATERIALS AND METHODS: A total of 492 patients with lung adenocarcinoma who underwent preoperative unenhanced chest CT were enrolled in the study. A total of 300 radiomics features quantifying tumor intensity, texture, and wavelet were extracted from the segmented entire-tumor volume of interest of the primary tumor. A radiomics signature was generated by use of the relief-based feature method and the support vector machine classification method. A ROC regression curve was drawn for the predictive performance of radiomics features. Multivariate logistic regression models based on clinicopathologic and radiomics features were compared for discriminating mediastinal LN metastasis. RESULTS: Clinical variables (sex, tumor diameter, tumor location) and predominant subtype were risk factors for pathologic mediastinal LN metastasis. The accuracy of radiomics signature for predicting mediastinal LN metastasis was 91.1% in ROC analysis (AUC, 0.972; sensitivity, 94.8%; specificity, 92%). Radiomics signature (Akaike information criterion [AIC] value, 80.9%) showed model fit superior to that of the clinicohistopathologic model (AIC value, 61.1%) for predicting mediastinal LN metastasis. CONCLUSION: The radiomics signature of a primary tumor based on CT scans can be used for quantitative and noninvasive prediction of occult mediastinal LN metastasis of lung adenocarcinoma.

Lung adenocarcinoma: Assessment of epidermal growth factor receptor mutation status based on extended models of diffusion-weighted image.

PURPOSE: To evaluate the diagnostic performance of extended models of diffusion-weighted (DW) imaging to help differentiate the epidermal growth factor receptor (EGFR) mutation status in stage IIIA-IV lung adenocarcinoma. MATERIALS AND METHODS: This retrospective study had institutional research board approval and was HIPAA compliant. Preoperative extended DW imaging including intravoxel incoherent motion (IVIM) and diffusional kurtosis imaging (DKI) 3 Tesla MRI were retrospectively evaluated in 53 patients with pathologically confirmed non-early stage (IIIA-IV) lung adenocarcinoma. EGFR mutationsat exons 18-21 were determined by using polymerase chain reaction-based ARMS. Quantitative parameters (mean, kurtosis, skewness, 10th and 90th percentiles) of IVIM (true-diffusion coefficient D, pseudo-diffusion coefficient D*, and perfusion fraction f) and DKI (kurtosis value Kapp, kurtosis corrected diffusion coefficient Dapp) were calculated by outlining entire-volume histogram analysis. Receiver operating characteristic analysis was constructed to determine the diagnostic performance of each parameter. Multivariate logistic regression was used to differentiate the probability of EGFR mutation status. RESULTS: Twenty-four of 53 patients with lung adenocarcinoma were EGFR mutations, which occurred most often in acinar (10 of 13 [76.9%]) and papillary predominant tumors (9 of 13 [69.2%]). Patients with EGFR mutation showed significant higher 10th percentile of D, lower D* value in terms of kurtosis, and lower Kapp value in terms of mean, skewness, 10th and 90th percentiles (all P values < 0.05). The 90th Kapp showed significantly higher sensitivity (97%; P < 0.05) and Az (0.817; P < 0.05) value. Multivariate logistic regression showed 90th Kapp was a independent factor for determining EGFR mutation with odds ratio -1.657. CONCLUSION: Multiple IVIM and DKI parameters, especially the histogram 90th Kapp value, helped differentiate EGFR mutation status in stage IIIA-IV lung adenocarcinoma. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2017;46:281-289.

Comparison of a radiomic biomarker with volumetric analysis for decoding tumour phenotypes of lung adenocarcinoma with different disease-specific survival.

