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.

B1 Power Modification for Amide Proton Transfer Imaging in Parotid Glands: A Strategy for Image Quality Accommodation and Evaluation of Tumor Detection Feasibility.

BACKGROUND: In the application of APTw protocols for evaluating tumors and parotid glands, inhomogeneity and hyperintensity artifacts have remained an obstacle. This study aimed to improve APTw imaging quality and evaluate the feasibility of difference B1 values to detect parotid tumors. METHODS: A total of 31 patients received three APTw sequences to acquire 32 lesions and 30 parotid glands (one patient had lesions on both sides). Patients received T2WI and 3D turbo-spin-echo (TSE) APTw imaging on a 3.0 T scanner for three sequences (B1 = 2 µT, 1 µT, and 0.7 µT in APTw 1, 2, and 3, respectively). APTw image quality was evaluated using four-point Likert scales in terms of integrity and hyperintensity artifacts. Image quality was compared between the three sequences. An evaluable group and a trustable group were obtained for APTmean value comparison. RESULTS: Tumors in both APT2 and APT3 had fewer hyperintensity artifacts than in APT1. With B1 values decreasing, tumors had less integrity in APTw imaging. APTmean values of tumors were higher than parotid glands in traditional APT1 sequence though not significant, while the APTmean subtraction value was significantly different. CONCLUSIONS: Applying a lower B1 value could remove hyperintensity but could also compromise its integrity. Combing different APTw sequences might increase the feasibility of tumor detection.

Pharmacists' Attitudes towards Medically Assisted Dying.

AIMS: We aimed to explore pharmacists' attitudes and support toward medically assisted dying (MaiD) through the End of Life Choice Act 2019 (EOLC), their willingness to provide services in this area of practice, and the influences on their decisions. METHODS: The study was conducted via an anonymous, online QualtricsTM survey of pharmacists. Registered New Zealand pharmacists who agreed to receive surveys from the two Schools of Pharmacy as part of their Annual Practicing Certificate renewal were invited to participate through an email with a Qualtrics URL link. The survey contained questions regarding demographics, awareness, knowledge, support for, and attitudes and willingness to participate. RESULTS: Of the 335 responses received, 289 were valid and included in the analysis. Most participants supported legally assisted medical dying (58%), almost a third of participants did not support it (29%), and 13% of respondents were unsure. The five primary considerations that participants perceived to be beneficial included support from legislation, respect for patient autonomy, discussions around morality, ending suffering, and preserving dignity. The main concerns were legal, personal bias, palliation, stigmatisation, and vulnerability. CONCLUSIONS: The influences on the decision by pharmacists to support and willingness to participate in the provision of services consistent with the EOLC are complex and multifactorial. Diverse factors may influence attitudes, of which religion is the most significant factor in not supporting the Act or willingness to participate. Clarity and standardised guidance to ensure that assisted dying queries are appropriately managed in practice would help to address any potential access issues.

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.

Rheumatology Patients' Experiences of a Mandatory Nationwide Transition to an Adalimumab Biosimilar.

OBJECTIVE: Transitions to biosimilars are common to reduce the cost burden of biologics. While brand changes can be daunting for patients, few studies have explored patients' experiences with the transitioning process. This study examined rheumatology patients' experiences with a mandatory nationwide brand change to an adalimumab biosimilar. METHODS: People with rheumatic diseases involved in the adalimumab transition in Aotearoa New Zealand completed a nationwide online survey. Participants (n = 117, 48% with rheumatoid arthritis) reported their satisfaction with the biosimilar, logistics and supply, information and communication, and availability of support. They also reported what did and did not go well during the transition and provided recommendations for future transitions. RESULTS: The mean [SD] satisfaction score with the transition was 6.2 [3.2] on a 0-10 scale, with 10 indicating high satisfaction. Participants were the least satisfied with the support and information from patient support organizations, and training for the device during the transition. Participants were most satisfied with the biosimilar supply, support from pharmacists, and how early they were informed before the transition occurred. After the transition, participants were less satisfied with the device quality, patient support program, biosimilar safety and efficacy, and the provision of alcohol wipes and sharps bins (p < 0.05 for all). Satisfaction with training for the biosimilar device (B = .25, p = .036) predicted overall satisfaction. Participants appreciated less injection pain and the ease of the biosimilar device. The lack of alcohol wipes and loss of the bio-originator support program were viewed negatively. CONCLUSION: Future biosimilar transitions should ensure the availability of alcohol wipes, sharps bins, and a comparable patient support program. Patient support organizations could be involved in providing information to patients about the change.

