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. A total of 16,317 panoramic radiographs (1589 labeled and 14,728 unlabeled images) were collected from various datasets to create a large-scale dataset (TSI15k). The labeled images were divided into training and test sets at a 7:1 ratio, while the unlabeled images were used for semi-supervised learning. The SemiTNet was developed using a semi-supervised learning method with a label-guided teacher-student knowledge distillation strategy, incorporating a Transformer-based architecture. The performance of SemiTNet was evaluated on the test set using the intersection over union (IoU), Dice coefficient, precision, recall, and F1 score, and compared with five state-of-the-art networks. Paired t-tests were performed to compare the evaluation metrics between SemiTNet and the other networks. SemiTNet outperformed other networks, achieving the highest accuracy for tooth segmentation and identification, while requiring minimal model size. SemiTNet's performance was near-perfect for fully dentate individuals (all metrics over 99.69%) and excellent for partially edentulous individuals (all metrics over 93%). In edentulous cases, SemiTNet obtained statistically significantly higher tooth identification performance than all other networks. The proposed SemiTNet outperformed previous high-complexity, state-of-the-art networks, particularly in partially edentulous cases. The established open-source TSI15k dataset could serve as a unified benchmark for future studies.

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. A p-value of < 0.05 indicated statistical significance. RESULTS: Significant early intra-treatment changes in mean T1rho (p = 0.049) and mean ADC (p < 0.01) were detected (using paired t-test), most showing a decrease in T1rho (63.4%) and an increase in ADC (95.1%). Responders to IC (n = 17), compared to non-responders (n = 12), showed higher preT1rho (64.0 ms vs 66.5 ms) and a greater decrease in ΔT1rho% (- 7.5% vs 1.3%) (p < 0.05). The non-residual group after CRT (n = 35), compared to the residual group (n = 6), showed higher intraADC (0.96 vs 1.09 × 10-3 mm2/s) and greater increase in ΔADC% (11.7% vs 27.0%) (p = 0.02). CONCLUSION: Early intra-treatment changes are detectable on T1rho and show potential to predict tumour shrinkage after IC. T1rho may be complementary to DWI, which, unlike T1rho, did not predict response to IC but did predict non-residual disease after CRT. CLINICAL RELEVANCE STATEMENT: T1rho has the potential to complement DWI in the prediction of treatment response. Unlike DWI, it predicted shrinkage of the primary NPC after IC but not residual disease after CRT. KEY POINTS: Changes in T1rho were detected early during cancer treatment for NPC. Pre-treatment and early intra-treatment change in T1rho predicted response to IC, but not residual disease after CRT. T1rho can be used to complement DWI with DWI predicting residual disease after CRT.

Deep learning for the automatic detection and segmentation of parotid gland tumors on MRI.

OBJECTIVES: Parotid gland tumors (PGTs) often occur as incidental findings on magnetic resonance images (MRI) that may be overlooked. This study aimed to construct and validate a deep learning model to automatically identify parotid glands (PGs) with a PGT from normal PGs, and in those with a PGT to segment the tumor. MATERIALS AND METHODS: The nnUNet combined with a PG-specific post-processing procedure was used to develop the deep learning model trained on T1-weighed images (T1WI) in 311 patients (180 PGs with tumors and 442 normal PGs) and fat-suppressed (FS)-T2WI in 257 patients (125 PGs with tumors and 389 normal PGs), for detecting and segmenting PGTs with five-fold cross-validation. Additional validation set separated by time, comprising T1WI in 34 and FS-T2WI in 41 patients, was used to validate the model performance. RESULTS AND CONCLUSION: To identify PGs with tumors from normal PGs, using combined T1WI and FS-T2WI, the deep learning model achieved an accuracy, sensitivity and specificity of 98.2% (497/506), 100% (119/119) and 97.7% (378/387), respectively, in the cross-validation set and 98.5% (67/68), 100% (20/20) and 97.9% (47/48), respectively, in the validation set. For patients with PGTs, automatic segmentation of PGTs on T1WI and FS-T2WI achieved mean dice coefficients of 86.1% and 84.2%, respectively, in the cross-validation set, and of 85.9% and 81.0%, respectively, in the validation set. The proposed deep learning model may assist the detection and segmentation of PGTs and, by acting as a second pair of eyes, ensure that incidentally detected PGTs on MRI are not missed.