OBJECTIVES: To compare a multi-feature-based radiomic biomarker with volumetric analysis in discriminating lung adenocarcinomas with different disease-specific survival on computed tomography (CT) scans. METHODS: This retrospective study obtained institutional review board approval and was Health Insurance Portability and Accountability Act (HIPAA) compliant. Pathologically confirmed lung adenocarcinoma (n = 431) manifested as subsolid nodules on CT were identified. Volume and percentage solid volume were measured by using a computer-assisted segmentation method. Radiomic features quantifying intensity, texture and wavelet were extracted from the segmented volume of interest (VOI). Twenty best features were chosen by using the Relief method and subsequently fed to a support vector machine (SVM) for discriminating adenocarcinoma in situ (AIS)/minimally invasive adenocarcinoma (MIA) from invasive adenocarcinoma (IAC). Performance of the radiomic signatures was compared with volumetric analysis via receiver-operating curve (ROC) analysis and logistic regression analysis. RESULTS: The accuracy of proposed radiomic signatures for predicting AIS/MIA from IAC achieved 80.5% with ROC analysis (Az value, 0.829; sensitivity, 72.1%; specificity, 80.9%), which showed significantly higher accuracy than volumetric analysis (69.5%, P = 0.049). Regression analysis showed that radiomic signatures had superior prognostic performance to volumetric analysis, with AIC values of 81.2% versus 70.8%, respectively. CONCLUSIONS: The radiomic tumour-phenotypes biomarker exhibited better diagnostic accuracy than traditional volumetric analysis in discriminating lung adenocarcinoma with different disease-specific survival. KEY POINTS: • Radiomic biomarker on CT was designed to identify phenotypes of lung adenocarcinoma • Built up radiomic signature for lung adenocarcinoma manifested as subsolid nodules • Retrospective study showed radiomic signature had greater diagnostic accuracy than volumetric analysis • Radiomics help to evaluate intratumour heterogeneity within lung adenocarcinoma • Medical decision can be given with more confidence.

Volumetric analysis of intravoxel incoherent motion imaging for assessment of solitary pulmonary lesions.

Background Differentiating between malignant and benign solitary pulmonary lesions (SPLs) is challenging. Purpose To determine diagnostic performance of intravoxel incoherent motion-based diffusion-weighted imaging (DW-IVIM) in distinguishing malignant from benign SPLs, using histogram analysis derived whole-tumor and single-section region of interest (ROI). Material and Methods This retrospective study received institutional review board approval. A total of 129 patients with diagnosed SPLs underwent DW-IVIM and apparent diffusion coefficient (ADC). ADC, slow diffusion coefficient (D), fast diffusion coefficient (D*), and perfusion fraction (f) were calculated separately by outlining whole-tumor and single-section ROI. Inter-observer reliability was assessed by inter-class correlation coefficient (ICC). ADC and DW-IVIM parameters were analyzed using independent-sample T-test. Receiver operating characteristic (ROC) analysis was constructed to determine diagnostic performance. Multiple logistic regression was performed to identify independent factors associated with malignant SPLs. Results There were 48 benign SPLs found in 35 patients and 94 patients with lung cancer (LC). ICC for whole-tumor ROI (range, 0.89-0.95) was higher than that for single-section ROI (range, 0.61-0.71). Mean ADC and D were significantly lower in the malignant group. ADC and D 10th showed significantly higher AUC values than did mean ADC and D. D showed significantly higher diagnostic accuracy in mean, 10th, and 25th percentiles than ADC values (all Ps < 0.05). D 10th was found to be an independent factor in discriminating LCs with an odds ratio of -1.217. Conclusion Volumetric analysis had higher reproducibility and diagnostic accuracy than did single-section. Further, compared to ADC, D value differentiated benign SPLs from LCs more accurately.

Comparison of intravoxel incoherent motion diffusion-weighted MR imaging with dynamic contrast-enhanced MRI for differentiating lung cancer from benign solitary pulmonary lesions.