Dual-Level Augmentation Radiomics Analysis for Multisequence MRI Meningioma Grading.

BACKGROUND: Preoperative, noninvasive prediction of meningioma grade is important for therapeutic planning and decision making. In this study, we propose a dual-level augmentation strategy incorporating image-level augmentation (IA) and feature-level augmentation (FA) to tackle class imbalance and improve the predictive performance of radiomics for meningioma grading on Magnetic Resonance Imaging (MRI). METHODS: This study recruited 160 consecutive patients with pathologically proven meningioma (129 low-grade (WHO grade I) tumors; 31 high-grade (WHO grade II and III) tumors) with preoperative multisequence MRI imaging. A dual-level augmentation strategy combining IA and FA was applied and evaluated in 100 repetitions in 3-, 5-, and 10-fold cross-validation. RESULTS: The best area under the receiver operating characteristics curve of our method in 100 repetitions was ≥0.78 in all cross-validations. The corresponding cross-validation sensitivities (cross-validation specificity) were 0.72 (0.69), 0.76 (0.71), and 0.63 (0.82) in 3-, 5-, and 10-fold cross-validation, respectively. The proposed method achieved significantly better performance and distribution of results, outperforming single-level augmentation (IA or FA) or no augmentation in each cross-validation. CONCLUSIONS: The dual-level augmentation strategy using IA and FA significantly improves the performance of the radiomics model for meningioma grading on MRI, allowing better radiomics-based preoperative stratification and individualized treatment.

Health Care Providers' Experiences of a Mandatory Nationwide Transition to an Adalimumab Biosimilar.

OBJECTIVE: Transitioning patients to biosimilars has become common to reduce costs and improve access. However, it is unclear how the transition process impacts health care providers at the frontline of the brand change. This study explores health care providers' experiences of a mandatory brand change to an adalimumab biosimilar. METHODS: A cross-sectional study was conducted in Aotearoa New Zealand with 164 providers involved in the nationwide adalimumab brand change. Rheumatologists (n = 39), rheumatology nurses (n = 16), and pharmacists (n = 109) completed a survey that assessed their satisfaction with logistics and supply, information and education, support, and administrative workload and reported what did and did not go well during the transition. RESULTS: The mean satisfaction score (0-10) with the transition was 5.7 (SD = 2.6). Providers were the least satisfied with training for the biosimilar device, information from government agencies, and administrative workload during the transition. Satisfaction with adalimumab safety, efficacy and device quality, and the availability of sharps bins, alcohol wipes, and patient support was lower following the transition. Satisfaction with administrative workload (B = 0.37, P < 0.001) and training for the device (B = 0.20, P = 0.020) predicted overall satisfaction. Providers reported that a poorly implemented initial authorization process, loss of a patient support program, and insufficient communication between providers complicated the transition. The citrate-free preservative and longer authorization duration after the transition were viewed positively. CONCLUSION: Providers experienced an increased workload and reported less satisfaction with the biosimilar following the transition. Experiences may be improved by ensuring training for the device, a high-quality patient support program, and functioning authorization processes throughout the transition.

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.

Understanding the Risk Factors and Stressors Impacting Optimal Work Practices in New Zealand Pharmacies: A S.H.E.L.L Model Analysis.