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 ± 8.1 years) enrolled in the UK Biobank study who were followed up until May 31, 2021. Social isolation and loneliness were self-reported and were further categorized into never, transient, incident, and persistent patterns. RESULTS: During a median follow-up of 12.7 years, 15,258 incident T2DM cases were documented. Social isolation (versus no social isolation: hazard ratio (HR) 95 % confidence interval (CI) 1.04 [1.00;1.09]) and loneliness (versus no loneliness: 1.26 [1.19;1.34]) were associated with an increased T2DM risk, independent of the genetic risk for T2DM. The interactions existed between social isolation and loneliness (Pinteraction < 0.05); the increased T2DM risk associated with social isolation was only significant among participants without loneliness. In the longitudinal analysis, only persistent social isolation (versus never social isolation: 1.22 [1.02;1.45]) was associated with an increased T2DM risk, whereas incident loneliness (versus never loneliness: 1.95 [1.40;2.71]) and persistent loneliness (2.00 [1.31;3.04]) were associated with higher T2DM risks. CONCLUSION: Social isolation and loneliness, especially their persistent pattern, were independently associated with an increased incident T2DM risk, irrespective of an individual's genetic risk. Loneliness modified the association between social isolation and incident T2DM.

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. The aim of this narrative review is to provide an overview of the valuable radiographic features in the diagnosis and management of skeletal Class III malocclusion. Based on the existing literature, a series of analyses on lateral cephalograms have been concluded to identify the significant variables related to facial type classification, growth prediction, and decision-making for tooth extractions and orthognathic surgery in patients with skeletal Class III malocclusion. Furthermore, we summarize the parameters regarding the inter-maxillary relationship, as well as different anatomical structures including the maxilla, mandible, craniofacial base, and soft tissues from conventional and machine learning statistical models. Several distinct radiographic features for Class III malocclusion have also been preliminarily observed using cone beam computed tomography (CBCT) and magnetic resonance imaging (MRI).

Early detection of nasopharyngeal carcinoma: performance of a short contrast-free screening magnetic resonance imaging.

BACKGROUND: Although contrast-enhanced magnetic resonance imaging (MRI) detects early-stage nasopharyngeal carcinoma (NPC) not detected by endoscopic-guided biopsy (EGB), a short contrast-free screening MRI would be desirable for NPC screening programs. This study evaluated a screening MRI in a plasma Epstein-Barr virus (EBV)-DNA NPC screening program. METHODS: EBV-DNA-screen-positive patients underwent endoscopy, and endoscopy-positive patients underwent EGB. EGB was negative if the biopsy was negative or was not performed. Patients also underwent a screening MRI. Diagnostic performance was based on histologic confirmation of NPC in the initial study or during a follow-up period of at least 2 years. RESULTS: The study prospectively recruited 354 patients for MRI and endoscopy; 40/354 (11.3%) endoscopy-positive patients underwent EGB. Eighteen had NPC (5.1%), and 336 without NPC (94.9%) were followed up for a median of 44.8 months. MRI detected additional NPCs in 3/18 (16.7%) endoscopy-negative and 2/18 (11.1%) EGB-negative patients (stage I/II, n = 4; stage III, n = 1). None of the 24 EGB-negative patients who were MRI-negative had NPC. MRI missed NPC in 2/18 (11.1%), one of which was also endoscopy-negative. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of MRI, endoscopy, and EGB were 88.9%, 91.1%, 34.8%, 99.4%, and 91.0%; 77.8%, 92.3%, 35.0%, 98.7%, and 91.5%; and 66.7%, 92.3%, 31.6%, 98.1%, and 91.0%, respectively. CONCLUSION: A quick contrast-free screening MRI complements endoscopy in NPC screening programs. In EBV-screen-positive patients, MRI enables early detection of NPC that is endoscopically occult or negative on EGB and increases confidence that NPC has not been missed.

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 (N8th) 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. METHODS & MATERIALS: Pre-treatment MRI of 750 patients from the internal cohort were retrospectively reviewed. Predictive values of six modified nodal staging systems that incorporated four patterns of ENE and two patterns of CNN to the current stage N3 for disease-free survival (DFS) were compared with that of N8th using multivariate cox-regression and concordance statistics in the internal cohort. Performance of stage N3 for predicting disease recurrence was calculated. An external cohort of 179 patients was used to validate the findings. RESULTS: Incorporation of advanced ENE, which infiltrates into adjacent muscle/skin/salivary glands outperformed the other five modifications for predicting outcomes (p < 0.01) and achieved a significantly higher c-index for 5-year DFS (0.69 vs 0.72) (p < 0.01) when compared with that of N8th staging system. By adding advanced ENE to the current N3 increased the sensitivity for predicting disease recurrence from 22.4 % to 47.1 %. The finding was validated in the external cohort (5-year DFS 0.65 vs. 0.72, p < 0.01; sensitivity of stage N3 increased from 14.0 % to 41.9 % for disease recurrence). CONCLUSION: Results from two centre cohorts confirmed that the radiological advanced ENE should be considered as a criterion for stage N3 disease in NPC.