BACKGROUND: To compare intravoxel incoherent motion (IVIM) and pharmacokinetic analysis dynamic contrast-enhanced MR imaging (DCE-MRI) in distinguishing lung cancer (LC) from benign solitary pulmonary lesions (SPL). METHODS: This prospective study was approved by the institutional review board, and written informed consent was obtained. Eighty-one consecutive patients considered for SPL underwent DW-IVIM and DCE-3T MRI. ADC, D, D*, and f were calculated with mono- and bi-exponential models. K(trans) , kep , ve , and vp were calculated with the modified Tofts model. Receiver operating characteristic (ROC) analysis was constructed to determine the diagnostic performance of IVIM and DCE-MRI in discriminating LC from benignity. RESULTS: There were 29 patients with a total of 48 benign SPL and 52 LCs: 4 small cell carcinomas (SCLC), 19 squamous cell carcinomas (SCC), and 29 adenocarcinomas (Adeno-Ca). Both Adeno-Ca (ADC: 1.19 ± 0.23 × 10(-3) mm(2) /s; D:1.12 ± 0.35 × 10(-3) mm(2) /s; ve :0.27 ± 0.13; K(trans) :0.24 ± 0.09 min(-1) ; kep :0.90 ± 0.45 min(-1) ) and SCC (1.13± 0.28 × 10(-3) mm(2) /s; 1.02 ± 0.32 10(-3) mm(2) /s; 0.32 ± 0.14; 0.26 ± 0.08 min(-1) ; 0.90 ± 0.48 min(-1) ) had significantly lower ADC, D, ve and larger K(trans) , kep than benignity (1.37 ± 0.38 × 10(-3) mm(2) /s; 1.34 ± 0.45 × 10(-3) mm(2) /s; 0.42 ± 0.19; 0.19 ± 0.08 min(-1) ; 0.53 ± 0.26 min(-1) ). D (72.2%) had significantly higher accuracy (72.2%) and higher sensitivity (91.3%) than other imaging indices (accuracy: 55.5-68.0%; sensitivity: 41.3-78.3%; all P < 0.01) except for accuracy in kep (70.8%; P > 0.05) in discriminating LC from benignity. K(trans) exhibited significantly higher specificity (84.6%) than the other indices (38.5-73.1%; P < 0.01). These results can be improved by combined D and K(trans) , leading to a sensitivity, specificity and accuracy of 94.2%, 92%, and 93.5%, respectively. CONCLUSION: IVIM-derived D and DCE-derived K(trans) are two promising parameters for differentiating LC from benignity.

Individually adapted tube current selection and contrast medium injection protocol of coronary CT angiography based on test bolus parameters: a feasibility study.

BACKGROUND: Test bolus is mostly used to determine the starting point of a full cardiac scanning with respect to injection of a larger bolus of contrast material. So far there are limited data demonstrating the feasibility of using information obtained from a test bolus to adjust contrast delivery protocols and tube current individually during coronary computed tomography angiography (CCTA). PURPOSE: To evaluate the feasibility of individually adapted tube current selection and contrast injection protocols of CCTA based on test bolus parameters. MATERIAL AND METHODS: Test bolus followed by CCTA was performed in 93 patients at 100 kV and in 81 patients at 120 kV, respectively. Simulated attenuation of the descending aorta (SimDA) of CCTA was calculated at a fixed contrast injection rate of 4 mL/s. Univariate and multivariate comparisons were performed to identify associations of SimDA and image noise of CCTA (NoiseCCTA) with test bolus information and patient-related factors including body weight (BW), body mass index (BMI), and body surface area (BSA). RESULTS: Compared with BW, BMI, and BSA, SimDA was more closely related to the peak time of left ventricle and peak enhancement of right ventricle obtained from test bolus (r = 0.495 and r = 0.642 for 100 and 120 kV protocol, respectively). Similarly, NoiseTB was much more closely related to NoiseCCTA (r = 0.740 and r = 0.630 for 100 and 120 kV protocol, respectively) when compared with BW, BMI, and BSA. CONCLUSION: It is feasible to individually adapt tube current and contrast injection protocol of CCTA based on the information of test bolus.