(1) Background: The safe performance of pharmacists is an important issue for patients and regulators. It is recognized that pharmacists interact with a variety of healthcare professionals and act as a bridge between other healthcare providers and systems and patients in the health setting. There has been growing activity in exploring factors that impact optimal performance and determinants that are linked with medication errors and practice incidents. The aviation and military industries have used S.H.E.L.L modeling to identify how personnel interact with factors that affect outcomes. A human factors approach is a useful angle to take when trying to improve optimal practice. Little is known about the experiences of New Zealand pharmacists and S.H.E.L.L factors that affect day-to-day practices in their work environment. (2) Methods: We investigated environment, team, and organizational considerations as the determining factors of optimal work practices using an anonymous online questionnaire. The questionnaire was built from a modified version of the software, hardware, environment, and liveware (S.H.E.L.L) model. This identified components of a work system that were vulnerable and that provided risks to optimal practice. Participants were New Zealand pharmacists approached through a subscriber list provided by the regulatory authority of the profession. (3) Results: We received responses from 260 participants (8.56%). The majority of participants indicated that optimal practice was occurring. More than 95% of respondents agreed that knowledge, fatigue interruptions, complacency, and stress affected optimal practice. Equipment and tools, medication arrangement on the shelf, lighting, physical layout, and communication with staff and patients were important factors for optimal practice. A smaller cohort of participants, 13 percent (n = 21), stated that dispensing processes, dissemination, and enforcement of standard operating procedures and procedural guidance did not affect pharmacy practice, 21.3% responded that professional and ethical requirements did not affect optimal practice, 20% stated that having a staffroom affected optimal practice, 20% did not think substance use affected optimal practice, and 30% did not state that cultural differences affected optimal practice. Optimal practice is constrained when there is a lack of experience, professionalism, and communication among staff, patients, and external agencies. COVID-19 also has had an impact on pharmacists both personally and in their work environments. Exploring how the pandemic has affected pharmacists and their work environment warrants further research. (4) Conclusions: Pharmacists across New Zealand agreed that optimal practices were occurring and considered other factors that were perceived to not affect optimal practice. A human factor S.H.E.L.L framework has been used to analyze themes to understand the optimal practice. The rising body of international literature on the effect of the pandemic on pharmacy practice serves as a foundation for many of these themes. Longitudinal data would be useful in exploring some factors, such as pharmacist well-being over time.

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.

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.

Community Pharmacists' Beliefs about Suboptimal Practice during the Times of COVID-19.

Introduction: Community pharmacies are high-performance workplaces; if the environment is not conducive to safe practice, mistakes can occur. There has been increasing demand for pharmacists during the COVID-19 pandemic as they have become integral to the response. Suboptimal practices in the work environment and with pharmacists and their teams can impact the safe delivery of services. New Zealand pharmacists' perceptions of the current work environment and beliefs around whether suboptimal practice have increased within the last five years and the effect of the COVID-19 pandemic on their practices are unknown. Aim/Objectives: To assess what New Zealand pharmacists associate with suboptimal practice in their workplace and investigate the effect of the COVID-19 pandemic on pharmacists and their workplaces. Methods: We employed an anonymous online questionnaire derived from a human factors framework utilised in the aviation industry to explore the potential environment, team and organisational factors as the determinants of suboptimal work practices. The software, hardware, environment and liveware (S.H.E.L.L) model was adapted to create questions classifying the risk factors to potentially identify aspects of work systems that are vulnerable and may provide risks to optimal practice. Additional perceptions around the effect of COVID-19 on their workplace and roles as pharmacists were explored. Participants were community pharmacists working in New Zealand contacted via a mailing list of the responsible authority for the profession. Findings: We received responses from 260 participants. Most participants indicated that suboptimal practice had increased in the last 5 years (79.8%). The majority of participants indicated that COVID-19 had impacted their workplaces (96%) and their roles as pharmacists (92.1%). Participants perceived that suboptimal practice was associated with a lack of leadership and appropriate management; poor access to resourcing, such as adequate staff and narrow time constraints for work tasks; a lack of procedures; competition; and stress. A lack of experience, professionalism and poor communication between staff, patients and external agencies were also issues. COVID-19 has affected pharmacists personally and their work environments. Further study in this area is required. Conclusions: We have identified that pharmacists across all sectors of New Zealand agreed that suboptimal practices had increased in the last 5 years. A human factors S.H.E.L.L framework can be used to classify themes to understand the increases in suboptimal practice and the role of COVID-19 on pharmacist practice. Many of these themes build on the growing body of the international literature around the effect of the pandemic on pharmacist practice. Areas for which there are less historical data to compare longitudinally include pharmacist wellbeing and the impact of COVID-19.

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).

Risk factors for complications after surgery for pancreatic neuroendocrine tumors.