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. We systematically reviewed studies published until July 2023, which employed radiomics analysis to characterize SGTs on MRI. In total, 14 of 98 studies were eligible. Each study examined 23-334 benign and 8-56 malignant SGTs. Least absolute shrinkage and selection operator (LASSO) was the most common feature selection method (in eight studies). Eleven studies confirmed the stability of selected features using cross-validation or bootstrap. Nine classifiers were used to build models that achieved area under the curves (AUCs) of 0.74 to 1.00 for characterizing benign and malignant SGTs and 0.80 to 0.96 for characterizing pleomorphic adenomas and Warthin's tumors. Performances were validated using cross-validation, internal, and external datasets in four, six, and two studies, respectively. No single feature consistently appeared in the final models across the studies. No standardized procedure was used for radiomics analysis in characterizing SGTs on MRIs, and various models were proposed. The need for a standard procedure for radiomics analysis is emphasized.

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. We identified single predictive radiomic feature from primary gross tumor volume (GTVnp) and defined predicted subvolume by calculating voxel-wised feature mapping and within GTVnp. We independently validate predictive value of identified feature and associated predicted subvolume. RESULTS: Only one radiomic feature, gldm_DependenceVariance in 3 mm-sigma LoG-filtered image, was discovered as a signature. In the high-risk group determined by the signature, patients received CCRT + ACT achieved 3-year disease free survival (DFS) rate of 90% versus 57% (HR, 0.20; 95%CI, 0.05-0.94; P = 0.007) for CCRT alone. The multivariate analysis showed patients receiving CCRT + ACT had a HR of 0.21 (95%CI: 0.06-0.68, P = 0.009) for DFS compared to those receiving CCRT alone. The predictive value can also be generalized to the subvolume with multivariate HR of 0.27 (P = 0.017) for DFS. CONCLUSION: The signature with its heterogeneity mapping could be a reliable and explainable ACT decision-making tool in clinical practice.

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. METHODS: The UK Biobank study is a population-based cohort study. Social isolation and loneliness were assessed using self-reported questionnaires. HF cases were identified by linking hospital records and death registries. The weighted polygenic risk score associated with HF was calculated. RESULTS: Among the 464,773 participants (mean age: 56.5 ± 8.1 years, 45.3% male), 12,898 incident HF cases were documented during a median follow-up of 12.3 years. Social isolation (most vs least: adjusted HR: 1.17; 95% CI:1.11-1.23) and loneliness (yes vs no: adjusted HR: 1.19; 95% CI: 1.11-1.27) were significantly associated with an increased risk of incident HF. The association between an elevated risk of HF and social isolation was modified by loneliness (Pinteraction = 0.034). A gradient of association between social isolation and the risk of incident HF was found only among individuals without loneliness (Ptrend < 0.001), but not among those with loneliness (Ptrend = 0.829). These associations were independent of the genetic risk of HF. CONCLUSIONS: Social isolation and loneliness were independently associated with a higher likelihood of incident HF regardless of genetic risk. The association between social isolation and incident HF was potentially modified by loneliness status.

Recommendations for Epstein-Barr virus-based screening for nasopharyngeal cancer in high- and intermediate-risk regions.

A meeting of experts was held in November 2021 to review and discuss available data on performance of Epstein-Barr virus (EBV)-based approaches to screen for early stage nasopharyngeal carcinoma (NPC) and methods for the investigation and management of screen-positive individuals. Serum EBV antibody and plasma EBV DNA testing methods were considered. Both approaches were found to have favorable performance characteristics and to be cost-effective in high-risk populations. In addition to endoscopy, use of magnetic resonance imaging (MRI) to investigate screen-positive individuals was found to increase the sensitivity of NPC detection with minimal impact on cost-effectiveness of the screening program.

