Association of Cigarette Consumption and Mucosal Thickening in the Paranasal Sinuses on MRI.

Objective: To investigate the impact of cigarette consumption on mucosal thickening in paranasal sinuses and the relationships of smoking-related factors and dental status with mucosal thickening at different maxillary sinus locations using MRI.

Materials And Methods: This retrospective study investigated 1094 paranasal sinuses on MRIs by correlating mucosal thickening with smoking-related factors. Presence/absence of maxillary posterior teeth was correlated with mucosal thickening on the maxillary sinus floor and other sinus locations.

Association of blood cadmium and physical activity with mortality: A prospective cohort study.

Physical activity (PA) may be considered an alternative method to ameliorate the elevated mortality risks associated with cadmium exposure. In this prospective cohort study, a total of 20,253 participants (weighted mean age, 47.79 years), including 10,247 men (weighted prevalence: 50.

Unlocking the Potentials of Large Language Models in Orthodontics: A Scoping Review.

(1) Background: In recent years, large language models (LLMs) such as ChatGPT have gained significant attention in various fields, including dentistry. This scoping review aims to examine the current applications and explore potential uses of LLMs in the orthodontic domain, shedding light on how they might improve dental healthcare. (2) Methods: We carried out a comprehensive search in five electronic databases, namely PubMed, Scopus, Embase, ProQuest and Web of Science.

A Semi-Supervised Transformer-Based Deep Learning Framework for Automated Tooth Segmentation and Identification on Panoramic Radiographs.

Automated tooth segmentation and identification on dental radiographs are crucial steps in establishing digital dental workflows. While deep learning networks have been developed for these tasks, their performance has been inferior in partially edentulous individuals. This study proposes a novel semi-supervised Transformer-based framework (SemiTNet), specifically designed to improve tooth segmentation and identification performance on panoramic radiographs, particularly in partially edentulous cases, and establish an open-source dataset to serve as a unified benchmark.

Predictive markers for head and neck cancer treatment response: T1rho imaging in nasopharyngeal carcinoma.

Objectives: To investigate the potential of T1rho, a new quantitative imaging sequence for cancer, for pre and early intra-treatment prediction of treatment response in nasopharyngeal carcinoma (NPC) and compare the results with those of diffusion-weighted imaging (DWI).

Materials And Methods: T1rho and DWI imaging of primary NPCs were performed pre- and early intra-treatment in 41 prospectively recruited patients. The mean preT1rho, preADC, intraT1rho, intraADC, and % changes in T1rho (ΔT1rho%) and ADC (ΔADC%) were compared between residual and non-residual groups based on biopsy in all patients after chemoradiotherapy (CRT) with (n = 29) or without (n = 12) induction chemotherapy (IC), and between responders and non-responders to IC in the subgroup who received IC, using Mann-Whitney U-test.

Radiographic Imaging for the Diagnosis and Treatment of Patients with Skeletal Class III Malocclusion.

Skeletal Class III malocclusion is one type of dentofacial deformity that significantly affects patients' facial aesthetics and oral health. The orthodontic treatment of skeletal Class III malocclusion presents challenges due to uncertainties surrounding mandibular growth patterns and treatment outcomes. In recent years, disease-specific radiographic features have garnered interest from researchers in various fields including orthodontics, for their exceptional performance in enhancing diagnostic precision and treatment effect predictability.

Trajectories of social isolation and loneliness and the risk of incident type 2 diabetes mellitus across genetic risk score.

Aim: This study aimed to investigate the association of social isolation, loneliness, and their trajectory with the risk of developing type 2 diabetes mellitus (T2DM) across genetic risk.

Methods: We included 439,337 participants (mean age 56.3 ± 8.

Social isolation, loneliness and subsequent risk of major adverse cardiovascular events among individuals with type 2 diabetes mellitus.

Background: Individuals with type 2 diabetes mellitus (T2DM) are more vulnerable to social disconnection compared with the general population; however, there are few relevant studies investigating this issue.