BACKGROUND: Surgical resection is the only potentially curative treatment for pancreatic neuroendocrine tumors. The choice for the type of procedure is influenced by the expected oncological benefit and the anticipated risk of procedure-specific complications. Few studies have focused on complications in these patients. This cohort study aimed to assess complications and risk factors after resections of pancreatic neuroendocrine tumors. METHODS: Patients undergoing resection of a pancreatic neuroendocrine tumor were identified within 2 centers of excellence. Complications were assessed according to the Clavien-Dindo classification and the comprehensive complication index. Logistic regression was performed to compare surgical procedures with adjustment for potential confounders (Clavien-Dindo ≥3). RESULTS: The cohort comprised 123 patients, including 12 enucleations, 50 distal pancreatectomies, 51 pancreatoduodenectomies, and 10 total/combined pancreatectomies. Mortality was 0.8%, a severe complication occurred in 41.5%, and the failure-to-rescue rate was 2.0%. The median comprehensive complication index was 22.6 (0-100); the comprehensive complication index increased after more extensive resections. After adjustment, a pancreatoduodenectomy, as compared to a distal pancreatectomy, increased the risk for a severe complication (odds ratio 3.13 [95% confidence interval 1.32-7.41]). Of the patients with multiple endocrine neoplasia type 1 or von Hippel-Lindau, 51.9% developed a severe complication vs 38.5% with sporadic disease. After major resections, morbidity was significantly higher in patients with multiple endocrine neoplasia type 1/von Hippel-Lindau (comprehensive complication index 45.1 vs 28.9, P = .029). CONCLUSION: Surgery for pancreatic neuroendocrine tumors is associated with a high rate of complications but low failure-to-rescue in centers of excellence. Complications are procedure-specific. Major resections in patients with multiple endocrine neoplasia type 1/von Hippel-Lindau appear to increase the risk of complications.

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. Low-dose CBCTs were divided into training, training-monitoring, and testing datasets at a 7:1:2 ratio. Full-dose CBCTs were used as a testing dataset. A three-step CNN algorithm built based on V-Net and support vector regression was trained on low-dose CBCTs and tested on the low-dose and full-dose datasets. Performance for detection of MT and MRCs using area under the curves (AUCs) and for segmentation using Dice similarity coefficient (DSC) was evaluated. RESULTS: For the detection of MT and MRCs, the algorithm achieved AUCs of 0.91 and 0.84 on low-dose scans and of 0.89 and 0.93 on full-dose scans, respectively. The median DSCs for segmenting the air space, MT, and MRCs were 0.972, 0.729, and 0.678 on low-dose scans and 0.968, 0.663, and 0.787 on full-dose scans, respectively. There were no significant differences in the algorithm performance between low-dose and full-dose CBCTs. CONCLUSIONS: The proposed CNN algorithm has the potential to accurately detect and segment MT and MRCs in maxillary sinus on CBCT scans with low-dose and full-dose protocols. CLINICAL RELEVANCE: An implementation of this artificial intelligence application in daily practice as an automated diagnostic and reporting system seems possible.

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. Most of these models showed high performance, and some of them even outperformed human experts. The models with performance on par with human experts have the potential to serve as clinically practicable tools to achieve the earliest possible diagnosis and treatment, leading to a more precise and personalized approach for the management of maxillofacial diseases. Challenges and issues, including the lack of the generalizability and explainability of deep learning models and the uncertainty in the reproducibility and stability of radiomic features, should be overcome to gain the trust of patients, providers, and healthcare organizers for daily clinical use of these models.

A convolutional neural network combined with positional and textural attention for the fully automatic delineation of primary nasopharyngeal carcinoma on non-contrast-enhanced MRI.

BACKGROUND: Convolutional neural networks (CNNs) have the potential to automatically delineate primary nasopharyngeal carcinoma (NPC) on magnetic resonance imaging (MRI), but currently, the literature lacks a module to introduce valuable pre-computed features into a CNN. In addition, most CNNs for primary NPC delineation have focused on contrast-enhanced MRI. To enable the use of CNNs in clinical applications where it would be desirable to avoid contrast agents, such as cancer screening or intra-treatment monitoring, we aim to develop a CNN algorithm with a positional-textural fully-connected attention (FCA) module that can automatically delineate primary NPCs on contrast-free MRI. METHODS: This retrospective study was performed in 404 patients with NPC who had undergone staging MRI. A proposed CNN algorithm incorporated with our positional-textural FCA module (Aproposed ) was trained on manually delineated tumours (M1st ) to automatically delineate primary NPCs on non-contrast-enhanced T2-weighted fat-suppressed (NE-T2W-FS) images. The performance of Aproposed , three well-established CNNs, Unet (Aunet ), Attention-Unet (Aatt ) and Dense-Unet (Adense ), and a second manual delineation repeated to evaluate human variability (M 2 nd ) were measured by comparing to the reference standard M 1 st to obtain the Dice similarity coefficient (DSC) and average surface distance (ASD). The Wilcoxon rank test was used to compare the performance of Aproposed against Aunet , Aatt , Adense and M 2 nd . RESULTS: Aproposed showed a median DSC of 0.79 (0.10) and ASD of 0.66 (0.84) mm. It performed better than the well-established networks Aunet [DSC =0.75 (0.12) and ASD =1.22 (1.73) mm], Aatt [DSC =0.75 (0.10) and ASD =0.96 (1.16) mm] and Adense [DSC =0.71 (0.14) and ASD =1.67 (1.92) mm] (all P<0.01), but slightly worse when compared to M 2 nd [DSC =0.81 (0.07) and ASD =0.56 (0.80) mm] (P<0.001). CONCLUSIONS: The proposed CNN algorithm has potential to accurately delineate primary NPCs on non-contrast-enhanced MRI.