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). SGTs included 21 malignant tumors (MTs) and 50 benign SGTs (33 pleomorphic adenomas [PAs] and 17 Warthin's tumors [WTs]). For each SGT, DWI and IVIM parameters, mean, skewness, and kurtosis of apparent diffusion coefficient (ADC), pure diffusion coefficient (D), pseudo-diffusion coefficient (D*) and perfusion volume fraction (f) were calculated and further compared between SGTs using univariable analysis. Areas under the curves (AUC) of receiver operating characteristic of significant parameters were compared using the Delong test. RESULTS: Significant differences in ADCmean, Dmean and D*mean were found between SGTs (P < 0.001). The highest AUC values were obtained for ADCmean (0.949) for identifying PAs and D*mean (0.985) for identifying WTs and skewness and kurtosis did not outperform mean. To discriminate benign from malignant SGTs with thresholds set to maximize Youden index, IVIM and DWI produced accuracies of 85.9% (61/71; 95% CI: 75.6-93.0) and 77.5% (55/71; 95% CI: 66.0-86.5) but misdiagnosed MTs as benign in 28.6% (6/21) and 61.9% (13/21) of SGTs, respectively. After maximizing specificity to 100% for benign SGTs, the accuracies of IVIM and DWI decreased to 76.1% (54/71; 95% CI: 64.5-85.4) and 64.8% (46/71; 95% CI: 52.5-75.8) but no MTs were misdiagnosed as benign. IVIM and DWI correctly diagnosed 66.0% (33/50) and 50.0% (25/50) of benign SGTs and 46.5% (33/71) and 35.2% (25/71) of all SGTs, respectively. CONCLUSION: IVIM is more accurate than DWI for discriminating between benign and malignant SGTs because of its advantage in detecting WTs. Thresholds set by maximizing specificity for benign SGTs may be advantageous in a clinical setting.

Plasma Epstein-Barr Virus DNA and Risk of Future Nasopharyngeal Cancer.

BACKGROUND: We previously conducted a prospective study to show that nasopharyngeal cancer (NPC) screening with circulating Epstein­Barr virus (EBV) DNA analysis can improve survival. However, the long-term significance of positive results in individuals without cancer was unclear. METHODS: We conducted a second-round screening at a median of 43 months after the initial screening. Participants with detectable plasma EBV DNA were retested in 4 weeks, and those with persistently positive results were investigated with nasal endoscopy and magnetic resonance imaging. RESULTS: Of the 20,174 volunteers who participated in the first-round screening, 17,838 (88.6%) were rescreened. Among them, 423 (2.37%) had persistently detectable plasma EBV DNA. Twenty-four patients were identified as having NPC. A significantly higher proportion of patients had stage I/II cancer than in a historical cohort (67% vs. 20%; chi-square test, P<0.001), and they had superior 3-year progression-free survival (100% vs. 78.8%). Compared with participants with undetectable plasma EBV DNA in the first round of screening, participants with transiently and persistently positive results in the first round were more likely to have a cancer identified in the second round, with relative risks of 4.4 (95% confidence interval, 1.3 to 15.0) and 16.8 (95% confidence interval, 5.7 to 49.6), respectively. CONCLUSIONS: Individuals with detectable plasma EBV DNA but without an immediately identifiable NPC were more likely to have the cancer identified in another round of screening performed 3 to 5 years later. (Funded by Kadoorie Charitable Foundation and others; ClinicalTrials.gov number, NCT02063399.)

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. Social isolation and loneliness were measured using self-report questionnaires. The study outcomes included the first events of myocardial infarction (MI) or stroke (n=2273) and all-cause (n=2820) or cardiovascular disease-related mortality through linked hospital data or death registries. Results: Over a median follow-up of 12.4 years (interquartile range (IQR): 11.6-13.3 years), participants who were more socially isolated (most social isolation vs least social isolation) experienced increased risks for all-cause (hazard ratio (HR) : 1.33, 95% confidence interval (CI): 1.19 to 1.47) and cardiovascular disease (HR: 1.36, 95% CI: 1.17 to 1.59) mortality but not first MI or stroke. Loneliness (yes vs no) was associated with a greater risk for a composite of incident MI or stroke (HR: 1.37, 95% CI: 1.19 to 1.57) but not mortality. Social isolation was associated with fatal MI and stroke, whereas loneliness was associated with non-fatal MI and stroke. The significant associations of social isolation and loneliness with outcomes were mainly mediated by behavioural factors (mediating proportion: 17.8%-28.2% and 17.6%-17.8%, respectively). Conclusions: Among individuals with T2DM, social isolation and loneliness are associated with a greater risk of developing major adverse cardiovascular events, with differences in both risks stratified according to fatal and non-fatal events and underlying mediating factors.

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. The short axial diameters (SAD) of the largest node in the parotid, submandibular, occipital, facial, retroauricular and Level IIb of the upper internal jugular nodal groups were documented and compared to the commonly used threshold of ≥ 10 mm for diagnosis of a malignant node. RESULTS: Normal nodes in the parotid, occipital, retroauricular and Level IIb groups were small with a mean SAD ranging from 3.8 to 4.4 mm, nodes in the submandibular group were larger with a mean SAD of 5.5 mm and facial nodes were not identified. A size ≥ 10 mm was found in 0.8% of submandibular nodes. Less than 10% of the other nodal group had a SAD of ≥ 6 mm and none of them had a SAD ≥ 8 mm. CONCLUSION: To identify malignant neck nodes in these groups there is scope to reduce the size threshold of ≥ 10 mm to improve sensitivity without substantial loss of specificity.