Intratumoral regulatory T cells are associated with suppression of colorectal carcinoma metastasis after resection through overcoming IL-17 producing T cells.

With opposite immune activities, regulatory T cells (Tregs) and IL-17 producing T cells were accumulated in various malignant tumors and played critical roles in pathophysiologic course of these diseases. In this study, we investigated the mix-effect of the intratumoral Tregs and IL-17 producing T cells on metastasis of colorectal carcinoma (CRC) after resection. The frequency of intratumoral Tregs and IL-17A+ T cells, and the levels of FoxP3 and IL-17 mRNA were analyzed. The ratio of Tregs/IL-17A+T cells and the ratio of FoxP3 mRNA/IL-17 mRNA were calculated. The activities of matrix metalloproteases (MMPs) in tumor tissues were analyzed. Meanwhile, Tregs from patient's blood was co-cultured with human CRC cells in the presence of IL-17. MMPs protein and mRNA levels were determined after 48 or 24h incubation. We found that Tregs and IL-17A+T cells were accumulated in CRC. The ratio of Tregs/IL-17A+T cells was decreased in CRC tissues. More intratumoral Tregs and less IL-17A+T cells were associated with suppressed MMPs activities and decreased metastases score. In addition, vitro studies demonstrated that Tregs suppressed MMPs expression in the presence of IL-17. Our findings suggested the possibility that intratumoral Tregs protected against metastasis of CRC after resection through overcoming IL-17 producing T cells.

Sorafenib reduces hepatic infiltrated regulatory T cells in hepatocellular carcinoma patients by suppressing TGF-beta signal.

BACKGROUND AND OBJECTIVES: Sorafenib has been shown to improve survival rate of hepatocellular carcinoma (HCC) patients significantly. Decline of tumor infiltrated regulatory T cells (TITs) may account for the activity of sorafenib partially. In this study, the underlying mechanism of sorafenib reducing TITs was investigated. METHODS: Tumor infiltrated mononuclear cells (TIMs), which were isolated form 19 HCC patients with or without sorafenib therapy, were analyzed by flow cytometry. TGF-ß signal pathways were analyzed by immunoblotting. In vitro test, naïve T cells were induced to regulatory T cells (Tregs) with or without sorafenib. After 3 days of culture, percentage of Tregs from CD4+ cells and TGF-ß signal pathways were analyzed. Meanwhile, TIMs from HCC patients without sorafenib treatment were cultured in the presence of sorafenib, and then the percentage of Foxp3 expressing cells from TIMs was analyzed. RESULTS: TITs were increased in HCC patients compared with controls. However, after sorafenib therapy, TITs were decreased significantly and TGF-ß signal pathways were down-regulated. Additionally, in the presence of sorafenib, induction of Tregs was inhibited and TGF-ß signal pathways in resulting cells were down-regulated. However, sorafenib treatment did not affect the percentage of Foxp3 expressing cells from TIMs in vitro. CONCLUSIONS: Sorafenib reducing TITs in HCC patients are associated with down-regulation of TGF-ß signal. This finding may help us for better understanding the activity of sorafenib in HCC patients.

Comparison and fusion prediction model for lung adenocarcinoma with micropapillary and solid pattern using clinicoradiographic, radiomics and deep learning features.