Convolutional neural network in nasopharyngeal carcinoma: how good is automatic delineation for primary tumor on a non-contrast-enhanced fat-suppressed T2-weighted MRI?

PURPOSE: Convolutional neural networks (CNNs) show potential for delineating cancers on contrast-enhanced MRI (ce-MRI) but there are clinical scenarios in which administration of contrast is not desirable. We investigated performance of the CNN for delineating primary nasopharyngeal carcinoma (NPC) on non-contrast-enhanced images and compared the performance to that on ce-MRI. MATERIALS AND METHODS: We retrospectively analyzed primary NPC in 195 patients using a well-established CNN, U-Net, for tumor delineation on the non-contrast-enhanced fat-suppressed (fs)-T2W, ce-T1W and ce-fs-T1W images. The CNN-derived delineations were compared to manual delineations to obtain Dice similarity coefficient (DSC) and average surface distance (ASD). The DSC and ASD on fs-T2W were compared to those on ce-MRI. Primary tumor volumes (PTVs) of CNN-derived delineations were compared to that of manual delineations. RESULTS: The CNN for NPC delineation on fs-T2W images showed similar DSC (0.71 ± 0.09) and ASD (0.21 ± 0.48 cm) to those on ce-T1W images (0.71 ± 0.09 and 0.17 ± 0.19 cm, respectively) (p > 0.05), and lower DSC but similar ASD to ce-fs-T1W images (0.73 ± 0.09, p < 0.001; and 0.17 ± 0.20 cm, p > 0.05). The CNN overestimated PTVs on all sequences (p < 0.001). CONCLUSION: The CNN showed promise for NPC delineation on fs-T2W images in cases where it is desirable to avoid contrast agent injection. The CNN overestimated PTVs on all sequences.

Convolutional neural network for discriminating nasopharyngeal carcinoma and benign hyperplasia on MRI.

OBJECTIVES: A convolutional neural network (CNN) was adapted to automatically detect early-stage nasopharyngeal carcinoma (NPC) and discriminate it from benign hyperplasia on a non-contrast-enhanced MRI sequence for potential use in NPC screening programs. METHODS: We retrospectively analyzed 412 patients who underwent T2-weighted MRI, 203 of whom had biopsy-proven primary NPC confined to the nasopharynx (stage T1) and 209 had benign hyperplasia without NPC. Thirteen patients were sampled randomly to monitor the training process. We applied the Residual Attention Network architecture, adapted for three-dimensional MR images, and incorporated a slice-attention mechanism, to produce a CNN score of 0-1 for NPC probability. Threefold cross-validation was performed in 399 patients. CNN scores between the NPC and benign hyperplasia groups were compared using Student's t test. Receiver operating characteristic with the area under the curve (AUC) was performed to identify the optimal CNN score threshold. RESULTS: In each fold, significant differences were observed in the CNN scores between the NPC and benign hyperplasia groups (p < .01). The AUCs ranged from 0.95 to 0.97 with no significant differences between the folds (p = .35 to .92). The combined AUC from all three folds (n = 399) was 0.96, with an optimal CNN score threshold of > 0.71, producing a sensitivity, specificity, and accuracy of 92.4%, 90.6%, and 91.5%, respectively, for NPC detection. CONCLUSION: Our CNN method applied to T2-weighted MRI could discriminate between malignant and benign tissues in the nasopharynx, suggesting that it as a promising approach for the automated detection of early-stage NPC. KEY POINTS: • The convolutional neural network (CNN)-based algorithm could automatically discriminate between malignant and benign diseases using T2-weighted fat-suppressed MR images. • The CNN-based algorithm had an accuracy of 91.5% with an area under the receiver operator characteristic curve of 0.96 for discriminating early-stage T1 nasopharyngeal carcinoma from benign hyperplasia. • The CNN-based algorithm had a sensitivity of 92.4% and specificity of 90.6% for detecting early-stage nasopharyngeal carcinoma.