Comparison of single shot and multishot diffusion-weighted imaging in 5-T magnetic resonance imaging for brain disease diagnosis.

Background: Diffusion-weighted imaging (DWI) with single-shot echo-planar imaging (ssEPI) is a valuable tool for detecting acute brain lesions but does suffer from image distortions. Multishot echo-planar imaging (msEPI) is a technique for reducing such distortions. This study aimed to compare the image quality and diagnostic efficacy of ssEPI- and msEPI-DWI at 5.

A vision transformer-based deep transfer learning nomogram for predicting lymph node metastasis in lung adenocarcinoma.

Background: Lymph node metastasis (LNM) plays a crucial role in the management of lung cancer; however, the ability of chest computed tomography (CT) imaging to detect LNM status is limited.

Purpose: This study aimed to develop and validate a vision transformer-based deep transfer learning nomogram for predicting LNM in lung adenocarcinoma patients using preoperative unenhanced chest CT imaging.

Methods: This study included 528 patients with lung adenocarcinoma who were randomly divided into training and validation cohorts at a 7:3 ratio.

Based on Gadolinium Ethoxybenzyl DTPA-Enhanced MRI: Diagnostic Performance of the Category-Modified LR-5 Criteria in Patients At Risk for Hepatocellular Carcinoma.

We aimed to modify the LR-5 strategy to improve the diagnostic sensitivity for hepatocellular carcinoma (HCC) in high-risk patients while maintaining specificity. This study retrospectively analyzed 412 patients with 445 liver observations who underwent preoperative gadolinium ethoxybenzyl DTPA (GD-EOB-DTPA)-enhanced MRI followed by surgical procedures or biopsies. All observations were classified according to LI-RADS v2018, and the classifications were adjusted by modifying major features (MF)(substituting threshold growth with a more HCC-specific ancillary features (AF): presence of blood products within the mass, arterial phase hyperenhancement (APHE) was interpreted with hypointensity on precontrast imaging- isointensity in arterial phase (AP) and extending washout to transitional phase (TP)(2 min)).

EfficientNet-Based System for Detecting EGFR-Mutant Status and Predicting Prognosis of Tyrosine Kinase Inhibitors in Patients with NSCLC.

We aimed to develop and validate a deep learning-based system using pre-therapy computed tomography (CT) images to detect epidermal growth factor receptor (EGFR)-mutant status in patients with non-small cell lung cancer (NSCLC) and predict the prognosis of advanced-stage patients with EGFR mutations treated with EGFR tyrosine kinase inhibitors (TKI). This retrospective, multicenter study included 485 patients with NSCLC from four hospitals. Of them, 339 patients from three centers were included in the training dataset to develop an EfficientNetV2-L-based model (EME) for predicting EGFR-mutant status, and the remaining patients were assigned to an independent test dataset.

Ct-based subregional radiomics using hand-crafted and deep learning features for prediction of therapeutic response to anti-PD1 therapy in NSCLC.

Purpose: To develop and externally validate subregional radiomics for predicting therapeutic response to anti-PD1 therapy in non-small-cell lung cancer (NSCLC).

Methods: Sixty-six patients from center 1 served as training and internal validation cohorts. Thirty patients from center 2 and thirty patients from center 3 served as external validation 1 and external validation 2 cohorts, respectively.

A general approach for automatic segmentation of pneumonia, pulmonary nodule, and tuberculosis in CT images.

Proposing a general segmentation approach for lung lesions, including pulmonary nodules, pneumonia, and tuberculosis, in CT images will improve efficiency in radiology. However, the performance of generative adversarial networks is hampered by the limited availability of annotated samples and the catastrophic forgetting of the discriminator, whereas the universality of traditional morphology-based methods is insufficient for segmenting diverse lung lesions. A cascaded dual-attention network with a context-aware pyramid feature extraction module was designed to address these challenges.

A Case of Giant Left Circumflex Coronary Fistula Aneurysm with Unidentified Fistula.

A coronary aneurysm is a rare type of cardiovascular disease. We report a case of a 53-year-old male patient who presented to our hospital with a giant left circumflex coronary fistula aneurysm (LCCA) (75 mm × 70 mm). Since coronary angiography and coronary computed tomography angiography failed to detect the fistula of the coronary aneurysm, interventional occlusion surgery could not be performed.