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. The discrimination performances of these preliminary models were evaluated using 5-fold-cross-validation with 100 repetitions and the area under the receiver operating characteristic curve (AUC). The differences between models' performances were identified using one-way ANOVA. Results show that the best feature categories were logarithm for T1WI and CE-T1WI and exponential for FS-T2WI, with AUCs of 0.828, 0.754 and 0.819, respectively. These AUCs were higher than the AUCs obtained using all feature categories combined, which were 0.750, 0.707 and 0.774, respectively (p < 0.001). The highest AUC (0.846) was obtained using a combination of T1WI + logarithm and FS-T2WI + exponential features, which reduced the initial features by 94.0% (from 1015 × 3 to 91 × 2). CE-T1WI did not improve performance. Using one feature category rather than all feature categories combined reduced the number of initial features without compromising radiomic performance.

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. A radiomics model was trained using data from 442 patients (221 early-stage T1 NPC and 221 with BH) scanned at 3T and tested on 213 patients (99 early-stage T1 NPC and 114 BH) scanned at 1.5T. To verify the improvement in feature selection stability, we compared our proposed ensemble technique, which uses a combination of bagging and boosting (BB-RENT), with the well-established elastic net. The proposed radiomics model achieved an area under the curve of 0.85 (95% confidence interval (CI): 0.82−0.89) and 0.80 (95% CI: 0.74−0.86) in discriminating NPC and BH in the 3T training and 1.5T testing cohort, respectively, using 17 features selected from a pool of 422 features by the proposed feature selection technique. BB-RENT showed a better feature selection stability compared to the elastic net (Jaccard index = 0.39 ± 0.14 and 0.24 ± 0.06, respectively; p < 0.001).

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. RESULTS: Nine studies, which investigated the prognostic values of 6 CNN patterns on MRI were included. Six/9 studies were eligible for meta-analysis, which investigated the CNN presence/absence in any nodal group among 4359 patients. The pooled unadjusted HRs showed that the CNN presence predicted poor DMFS (HR =1.89, 95%CI =1.72-2.08), DFS (HR =1.57, 95%CI =1.08-2.26), and OS (HR =1.87, 95%CI =1.69-2.06). The pooled adjusted HRs also showed the consistent results for DMFS (HR =1.34, 95%CI =1.17-1.54), DFS (HR =1.30, 95%CI =1.08-1.56), and OS (HR =1.61, 95%CI =1.27-2.04). Results shown in the other studies analysing different CNN patterns indicated the high grade of CNN predicted poor outcome, but meta-analysis was unable to perform because of the heterogeneity of the analysed CNN patterns. CONCLUSION: The CNN observed on the staging MRI is a negative factor for NPC outcome, suggesting that the inclusion of CNN is important in the future survival analysis. However, whether and how should CNN be included in the staging system warrant further evaluation.

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. MTNR1B rs10830963 was directly genotyped (CC, GC, and GG). Incident stroke was ascertained through hospital records and death registries. Cox proportional hazards models were employed to examine the associations of night shift work and MTNR1B rs10830963 with the risk of incident stroke. RESULTS: A total of 242 194 participants were finally included (mean age: 52.95 years; 51.63% women). Over 12-year follow-up, 3287 incident stroke events occurred. Night shift work increased the risk of incident stroke [hazard ratio (HR) 1.13, 95% confidence interval (CI) 1.00-1.28] after adjusting for socio-demographics, and this association attenuated after additional adjustment for lifestyle factors (HR 1.06, 95% CI 0.94-1.20). MTNR1B rs10830963 polymorphism modified the association between night shift work and incident stroke (Pfor interaction =0.010). In the Cox models adjusted for socio-demographics and lifestyle factors, among night-shift workers, minor allele G was associated with a reduced risk of incident stroke (GC versus CC, HR 0.74, 95% CI 0.58-0.95; GG versus CC, HR 0.65, 95% CI 0.40-1.06; P for trend=0.010); while night shift work was associated with a higher stroke risk only among MTNR1B rs10830963 CC carriers (HR 1.23, 95% CI 1.05-1.44) but not GC/GG carriers. CONCLUSIONS: These results suggest that MTNR1B rs10830963 may potentially modify the associations between night shift work and incident stroke.