Efficacy of different irrigation needles and ultrasonic activation on calcium hydroxide removal: a micro-CT study using 3D-printed endodontic models.

INTRODUCTION: The aim of this study was to evaluate the efficacy of different irrigation needles and passive ultrasonic activation in removing Ca(OH)2 from an endodontic model that duplicated a root canal configuration of a human natural tooth. METHODS: An extracted human maxillary premolar was subjected to root canal preparation and scanned with microcomputed tomography (micro-CT). A 3-dimensional reconstruction model of the natural tooth was printed to endodontic models using a polyjet printer. The root canals of the models were filled with Ca(OH)2 paste and divided into two groups based on the irrigation protocol: conventional syringe-needle irrigation (conventional group) and passive ultrasonic irrigation (PUI group). Each group was subdivided into three groups (n = 10) according to the type of needle: half-cut, side-vented, and TruNatomy irrigation needle. Micro-CT imaging was used to assess the percentage of reduction of Ca(OH)2. Data were analyzed using two-way analysis of variance test (α = .05). RESULTS: The side-vented and TruNatomy irrigation needles showed significantly higher percentage reductions than the half-cut needle (P < .05) in the conventional irrigation group. The PUI group showed significantly higher percentage reductions of Ca(OH)2 than the conventional group regardless of the type of needle (P < .05). However, no significant difference was found among the needles in the PUI group. CONCLUSIONS: The type of irrigation needle and the use of PUI influenced the removal efficacy of Ca(OH)2. PUI enhanced the removal of Ca(OH)2 regardless of the type of irrigation needle.

Classification of presence of missing teeth in each quadrant using deep learning artificial intelligence on panoramic radiographs of pediatric patients.

Early tooth loss in pediatric patients can lead to various complications, making quick and accurate diagnosis essential. This study aimed to develop a novel deep learning model for classification of missing teeth on panoramic radiographs in pediatric patients and to assess the accuracy. The study included patients aged 8-16 years who visited the Pusan National University Dental Hospital and underwent panoramic radiography. A total of 806 panoramic radiographs were retrospectively analyzed to determine the presence or absence of missing teeth for each tooth number. Moreover, each panoramic radiograph was divided into four quadrants, each of a smaller size, containing both primary and permanent teeth, generating 3224 data. Quadrants with missing teeth (n = 1457) were set as the experimental group, and quadrants without missing teeth (n = 1767) were set as the control group. The data were split into training and validation sets in a 4:1 ratio, and a 5-fold cross-validation was conducted. A gradient-weighted class activation map was used to visualize the deep learning model. The average values of sensitivity, specificity, accuracy, precision, recall and F1-score of this deep learning model were 0.635, 0.814, 0.738, 0.730, 0.732 and 0.731, respectively. In the experimental group, the accuracy was the highest for missing canines and premolars, and the lowest for molars. The deep learning model exhibited a moderate to good distinguishing power with a classification performance of 0.730. This deep learning model and the newly defined small sized region of interest proved adequate for classifying the presence of missing teeth.

Radiofrequency ablation for the treatment of great saphenous venous insufficiency using a newly developed VENISTAR catheter: A preliminary study.

OBJECTIVES: This study was aimed to assess the preliminary outcomes of radiofrequency ablation (RFA) using a newly developed catheter (VENISTAR) for the treatment of incompetent great saphenous veins (GSVs). METHODS: In this prospective observational study, endovenous RFA using a VENISTAR catheter was performed on 16 saphenous veins in 12 patients between August and November 2019. Patients' pre- and post-procedural data were recorded. Doppler ultrasound imaging and clinical evaluation were performed at 1 week and 1, 3, and 6 months to determine the efficacy and safety of the treatment. RESULTS: Technical success and complete closure of the targeted GSVs immediately after the procedure were observed in all 16 limbs (100%). However, one patient (one limb) was found to have partial occlusion without significant reflux after 1 week of follow-up. Kaplan-Meier analysis yielded a complete occlusion rate of 93% at 6 months of follow-up. The Venous Clinical Severity Scores at the time of all follow-up were significantly lower than those at baseline (3.3 ± 1.1 at baseline to 0.6 ± 0.6, 0.3 ± 0.6, 0.1 ± 0.4, and 0.2 ± 0.4 at 1 week and 1, 3, and 6 months, respectively) (p < .001). Mild post-procedural pain was noted in 7 and 4 limbs at 1 week and 1 month, respectively. Grade 1 ecchymosis over the ablated segment was noted in 5 (35.7%) of 14 limbs at 1-week follow-up. CONCLUSIONS: Endovenous treatment of GSV insufficiency using a new VENISTAR radiofrequency catheter has been shown to be feasible, effective, and safe throughout the 6-month follow-up.