Aims: To investigate whether social isolation or loneliness may be associated with subsequent risk of developing major adverse cardiovascular events, whether these associations vary according to fatal and non-fatal outcomes and how behavioural, psychological and physiological factors mediate these associations.

Methods: This longitudinal analysis included data from 19 360 individuals with T2DM at baseline (2006-2010) from the UK Biobank.

Radiologic extranodal extension for nodal staging in nasopharyngeal carcinoma.

Purpose: Extranodal extension (ENE) has the potential to add value to the current nodal staging system (N) for predicting outcome in nasopharyngeal carcinoma (NPC). This study aimed to incorporate ENE, as well as cervical nodal necrosis (CNN) to the current stage N3 and evaluated their impact on outcome prediction. The findings were validated on an external cohort.

Radiomics Analysis in Characterization of Salivary Gland Tumors on MRI: A Systematic Review.

Radiomics analysis can potentially characterize salivary gland tumors (SGTs) on magnetic resonance imaging (MRI). The procedures for radiomics analysis were various, and no consistent performances were reported. This review evaluated the methodologies and performances of studies using radiomics analysis to characterize SGTs on MRI.

Explainable machine learning via intra-tumoral radiomics feature mapping for patient stratification in adjuvant chemotherapy for locoregionally advanced nasopharyngeal carcinoma.

Purpose: This study aimed to discover intra-tumor heterogeneity signature and validate its predictive value for adjuvant chemotherapy (ACT) following concurrent chemoradiotherapy (CCRT) in locoregionally advanced nasopharyngeal carcinoma (LA-NPC).

Materials And Methods: 397 LA-NPC patients were retrospectively enrolled. Pre-treatment contrast-enhanced T1-weighted (CET1-w) MR images, clinical variables, and follow-up were retrospectively collected.

Association of Social Isolation and Loneliness With Incident Heart Failure in a Population-Based Cohort Study.

Background: Social isolation and loneliness have emerged as important risk factors for cardiovascular diseases, particularly during the coronavirus disease pandemic. However, it is unclear whether social isolation and loneliness had independent and joint associations with incident heart failure (HF).

Objectives: This study sought to examine the association of social isolation, loneliness, and their combination with incident HF.

Current Applications of Deep Learning and Radiomics on CT and CBCT for Maxillofacial Diseases.

The increasing use of computed tomography (CT) and cone beam computed tomography (CBCT) in oral and maxillofacial imaging has driven the development of deep learning and radiomics applications to assist clinicians in early diagnosis, accurate prognosis prediction, and efficient treatment planning of maxillofacial diseases. This narrative review aimed to provide an up-to-date overview of the current applications of deep learning and radiomics on CT and CBCT for the diagnosis and management of maxillofacial diseases. Based on current evidence, a wide range of deep learning models on CT/CBCT images have been developed for automatic diagnosis, segmentation, and classification of jaw cysts and tumors, cervical lymph node metastasis, salivary gland diseases, temporomandibular (TMJ) disorders, maxillary sinus pathologies, mandibular fractures, and dentomaxillofacial deformities, while CT-/CBCT-derived radiomics applications mainly focused on occult lymph node metastasis in patients with oral cancer, malignant salivary gland tumors, and TMJ osteoarthritis.

Radiomics for Discriminating Benign and Malignant Salivary Gland Tumors; Which Radiomic Feature Categories and MRI Sequences Should Be Used?

The lack of a consistent MRI radiomic signature, partly due to the multitude of initial feature analyses, limits the widespread clinical application of radiomics for the discrimination of salivary gland tumors (SGTs). This study aimed to identify the optimal radiomics feature category and MRI sequence for characterizing SGTs, which could serve as a step towards obtaining a consensus on a radiomics signature. Preliminary radiomics models were built to discriminate malignant SGTs (n = 34) from benign SGTs (n = 57) on T1-weighted (T1WI), fat-suppressed (FS)-T2WI and contrast-enhanced (CE)-T1WI images using six feature categories.