To investigate whether the combination scheme of deep learning score (DL-score) and radiomics can improve preoperative diagnosis in the presence of micropapillary/solid (MPP/SOL) patterns in lung adenocarcinoma (ADC). A retrospective cohort of 514 confirmed pathologically lung ADC in 512 patients after surgery was enrolled. The clinicoradiographic model (model 1) and radiomics model (model 2) were developed with logistic regression. The deep learning model (model 3) was constructed based on the deep learning score (DL-score). The combine model (model 4) was based on DL-score and R-score and clinicoradiographic variables. The performance of these models was evaluated with area under the receiver operating characteristic curve (AUC) and compared using DeLong's test internally and externally. The prediction nomogram was plotted, and clinical utility depicted with decision curve. The performance of model 1, model 2, model 3 and model 4 was supported by AUCs of 0.848, 0.896, 0.906, 0.921 in the Internal validation set, that of 0.700, 0.801, 0.730, 0.827 in external validation set, respectively. These models existed statistical significance in internal validation (model 4 vs model 3, P = 0.016; model 4 vs model 1, P = 0.009, respectively) and external validation (model 4 vs model 2, P = 0.036; model 4 vs model 3, P = 0.047; model 4 vs model 1, P = 0.016, respectively). The decision curve analysis (DCA) demonstrated that model 4 predicting the lung ADC with MPP/SOL structure would be more beneficial than the model 1and model 3 but comparable with the model 2. The combined model can improve preoperative diagnosis in the presence of MPP/SOL pattern in lung ADC in clinical practice.

Quantitative texture analysis based on dynamic contrast enhanced MRI for differential diagnosis between primary thymic lymphoma from thymic carcinoma.

To evaluate the value of texture analysis based on dynamic contrast enhanced MRI (DCE-MRI) in the differential diagnosis of thymic carcinoma and thymic lymphoma. Sixty-nine patients with pathologically confirmed (thymic carcinoma, n = 32; thymic lymphoma, n = 37) were enrolled in this retrospective study. Ktrans, Kep and Ve maps were automatically generated, and texture features were extracted, including mean, median, 5th/95th percentile, skewness, kurtosis, diff-variance, diff-entropy, contrast and entropy. The differences in parameters between the two groups were compared and the diagnostic efficacy was calculated. The Ktrans-related significant features yielded an area under the curve (AUC) of 0.769 (sensitivity 90.6%, specificity 51.4%) for the differentiation between thymic carcinoma and thymic lymphoma. The Kep-related significant features yielded an AUC of 0.780 (sensitivity 87.5%, specificity 62.2%). The Ve-related significant features yielded an AUC of 0.807 (sensitivity 75.0%, specificity 78.4%). The combination of DCE-MRI textural features yielded an AUC of 0.962 (sensitivity 93.8%, specificity 89.2%). Five parameters were screened out, including age, Ktrans-entropy, Kep-entropy, Ve-entropy, and Ve-P95. The combination of these five parameters yielded the best discrimination efficiency (AUC of 0.943, 93.7% sensitivity, 81.1% specificity). Texture analysis of DCE-MRI may be helpful to distinguish thymic carcinoma from thymic lymphoma.

Dual-energy computed tomography iodine uptake in differential diagnosis of inflammatory and malignant pulmonary nodules.

PURPOSE The aim of this study was to evaluate the diagnostic performance of iodine uptake parameters using dual-energy computed tomography (DECT) in discriminating inflammatory nodules from malignant tumors. METHODS This retrospective study included 116 solid pulmonary nodules from 112 patients who were admitted to our hospital between January and September 2018. All nodules were confirmed by surgery or puncture. The degree of enhancement of a single-section region of interest was evalu ated. After total tumor volume-of-interest segmentation, the mean iodine density of the whole tumor was measured. Meanwhile, iodine uptake parameters, including total iodine uptake vol ume, total iodine concentration, vital iodine uptake volume, and vital iodine concentration, were calculated, and a predictive model was established. The overall ability to discriminate between inflammatory and malignant nodules was analyzed using an independent samples t-test for normally distributed variables. The diagnostic accuracy and prognostic performance of DECT parameters were evaluated and compared using receiver operating characteristic curve analysis and logistic regression analysis. A multivariate logistic regression analysis was used to determine the prognostic factors and goodness-of-fit of the whole tumor mean iodine and iodine uptake parameters for discriminating malignant nodules. RESULTS There were 116 non-calcified nodules, including 64 inflammatory nodules and 52 malignant nodules. The degree of enhancement in malignant nodules was significantly lower than that in inflammatory nodules (P=.043). All iodine uptake parameters in malignant nodules were signifi cantly higher than those in inflammatory nodules (P < .001). The area under the receiver operat ing curve value, accuracy, sensitivity, and specificity of the established model based on iodine uptake parameters were 0.803, 76.72%, 82.69%, and 84.37%, respectively, which exhibited bet ter diagnostic performance than the degree of enhancement on weighted average images with respective values of 0.609, 59.48%, 61.54%, and 59.38%. CONCLUSION The iodine uptake parameters of DECT exhibited better diagnostic accuracy in discriminating inflammatory nodules from malignant nodules than the degree of enhancement on weighted average images.