Clinical Outcomes of Arteriovenous Grafts Using the Superficial Vein versus Venae Comitantes as Venous Outflow.

Background: The superficial veins are commonly used in conventional autogenous arteriovenous fistulas and the placement of prosthetic grafts. When they are unsuitable, however, the use of the deep veins (venae comitantes) is generally considered to be a reasonable alternative. This study conducted a comparative analysis of clinical outcomes for arteriovenous grafts between 2 groups based on the type of venous outflow: superficial veins or venae comitantes. Methods: In total, 151 patients who underwent arteriovenous grafts from November 2005 to March 2022 were retrospectively analyzed. The patients were divided into 2 groups: group A (superficial veins, n=89) and group B (venae comitantes, n=62). The primary, secondary patency, and complication rates were analyzed in each group. A propensity score-matched analysis was performed. Results: In total, 55 well-balanced pairs were matched. Kaplan-Meier analysis revealed no significant differences in the primary patency rate between the 2 groups at 1-year, 3-year and 5-year intervals (group A, 54.7%, 35.9%, 25.4% vs. group B, 47.9%, 16.8%, 12.6%; p=0.14), but there was a difference in the secondary patency rate (group A, 98.2%, 95.3%, 86.5% vs. group B, 87.3%, 76.8%, 67.6%; p=0.0095). The rates of complications, simple percutaneous transluminal angioplasty, and stent insertion were comparable between the groups. Conclusion: Although this study demonstrated not particularly favorable secondary patency rates in the venae comitantes group, the venae comitantes may still be a viable option for patients with unsuitable superficial veins because there were no significant differences in the primary patency and complication rates between the 2 groups.

Endovenous radiofrequency ablation using a new bipolar electrode in a canine model: a new endovenous radiofrequency electrode.

Purpose: This study aimed to determine the effectiveness and safety of a newly developed endovenous radiofrequency (RF) catheter compared with that of the existing RF catheter in a canine model. Methods: Seven dogs underwent ablation using 1 control catheter (ClosureFAST, CF; Covidien) and 1 experimental catheter (VENISTAR, VS; STARmed Co., Ltd.) in the femoral and cephalic veins. The ablated vein was evaluated macroscopically (2,3,5-triphenyltetrazolium chloride staining, TTC), microscopically (hematoxylin and eosin staining), and ultrasonographically. Vessel injury score was used to evaluate the ablating effect objectively. Veins from 1 dog were evaluated on the day of ablation, while in the remaining 6 dogs, the ablated veins were evaluated 2 weeks later. Results: A total of 23 veins (CF, 11 veins; VS, 12 veins) were ablated in 7 dogs. Non-TTC-stained vein wall areas were identified in all ablated veins. No significant difference was observed in the mean vessel injury score (2.54 ± 1.16 vs. 2.42 ± 1.13, P = 0.656) and the mean vessel wall thickness (0.32 ± 0.03 mm vs. 0.31 ± 0.05 mm, P = 0.212) between CF and VS. There was no blood flow in all veins ablated with VS, whereas there was remaining blood flow in 1 vein ablated with CF. Perivenous complication was not observed. Conclusion: Endovenous RF ablation using a newly developed VS RF catheter seems to provide comparable occlusion rate and degree of vein wall injury without perivenous adverse events compared to the most commonly used RF catheter (CF).

Three-dimensional soft tissue landmark detection with marching cube algorithm.

Current method of analyzing three-dimensional soft tissue data, especially in the frontal view, is subjective and has poor reliability. To overcome this limitation, the present study aimed to introduce a new method of analyzing soft tissue data reconstructed by marching cube algorithm (Program S) and compare it with a commercially available program (Program A). Cone-beam computed tomography images of 42 patients were included. Two orthodontists digitized six landmarks (pronasale, columella, upper and lower lip, right and left cheek) twice using both programs in two-week intervals, and the reliability was compared. Furthermore, computer-calculated point (CC point) was developed to evaluate whether human error could be reduced. The results showed that the intra- and inter-examiner reliability of Program S (99.7-100% and 99.9-100%, respectively) were higher than that of Program A (64.0-99.9% and 76.1-99.9%, respectively). Moreover, the inter-examiner difference of coordinate values and distances for all six landmarks in Program S was lower than Program A. Lastly, CC point was provided as a consistent single point. Therefore, it was validated that this new methodology can increase the intra- and inter-examiner reliability of soft tissue landmark digitation and CC point can be used as a landmark to reduce human error.

