Res-TransNet: A Hybrid deep Learning Network for Predicting Pathological Subtypes of lung Adenocarcinoma in CT Images.

This study aims to develop a CT-based hybrid deep learning network to predict pathological subtypes of early-stage lung adenocarcinoma by integrating residual network (ResNet) with Vision Transformer (ViT). A total of 1411 pathologically confirmed ground-glass nodules (GGNs) retrospectively collected from two centers were used as internal and external validation sets for model development. 3D ResNet and ViT were applied to investigate two deep learning frameworks to classify three subtypes of lung adenocarcinoma namely invasive adenocarcinoma (IAC), minimally invasive adenocarcinoma and adenocarcinoma in situ, respectively.

An attention base U-net for parotid tumor autosegmentation.

A parotid neoplasm is an uncommon condition that only accounts for less than 3% of all head and neck cancers, and they make up less than 0.3% of all new cancers diagnosed annually. Due to their nonspecific imaging features and heterogeneous nature, accurate preoperative diagnosis remains a challenge.

Visible-light-promoted trifluoromethylselenolation of -hydroxyarylenaminones.

Development of an efficient process that employs easy to handle and shelf-stable reagents for the synthesis of trifluoromethylselenylated heterocyclics remains a daunting challenge in organic synthesis. Herein, we report a green and practical protocol using trifluoromethyl tolueneselenosulfonate and -hydroxyarylenaminones to access a wide range of chromone derivatives under photocatalyst and oxidant free conditions. This reaction proceeded smoothly under photoirradiation conditions and various functional groups were tolerant of the reaction conditions.

Deep Learning for Differentiating Benign From Malignant Parotid Lesions on MR Images.

Purpose/objectivess: Salivary gland tumors are a rare, histologically heterogeneous group of tumors. The distinction between malignant and benign tumors of the parotid gland is clinically important. This study aims to develop and evaluate a deep-learning network for diagnosing parotid gland tumors the deep learning of MR images.

Deep Learning-Based Stage-Wise Risk Stratification for Early Lung Adenocarcinoma in CT Images: A Multi-Center Study.

This study aims to develop a deep neural network (DNN)-based two-stage risk stratification model for early lung adenocarcinomas in CT images, and investigate the performance compared with practicing radiologists. A total of 2393 GGNs were retrospectively collected from 2105 patients in four centers. All the pathologic results of GGNs were obtained from surgically resected specimens.

Baseline SUVmax of 18F-FDG PET-CT indicates prognosis of extranodal natural killer/T-cell lymphoma.

To evaluate the prognostic value of the baseline SUVmax of F-FDG PET-CT in extranodal natural killer/T-cell lymphoma (NKTCL) patients.From January 2010 to December 2015, 141 extranodal NKTCL patients with staging F-FDG PET-CT scan were divided into two group based on SUVmax cutoff value obtained from operating characteristic (ROC) curves. All the patients received radiotherapy, chemotherapy or chemoradiation.

Characteristics of diffusion tensor imaging of central nervous system in children with tourette's disease.

To investigate the characteristics of diffusion tensor imaging (DTI) of the central nervous system in children with Tourette syndrome (TS).Fifteen children with TS (TS group) and 15 normal children (control group) were studied, and all of them underwent DTI. The apparent diffusion coefficient (ADC) and fractional anisotropy (FA) parameters were calculated using the DTIStudio software.

Comparison and Fusion of Deep Learning and Radiomics Features of Ground-Glass Nodules to Predict the Invasiveness Risk of Stage-I Lung Adenocarcinomas in CT Scan.

For stage-I lung adenocarcinoma, the 5-years disease-free survival (DFS) rates of non-invasive adenocarcinoma (non-IA) is different with invasive adenocarcinoma (IA). This study aims to develop CT image based artificial intelligence (AI) schemes to classify between non-IA and IA nodules, and incorporate deep learning (DL) and radiomics features to improve the classification performance. We collect 373 surgical pathological confirmed ground-glass nodules (GGNs) from 323 patients in two centers.

Preoperative evaluation of neurovascular relationship in trigeminal neuralgia by three-dimensional fast low angle shot (3D-FLASH) and three-dimensional constructive interference in steady-state (3D-CISS) MRI sequence.

Objective: The purpose of the study was to evaluate the value of high-resolution three-dimensional fast low angle shot (3D-FLASH) and three-dimensional constructive interference in steady-state (3D-CISS) MRI sequence solely or the combination of both in the visualization of neurovascular relationship in patients with trigeminal neuralgia (TN).

Methods: 65 patients with unilateral TN underwent 3D-FLASH and 3D-CISS imaging were retrospectively studied. Neurovascular relationship at the intracisternal segment of trigeminal nerve was reviewed by two experienced neuroradiologist, who was blinded to the clinical details.

The use of MRI apparent diffusion coefficient (ADC) in monitoring the development of brain infarction.

Background: To study the rules that apparent diffusion coefficient (ADC) changes with time and space in cerebral infarction, and to provide the evidence in defining the infarction stages.

Methods: 117 work-ups in 98 patients with cerebral infarction (12 hyperacute, 43 acute, 29 subacute, 10 steady, and 23 chronic infarctions) were imaged with both conventional MRI and diffusion weighted imaging. The average ADC values, the relative ADC (rADC) values, and the ADC values or rADC values from the center to the periphery of the lesion were calculated.

Diagnosis of Struma ovarii with medical imaging.

Objective: To analyze the imaging features of Struma ovarii (SO), and to correlate the imaging results with the pathological findings so as to enhance the knowledge of the imaging diagnostics of this disease.

Methods: The clinical records, CT and MRI features of twelve patients with pathologically proved SO were retrospectively analyzed. Imaging features were compared with pathological results.

[Imaging manifestation and clinical value in herniation pit of femoral neck].

Objective: To discuss the imaging manifestation and clinical value in herniation pit of femoral neck.

Methods: One case proved by operation and pathology and twenty cases with typical imaging manifestation described by Pitt were reviewed retrospectively. There were 17 males and 4 females with an average age of 53 years old(ranging from 30 to 85 years).