Factors influencing delay time and coronary arterial density during coronary angiography with DSCT.

BACKGROUND: CT angiography (CTA) plays an important role in diagnosing coronary arterial disease. Delay time and density of the coronary arteries related with patient-specific factors are essential for getting an optimal CTA image. PURPOSE: To investigate various factors influencing delay time and coronary arterial density during coronary CTA with dual source CT. MATERIAL AND METHODS: One hundred and sixteen consecutive subjects who underwent cardiac DSCT with retrospective ECG-gating were included. Factors including gender, age, height, weight, transversal cardiac diameter (TCD), transversal thoracic diameter (TTD), heart rate (HR), body surface area (BSA = [weight × height/3600](1/2)) and cardiothoracic ratio (CTR = TCD/TTD) were recorded, measured and calculated before administration of contrast media during coronary CT angiography. Delay time was determined as duration from the beginning of the injection to the density in the descending aorta at the level of right main pulmonary artery reaching a threshold of 100 HU. Coronary arterial density was measured at the mid portion of the right coronary artery. Regression analysis and stepwise regression analysis were used to investigate the influence of these factors on delay time and coronary arterial density. RESULTS: Delay time decreased with an increasing HR and it was shorter in women than men. Delay time increased with an increasing TCD. Delay time could be predicted by the formula: DT = 16.651-0.110 × HR + 1.902 × gender + 0.394 × TCD (where DT is abbreviation for delay time, gender is 0 for women and 1 for men). Coronary arterial density decreased with an increasing HR and weight. Coronary arterial density could be predicted by the formula: CAD = 923.42-4.099 × HR-3.293 × weight (CAD = coronary arterial density). There was no relationship between the other factors mentioned above and delay time or coronary arterial density. CONCLUSION: Delay time is influenced by HR, gender and TCD. Coronary arterial density also changes with HR and weight. So HR, gender, TCD and weight can be used to adjust flow rate and dosage of contrast media before administration of contrast media during coronary CT angiography.

High-resolution CT image analysis based on 3D convolutional neural network can enhance the classification performance of radiologists in classifying pulmonary non-solid nodules.

OBJECTIVE: To investigate whether 3D convolutional neural network (CNN) is able to enhance the classification performance of radiologists in classifying pulmonary non-solid nodules (NSNs). MATERIALS AND METHODS: Data of patients with solitary NSNs and diagnosed as adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), or invasive adenocarcinoma (IAC) in pathological after surgical resection were analyzed retrospectively. Ultimately, 532 patients in our institution were included in the study: 427 cases (144 AIS, 167 MIA, 116 IAC) were assigned to training dataset and 105 cases (36 AIS, 41 MIA and 28 IAC) were assigned to validation dataset. For external validation, 177 patients (60 AIS, 69 MIA and 48 IAC) from another hospital were assigned to testing dataset. The clinical and morphological characteristics of NSNs were established as radiologists' model. The trained classification model based on 3D CNN was used to identify NSNs types automatically. The evaluation and comparison on classification performance of the two models and CNN + radiologists' model were performed via receiver operating curve (ROC) analysis and integrated discrimination improvement (IDI) index. The Akaike information criterion (AIC) was calculated to find the best-fit model. RESULTS: In external testing dataset, radiologists' model showed inferior classification performance than CNN model both in discriminating AIS from MIA-IAC and AIS-MIA from IAC (the area under the ROC curve (Az value), 0.693 vs 0.820, P = 0.011; 0.746 vs 0.833, P = 0.026, respectively). However, combining CNN significantly enhanced the classification performance of radiologists and exhibited higher Az values than CNN model alone (Az values, 0.893 vs 0.820, P < 0.001; 0.906 vs 0.833, P < 0.001, respectively). The IDI index further confirmed CNN's contribution to radiologists in classifying NSNs (IDI = 25.8 % (18.3-46.1 %), P < 0.001; IDI = 30.1 % (26.1-45.2 %), P < 0.001, respectively). The CNN + radiologists' model also provided the best fit over radiologists' model and CNN model alone (AIC value 63.3 % vs. 29.5 %, 49.5 %, P < 0.001; 69.2 % vs. 34.9 %, 53.6 %, P < 0.001, respectively). CONCLUSION: CNN successfully classified NSNs based on CT images and its classification performance were superior to radiologists' model. But the classification performance of radiologists can be significantly enhanced when combined with CNN in classifying NSNs.