Promoter Methylation of Cancer Stem Cell Surface Markers as an Epigenetic Biomarker for Prognosis of Oral Squamous Cell Carcinoma.

Growing evidence suggests that genetic and epigenetic factors, including environmental factors, contribute to the development of oral squamous cell carcinoma (OSCC). Here, we investigated the transcriptional silencing of the CD24, CD44, CD133, and CD147 genes, which are well-known cancer stem cell surface markers in various cancer types, including OSCC. We first examined the correlation between the transcriptional expression level and reactivation by 5-aza-2'-deoxycytidine (5-aza-dC) and the promoter methylation levels of the four genes in several OSCC cell lines. We observed promoter hypermethylation for the CD24, CD133, and CD147 genes at 70%, 75%, and 70%, respectively, in OSCC cell lines compared to normal oral mucosa tissues (<53%), indicating that this methylation pattern is cancer-specific, which was confirmed by bisulfite sequencing analysis. More specifically, the expression and methylation profiles of CD133 and CD147 extracted from The Cancer Genome Atlas (TCGA) database were negatively correlated, supporting their epigenetic regulation in primary OSCC tumors. The methylation status of CD133 and CD147 was associated with poor survival in patients with OSCC using the TCGA database. Our findings provide additional insight into the abnormal DNA methylation of CD133 and that CD147 could be used for the diagnosis and therapeutic treatment of patients with OSCC.

Development of automated segmentation of visceral adipose tissue in computed tomography.

PURPOSE: Imaging modalities such as computed tomography (CT) or magnetic resonance imaging have been used to measure adiposity. However, manual segmentation of visceral adipose tissue (VAT) in the entire abdomen is laborious and time-consuming. We aimed to develop a new method for accurate visceral fat segmentation by automatically dividing the three anatomical compartments of the lung, soft tissue, and post-vertebral spaces. METHODS: To automatically separate visceral fat, a three-step process was performed that sequentially divided tissues and regions in a three-dimensional CT image. Manual segmentation was performed in 99 individuals who underwent 18-fluoro-2-deoxyglucosepositron emission tomography/CT for cancer screening between January 2010 and December 2018 to validate the automated segmentation. The similarity index and Pearson's correlation analysis were performed to compare automated segmentation with manual segmentation. Clinical data, such as weight, height, and glucose and insulin levels, were measured. Pearson's correlation analysis was performed to investigate the association between the two methods. RESULTS: VAT volume of automated segmentation (3,594.6 ± 1,776.5 cm3) strongly correlated with that of manual segmentation (3,375.7 ± 1567.5 cm3) (r = 0.9676, p < 0.0001). The similarity index positively correlated with the VAT volume (r = 0.6396, p < 0.0001) and negatively correlated with the mean Hounsfield units (HU) (r = -0.4328, p < 0.0001). Bland-Altman plots are presented with 5.1 % for VAT volume and 7.1 % for mean HU were outside 1.96 standard deviation from the mean value. CONCLUSION: We developed an automated segmentation method for VAT in the entire abdomen. This automated segmentation method is feasible for measuring the VAT volume and VAT HU. This method could be employed in daily clinical practice to provide more detailed information about VAT.

Deep learning-based identification of mesiodens using automatic maxillary anterior region estimation in panoramic radiography of children.

OBJECTIVES: The purpose of this study is to develop and evaluate the performance of a model that automatically sets a region of interest (ROI) and diagnoses mesiodens in panoramic radiographs of growing children using deep learning technology. METHODS: Out of 988 panoramic radiographs, 489 patients with mesiodens were classified as an experimental group, and 499 patients without mesiodens were classified as a control group. This study consists of two networks. The first network (DeeplabV3plus) is a segmentation model that uses the posterior molar space to set the ROI in the maxillary anterior region with the mesiodens in the panoramic radiograph. The second network (Inception-resnet-v2) is a classification model that uses cropped maxillary anterior teeth to determine the presence of mesiodens. The data were divided into five groups and cross-validated. Deep learning model were created and trained using Inception-ResNet-v2. The performance of the segmentation network was evaluated using accuracy, Intersection over Union (IoU), and MeanBFscore. The overall network performance was evaluated using accuracy, precision, recall, and F1-score. RESULTS: Segmentation performance using posterior molar space in panoramic radiographs was 0.839, IoU 0.762, and MeanBFscore 0.907. The mean values of accuracy, precision, recall, F1-score, and area under the curve for the diagnosis of mesiodens using automatic segmentation were 0.971, 0.971, 0.971, 0.971, and 0.971, respectively. CONCLUSIONS: The diagnostic performance of the deep learning system using posterior molar space on the panoramic radiograph was sufficiently useful. The results of the deep learning system confirmed the possibility of complete automation of the classification of mesiodens.