Blood-based biomarkers for Alzheimer's disease: a multicenter-based cross-sectional and longitudinal study in China.

Discrepancies in diagnostic biomarkers for Alzheimer's Disease (AD) may arise from racial disparities, risk factors, or lifestyle differences. Moreover, there has been a lack of systematic and multicenter studies to evaluate baselines of the AD biomarkers in Chinese populations. Thus, there is an urgent need for research to investigate the effectiveness of blood biomarkers for AD, specifically in the Chinese Han population, using a multicenter approach.

Investigation of brain iron levels in Chinese patients with Alzheimer's disease.

Introduction: We aimed (i) to explore the diagnostic value of deep gray matter magnetic susceptibility in Alzheimer's disease (AD) in China and (ii) to analyze its correlation with neuropsychiatric scales. Moreover, we conducted subgroup analysis based on the presence of the ε gene to improve the diagnosis of AD.

Methods: From the prospective studies of the China Aging and Neurodegenerative Initiative (CANDI), a total of 93 subjects who could undergo complete quantitative magnetic susceptibility imaging and ε gene detection were selected.

Higher betweenness and degree centrality in the frontal and cerebellum cortex of Meige's syndrome patients than hemifacial spasm patients.

Meige's syndrome and hemifacial spasm (HFS) are two different forms of dystonic movement disorder, but their difference in terms of resting state functional connectivity (rsFC) remains unclear. The present study applied resting state fMRI on the patients and quantified their functional connectivity with graph theoretical measures, including the degree centrality and the betweenness centrality. Fifteen Meige's syndrome patients and 19 HFS patients matched in age and gender were recruited and their MRI data were collected.

Development and validation of CT-based radiomics nomogram for the classification of benign parotid gland tumors.

Purpose: Accurate preoperative diagnosis of parotid tumor is essential for the formulation of optimal individualized surgical plans. The study aims to investigate the diagnostic performance of radiomics nomogram based on contrast-enhanced computed tomography (CT) images in the differentiation of the two most common benign parotid gland tumors.

Methods: One hundred and ten patients with parotid gland tumors including 76 with pleomorphic adenoma (PA) and 34 with adenolymphoma (AL) confirmed by histopathology were included in this study.

Development and validation of a deep learning model for survival prognosis of transcatheter arterial chemoembolization in patients with intermediate-stage hepatocellular carcinoma.

Purpose: We aimed to develop a deep learning-based approach to evaluate both time-to-progression (TTP) and overall survival (OS) prognosis of transcatheter arterial chemoembolization (TACE) in treatment-naïve patients with intermediate-stage hepatocellular carcinoma (HCC) and compare the approach's performance with those of radiomics and clinical models.

Methods: EfficientNetV2 was used to build a prognosis model for treatment-naïve patients with HCC. Data of 414 intermediate-stage HCC patients from one participant center were collected to construct the training and validation datasets (70%:30%) for TTP prognosis, while data of 129 intermediate-stage HCC patients from another participant center were collected as the test dataset for both TTP and OS prognosis.

A deep learning-based system for survival benefit prediction of tyrosine kinase inhibitors and immune checkpoint inhibitors in stage IV non-small cell lung cancer patients: A multicenter, prognostic study.

Background: For clinical decision making, it is crucial to identify patients with stage IV non-small cell lung cancer (NSCLC) who may benefit from tyrosine kinase inhibitors (TKIs) and immune checkpoint inhibitors (ICIs). In this study, a deep learning-based system was designed and validated using pre-therapy computed tomography (CT) images to predict the survival benefits of EGFR-TKIs and ICIs in stage IV NSCLC patients.

Methods: This retrospective study collected data from 570 patients with stage IV EGFR-mutant NSCLC treated with EGFR-TKIs at five institutions between 2010 and 2021 (data of 314 patients were from a previously registered study), and 129 patients with stage IV NSCLC treated with ICIs at three institutions between 2017 and 2021 to build the ICI test dataset.

A combination model of AD biomarkers revealed by machine learning precisely predicts Alzheimer's dementia: China Aging and Neurodegenerative Initiative (CANDI) study.

Introduction: To test the utility of the "A/T/N" system in the Chinese population, we study core Alzheimer's disease (AD) biomarkers in a newly established Chinese cohort.