Nomograms to predict the presence and extent of inguinal lymph node metastasis in penile cancer patients with clinically positive lymph nodes.

BACKGROUND: We developed two nomograms to predict the presence of inguinal lymph node metastasis (ILNM) and two or more histologically positive inguinal lymph nodes (ILNs) or one ILN with extranodal extension (ENE) in penile cancer patients with clinically positive inguinal lymph nodes (ILNs). METHODS: This study enrolled 75 penile squamous cell carcinoma (PSCC) patients with clinically positive ILNs. A binary logistic regression model was taken to screen out the risk factors for the presence of ILNM and two or more positive ILNs or one node with ENE in one side. Nomograms were plotted based on the results of multivariate logistic regression analysis. RESULTS: Our multivariate analysis demonstrated that the pathological grade, lymphovascular invasion (LVI), and short diameter (SD) of the largest ILN were independent risks. The bootstrap-corrected concordance index of the nomogram for the presence of ILNM was 0.948, and 0.878 for two or more histologically positive ILNs or one ILN with ENE on one side. CONCLUSIONS: By incorporating tumor pathological grade, LVI and SD of the largest ILN, our nomograms can efficiently predict the ILNM in penile cancer patients with clinically positive nodes.

Deep Learning in CT Images: Automated Pulmonary Nodule Detection for Subsequent Management Using Convolutional Neural Network.

PURPOSE: The purpose of this study is ï»¿to compare the detection performance of the 3-dimensional convolutional neural network (3D CNN)-based computer-aided detection (CAD) models with radiologists of different levels of experience in detecting pulmonary nodules on thin-section computed tomography (CT). PATIENTS AND METHODS: We retrospectively reviewed 1109 consecutive patients who underwent follow-up thin-section CT at our institution. The 3D CNN model for nodule detection was re-trained and complemented by expert augmentation. The annotations of a consensus panel consisting of two expert radiologists determined the ground truth. The detection performance of the re-trained CAD model and three other radiologists at different levels of experience were tested using a free-response receiver operating characteristic (FROC) analysis in the test group. RESULTS: The detection performance of the re-trained CAD model was significantly better than that of the pre-trained network (sensitivity: 93.09% vs 38.44%). The re-trained CAD model had a significantly better detection performance than radiologists (average sensitivity: 93.09% vs 50.22%), without significantly increasing the number of false positives per scan (1.64 vs 0.68). In the training set, 922 nodules less than 3 mm in size in 211 patients at high risk were recommended for follow-up CT according to the Fleischner Society Guidelines. Fifteen of 101 solid nodules were confirmed to be lung cancer. CONCLUSION: The re-trained 3D CNN-based CAD model, complemented by expert augmentation, was an accurate and efficient tool in identifying incidental pulmonary nodules for subsequent management.