Normal size of benign upper neck nodes on MRI: parotid, submandibular, occipital, facial, retroauricular and level IIb nodal groups.

Purpose: Nodal size is an important imaging criterion for differentiating benign from malignant nodes in the head and neck cancer staging. This study evaluated the size of normal nodes in less well-documented nodal groups in the upper head and neck on magnetic resonance imaging (MRI).

Methods: Analysis was performed on 289 upper head and neck MRIs of patients without head and neck cancer.

Discriminating between benign and malignant salivary gland tumors using diffusion-weighted imaging and intravoxel incoherent motion at 3 Tesla.

Purpose: The purpose of this study was to retrospectively evaluate the diagnostic performances of diffusion-weighted imaging (DWI) and intravoxel incoherent motion (IVIM) for discriminating between benign and malignant salivary gland tumors (SGTs).

Materials And Methods: Sixty-seven patients with 71 SGTs who underwent MRI examination at 3 Tesla were included. There were 34 men and 37 women with a mean age of 57 ± 17 (SD) years (age range: 20-90 years).

Radiomics for Discrimination between Early-Stage Nasopharyngeal Carcinoma and Benign Hyperplasia with Stable Feature Selection on MRI.

Discriminating early-stage nasopharyngeal carcinoma (NPC) from benign hyperplasia (BH) on MRI is a challenging but important task for the early detection of NPC in screening programs. Radiomics models have the potential to meet this challenge, but instability in the feature selection step may reduce their reliability. Therefore, in this study, we aim to discriminate between early-stage T1 NPC and BH on MRI using radiomics and propose a method to improve the stability of the feature selection step in the radiomics pipeline.

Prognostic value of cervical nodal necrosis on staging imaging of nasopharyngeal carcinoma in era of intensity-modulated radiotherapy: a systematic review and meta-analysis.

Purposes: To systematically review and perform meta-analysis to evaluate the prognostic value of cervical nodal necrosis (CNN) on the staging computed tomography/magnetic resonance imaging (MRI) of nasopharyngeal carcinoma (NPC) in era of intensity-modulated radiotherapy.

Methods: Literature search through PubMed, EMBASE, and Cochrane Library was conducted. The hazard ratios (HRs) with 95% confidence intervals (CIs) of CNN for distant metastasis-free survival (DMFS), disease free survival (DFS) and overall survival (OS) were extracted from the eligible studies and meta-analysis was performed to evaluate the pooled HRs with 95%CI.

Potential and impact of artificial intelligence algorithms in dento-maxillofacial radiology.

Objectives: Novel artificial intelligence (AI) learning algorithms in dento-maxillofacial radiology (DMFR) are continuously being developed and improved using advanced convolutional neural networks. This review provides an overview of the potential and impact of AI algorithms in DMFR.

Materials And Methods: A narrative review was conducted on the literature on AI algorithms in DMFR.

Interaction of night shift work with polymorphism in melatonin receptor 1B gene on incident stroke.

Objectives: The aim of this study was to investigate whether melatonin receptor type 1B (MTNR1B) rs10830963 polymorphism interacts with night shift work on the risk of incident stroke.

Methods: This study included individuals free of stroke at baseline from the UK Biobank. Night-shift work was assessed by the self-reported questions.

Automatic detection and segmentation of morphological changes of the maxillary sinus mucosa on cone-beam computed tomography images using a three-dimensional convolutional neural network.

Objectives: To propose and evaluate a convolutional neural network (CNN) algorithm for automatic detection and segmentation of mucosal thickening (MT) and mucosal retention cysts (MRCs) in the maxillary sinus on low-dose and full-dose cone-beam computed tomography (CBCT).

Materials And Methods: A total of 890 maxillary sinuses on 445 CBCT scans were analyzed. The air space, MT, and MRCs in each sinus were manually segmented.