To develop and validate predictive models using clinical parameters, radiomic features and a combination of both for lymph node metastasis (LNM) in pre-surgical CT-based stage IA non-small cell lung cancer (NSCLC) patients. Methods: This retrospective study included 649 pre-surgical CT-based stage IA NSCLC patients from our hospital. One hundred and thirty-eight (21 %) of the 649 patients had LNM after surgery. A total of 396 radiomic features were extracted from the venous phase contrast enhanced computed tomography (CECT). The training group included 455 patients (97 with and 358 without LNM) and the testing group included 194 patients (41 with and 153 without LNM). The least absolute shrinkage and selection operator (LASSO) algorithm was used for radiomic feature selection. The random forest (RF) was used for model development. Three models (a clinical model, a radiomics model, and a combined model) were developed to predict LNM in early stage NSCLC patients. The area under the receiver operating characteristic (ROC) curve (AUC) value and decision curve analysis were used to evaluate the performance in LNM status (with or without LNM) using the three models. Results: The ROC analysis (also decision curve analysis) showed predictive performance for LNM of the radiomics model (AUC values for training and testing, respectively 0.898 and 0.851) and of the combined model (0.911 and 0.860, respectively). Both performed better than the clinical model (0.739 and 0.614, respectively; delong test p-values both < 0.001). Conclusion: A radiomics model using the venous phase of CE-CT has potential for predicting LNM in pre-surgical CT-based stage IA NSCLC patients.

The objective of this study was to develop a venous computed tomography (CT)-based radiomics model to predict the lymph node metastasis (LNM) in patients with non-small cell lung cancer (NSCLC). A total of 411 consecutive patients with NSCLC underwent tumor resection and lymph node (LN) dissection from January 2018 to September 2018 in our hospital. A radiologist with 20 years of diagnostic experience retrospectively reviewed all CT scans and classified all visible LNs into LNM and non-LNM groups without the knowledge of pathological diagnosis. A logistic regression model (radiomics model) in classification of pathology-confirmed NSCLC patients with and without LNM was developed on radiomics features for NSCLC patients. A morphology model was also developed on qualitative morphology features in venous CT scans. A training group included 288 patients (99 with and 189 without LNM) and a validation group included 123 patients (42 and 81, respectively). The receiver operating characteristic curve was performed to discriminate LNM (+) from LNM (−) for CT-reported status, the morphology model and the radiomics model. The area under the curve value in LNM classification on the training group was significantly greater at 0.79 (95% confidence interval [CI]: 0.77–0.81) by use of the radiomics model (build by best 10 features in predicting LNM) compared with 0.51 by CT-reported LN status ( P < .001) or 0.66 (95% CI: 0.64–0.68) by morphology model (build by tumor size and spiculation) ( P < .001). Similarly, the area under the curve value on the validation group was 0.73 (95% CI: 0.70–0.76) by the radiomics model, compared with 0.52 or 0.63 (95% CI: 0.60–0.66) by the other 2 (both P < .001). A radiomics model shows excellent performance for predicting LNM in NSCLC patients. This predictive radiomics model may benefit patients to get better treatments such as an appropriate surgery.

Solitary pulmonary nodules are the main manifestation of pulmonary lesions. Doctors often make diagnosis by observing the lung CT images. In order to further study the brain response structure and construct a brain-computer interface, we propose an isolated pulmonary nodule detection model based on a brain-computer interface. First, a single channel time-frequency feature extraction model is constructed based on the analysis of EEG data. Second, a multilayer fusion model is proposed to establish the brain-computer interface by connecting the brain electrical signal with a computer. Finally, according to image presentation, a three-frame image presentation method with different window widths and window positions is proposed to effectively detect the solitary pulmonary nodules.

ObjectivesTo develop a radiomics model based on contrast-enhanced CT (CECT) to predict the lymphovascular invasion (LVI) in esophageal squamous cell carcinoma (ESCC) and provide decision-making support for clinicians.Patients and MethodsThis retrospective study enrolled 334 patients with surgically resected and pathologically confirmed ESCC, including 96 patients with LVI and 238 patients without LVI. All enrolled patients were randomly divided into a training cohort and a testing cohort at a ratio of 7:3, with the training cohort containing 234 patients (68 patients with LVI and 166 without LVI) and the testing cohort containing 100 patients (28 patients with LVI and 72 without LVI). All patients underwent preoperative CECT scans within 2 weeks before operation. Quantitative radiomics features were extracted from CECT images, and the least absolute shrinkage and selection operator (LASSO) method was applied to select radiomics features. Logistic regression (Logistic), support vector machine (SVM), and decision tree (Tree) methods were separately used to establish radiomics models to predict the LVI status in ESCC, and the best model was selected to calculate Radscore, which combined with two clinical CT predictors to build a combined model. The clinical model was also developed by using logistic regression. The receiver characteristic curve (ROC) and decision curve (DCA) analysis were used to evaluate the model performance in predicting the LVI status in ESCC.ResultsIn the radiomics model, Sphericity and gray-level non-uniformity (GLNU) were the most significant radiomics features for predicting LVI. In the clinical model, the maximum tumor thickness based on CECT (cThick) in patients with LVI was significantly greater than that in patients without LVI (P&lt;0.001). Patients with LVI had higher clinical N stage based on CECT (cN stage) than patients without LVI (P&lt;0.001). The ROC analysis showed that both the radiomics model (AUC values were 0.847 and 0.826 in the training and testing cohort, respectively) and the combined model (0.876 and 0.867, respectively) performed better than the clinical model (0.775 and 0.798, respectively), with the combined model exhibiting the best performance.ConclusionsThe combined model incorporating radiomics features and clinical CT predictors may potentially predict the LVI status in ESCC and provide support for clinical treatment decisions.