Methods: A total of 411 participants were selected, including 96 cognitively normal individuals, 94 patients with mild cognitive impairment (MCI) patients, 173 patients with AD, and 48 patients with non-AD dementia. Fluid biomarkers were measured with single molecule array.

Imaging presentation of biliary adenofibroma: A case report.

Background: Biliary adenofibroma (BF) is a rare benign epithelial tumor with the possibility of malignant transformation. Its main pathological feature is a well-defined cystic or honeycomb mass. BF has no specific clinical manifestations or laboratory and imaging findings; thus, it is easily misdiagnosed before surgery.

Application of diffusion kurtosis imaging to the study of edema in solid and peritumoral areas of glioma.

Objective: When gliomas grow in an infiltrative form, high-grade malignant glioma tissue extends beyond the contrast-enhancing tumor boundary, and this diffuse non-enhancing tumor infiltration is not visible on conventional MRI. The purpose of this study was to evaluate the of diffusion kurtosis imaging (DKI)-derived parameters in a group of patients with pre-operative gliomas, evaluating changes in the solid tumor and peritumoral edema area, and investigating their use for evaluating the recurrence and prognosis of gliomas.

Methods: In this retrospective study, 51 patients with gliomas who underwent biopsy or surgery underwent DKI scans before surgery.

Diagnostic accuracy of dynamic contrast-enhanced magnetic resonance imaging for distinguishing pseudoprogression from glioma recurrence: a meta-analysis.

Background: It is crucial to differentiate accurately glioma recurrence and pseudoprogression which have entirely different prognosis and require different treatment strategies. This study aimed to assess the diagnostic accuracy of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) as a tool for distinguishing glioma recurrence and pseudoprogression.

Methods: According to particular criteria of inclusion and exclusion, related studies up to May 1, 2019, were thoroughly searched from several databases including PubMed, Embase, Cochrane Library, and Chinese biomedical databases.

Multi-classifier-based identification of COVID-19 from chest computed tomography using generalizable and interpretable radiomics features.

Purpose: To investigate the efficacy of radiomics in diagnosing patients with coronavirus disease (COVID-19) and other types of viral pneumonia with clinical symptoms and CT signs similar to those of COVID-19.

Methods: Between 18 January 2020 and 20 May 2020, 110 SARS-CoV-2 positive and 108 SARS-CoV-2 negative patients were retrospectively recruited from three hospitals based on the inclusion criteria. Manual segmentation of pneumonia lesions on CT scans was performed by four radiologists.

Decoding COVID-19 pneumonia: comparison of deep learning and radiomics CT image signatures.

Purpose: High-dimensional image features that underlie COVID-19 pneumonia remain opaque. We aim to compare feature engineering and deep learning methods to gain insights into the image features that drive CT-based for COVID-19 pneumonia prediction, and uncover CT image features significant for COVID-19 pneumonia from deep learning and radiomics framework.

Methods: A total of 266 patients with COVID-19 and other viral pneumonia with clinical symptoms and CT signs similar to that of COVID-19 during the outbreak were retrospectively collected from three hospitals in China and the USA.

End-to-end automatic differentiation of the coronavirus disease 2019 (COVID-19) from viral pneumonia based on chest CT.

Purpose: In the absence of a virus nucleic acid real-time reverse transcriptase-polymerase chain reaction (RT-PCR) test and experienced radiologists, clinical diagnosis is challenging for viral pneumonia with clinical symptoms and CT signs similar to that of coronavirus disease 2019 (COVID-19). We developed an end-to-end automatic differentiation method based on CT images to identify COVID-19 pneumonia patients in real time.

Methods: From January 18 to February 23, 2020, we conducted a retrospective study and enrolled 201 patients from two hospitals in China who underwent chest CT and RT-PCR tests, of which 98 patients tested positive for COVID-19 (118 males and 83 females, with an average age of 42 years).

A water-soluble, upconverting SrYbGdF:Er/Tm@PSI bio-probe for in vivo trimodality imaging.

Multi-modality in vivo bioimaging has great renown for offering more comprehensive information in medical diagnosis and research. Incorporating different bioimaging capabilities into one biocompatible nanoprobe requires an elegant structural design. Considering optical and magnetic properties, X-ray absorption ability, and clinical safety, we prepared a water-soluble and upconverting PSIoAm-modified Sr2Yb0.