The aim of the present study was to develop predictive models using clinical features and MRI texture features for distinguishing between growth hormone deficiency (GHD) and idiopathic short stature (ISS) in children with short stature. This retrospective study included 362 children with short stature from Children's Hospital of Hebei Province. GHD and ISS were identified via the GH stimulation test using arginine. Overall, there were 190 children with GHD and 172 with ISS. A total of 57 MRI texture features were extracted from the pituitary gland region of interest using C++ language and Matlab software. In addition, the laboratory examination data were collected. Receiver operating characteristic (ROC) regression curves were generated for the predictive performance of clinical features and MRI texture features. Logistic regression models based on clinical and texture features were established for discriminating children with GHD and ISS. Two clinical features [IGF-1 (insulin growth factor-1) and IGFBP-3 (IGF binding protein-3) levels] were used to build the clinical predictive model, whereas the three best MRI textures were used to establish the MRI texture predictive model. The ROC analysis of the two models revealed predictive performance for distinguishing GHD from ISS. The accuracy of predicting ISS from GHD was 64.5% in ROC analysis [area under the curve (AUC), 0.607; sensitivity, 57.6%; specificity, 72.1%] of the clinical model. The accuracy of predicting ISS from GHD was 80.4% in ROC analysis (AUC, 0.852; sensitivity, 93.6%; specificity, 65.8%) of the MRI texture predictive model. In conclusion, these findings indicated that a texture predictive model using MRI texture features was superior for distinguishing children with GHD from those with ISS compared with the model developed using clinical features.

Rationale: Pulmonary benign metastasizing leiomyoma (PBML) is rare, usually occurs in women who underwent hysterectomy during the reproductive years, and has no obvious clinical symptoms. A full understanding of the characteristics of PBML is important for its sequential treatment and prognosis. Patient concerns: In this report, a 36-year-old female patient with previous uterine leiomyoma who underwent 3 surgical resections of the uterus, bilateral fallopian tubes, and partial omentum was investigated. The physical examination revealed a tumor in the right lower lobe and mediastinum and a solid nodule in the right middle lobe. Diagnoses: Chest computed tomography (CT) confirmed a tumor in the right lower lobe and mediastinum and a solid nodule in the right middle lobe. Further positron-emission tomography computed tomography (PET-CT) with 18F-fluorodeoxyglucose (FDG) of the whole body showed mildly intense accumulation of 18F-FDG in the tumor (maximum standardized uptake value [SUV max], 2.6). A pathological examination then confirmed the presence of fibrous and vascular tissue after CT-guided percutaneous biopsy of the tumor in the right lower lobe. Additionally, surgical resection of the tumor and nodule was performed for histological analysis and immunohistochemical assays for estrogen receptor (ER) and progesterone receptor (PR). Interventions: The patient underwent complete tumor surgical resection and nodule wedge resection. Outcomes: No postoperative complications occurred. No recurrence or other signs of metastasis were found during an 18-month follow-up observation period. Conclusion: In this case, lung and mediastinal metastasis of uterine fibroids was observed. However, depending on only a postoperative histological analysis is insufficient for the diagnosis of PBML. Histological analysis combined with an evaluation of the expression levels of ER and PR is crucial for the diagnosis and treatment of PBML.

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Purpose To explore the feasibility of high-resolution MRI 3-dimensional (3D) CUBE T1-weighted magnetic resonance imaging (MRI) in combination with non-contrast-enhanced (NCE) magnetic resonance venography (MRV) for the assessment of lumen stenosis in May–Thurner syndrome. Methods Twenty-nine patients underwent computed tomography venography (CTV) and high-resolution MRI-CUBE T1, and NCE MRV acquisitions. ANOVA and LSD tests were used to compare the stenosis rate and narrowest and distal diameters of the vessel lumen. Results There were no significant differences in the estimated stenosis rate between CTV, CUBE T1, and NCE MRV (p = 0.768). However, there were significant differences in the measured stenosis diameters of the left common iliac vein (LCIV), with CTV giving the largest mean diameter and CUBE had the smallest mean diameter (p < 0.05). The measured normal LCIV diameters did not significantly differ between MRV and CUBE (p = 0.075) but were significantly larger on CTV than on MRV and CUBE (p < 0.05). Conclusions Compared with CTV, a combination of CUBE and MRV could provide an improved assessment of the degree of lumen stenosis in May–Thurner syndrome and demonstrate acute thrombosis. MRI underestimates the diameter of the vessel in comparison with CTV. MRI can be a substitute tool for Duplex ultrasound and CTV.