Aggressive central odontogenic fibroma in the maxilla: A case report.

A central odontogenic fibroma is a rare benign tumor composed of mature fibrous connective tissue with variable amounts of odontogenic epithelium. It appears at similar rates in the maxilla and mandible. In the maxilla, it usually occurs anterior to the molars. Radiographically, central odontogenic fibroma commonly presents as a multilocular or unilocular radiolucency with a distinct border. This paper reports a case of an aggressive central odontogenic fibroma involving the right posterior maxilla of a 53-year-old man. Radiographs showed an extensive soft tissue mass involving the entire right maxilla with frank bone resorption. The patient had a history of 2 operations in the region, both more than 2 decades ago. Although it was impossible to confirm the previous diagnoses, it was presumed that this case was a recurrent lesion.

Analysis of the root position and angulation of maxillary premolars in alveolar bone using cone-beam computed tomography.

Purpose: This study investigated whether the relationship between the maxillary sinus and the root of the maxillary premolar is correlated with the root position and whether there is a difference in the long axis angle of premolars and the buccal bone thickness according to the sinus-root relationship and root position. Materials and Methods: Cone-beam computed tomographic images of 587 maxillary first premolars and 580 second premolars from 303 patients were retrospectively reviewed. The maxillary sinus floor-root relationship was classified into 4 types, and the root position in the alveolar bone was evaluated as buccal, middle, or palatal. The long axis angle of the maxillary premolars in the alveolar bone and the buccal bone thickness were measured. The correlation between these parameters was analyzed. Results: The maxillary sinus floor-root relationship showed a statistically significant correlation with the root position in the alveolar bone. Most maxillary first premolars were buccally located, and more than half of the second premolars had their roots in the middle. The long axis angle of the premolars was significantly larger in buccal-positioned teeth than in middle-positioned teeth, and the buccal bone was thinner. Conclusion: When the root of the maxillary premolar was separated from the sinus floor, the premolar was often located on the buccal side. Most of the maxillary first premolars had a thinner buccal bone and larger inclination than the second premolars. It is recommended to evaluate the root position, sagittal angle and buccal bone thickness using CBCT for implant treatment planning.

DeepCIA: a novel deep-learning model for cancer type identification using class activation map via transcription factor expression.

Deep learning methods are powerful analytical tools for large-scale data analysis. Here, we introduce DeepCIA as a novel diagnostic deep-learning model for cancer type identification using a class activation map via transcription factor expression. Although many deep learning researches attempts have recently been made in relation to cancer diagnosis, there are difficulties in using cancer data due to a large-scale problem. Therefore, From The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) public databases, we selected transcription factor expression profiles of eight cancer types. TCGA included 3496 samples and divided the train and validation sets in an 8:2 ratio. ICGC included 552 samples and was used as a test set for external validation. To compare the performance of 1D-CNN models, we also used SVM and KNN from machine learning. In external validation, 1D-CNN showed a high average accuracy of 98% and was superior to support vector machine (SVM) and k-nearest neighbor (KNN) with a difference in the accuracy of 10-12%. Also, 1D-CNN performed very well in several performance metrics (98.2% Recall, 98.1% Precision, 98.2% F score, 99.8% Specificity, 99.8% AUC, and 99.0% Balanced Accuracy). In each data set evaluation, 1-network, 5-network, and 2-network with high accuracy were selected and visualized through the Class Activation Map. We identified the Cys2Hys2 zinc finger group with the highest distribution across all cancer types. Collectively, DeepCIA can be used as a decision support system for cancer and a classifier for diagnosing unknown primary cancer, while emphasizing its usefulness in cancer diagnosis.

Automated Mesiodens Classification System Using Deep Learning on Panoramic Radiographs of Children.

In this study, we aimed to develop and evaluate the performance of deep-learning models that automatically classify mesiodens in primary or mixed dentition panoramic radiographs. Panoramic radiographs of 550 patients with mesiodens and 550 patients without mesiodens were used. Primary or mixed dentition patients were included. SqueezeNet, ResNet-18, ResNet-101, and Inception-ResNet-V2 were each used to create deep-learning models. The accuracy, precision, recall, and F1 score of ResNet-101 and Inception-ResNet-V2 were higher than 90%. SqueezeNet exhibited relatively inferior results. In addition, we attempted to visualize the models using a class activation map. In images with mesiodens, the deep-learning models focused on the actual locations of the mesiodens in many cases. Deep-learning technologies may help clinicians with insufficient clinical experience in more accurate and faster diagnosis.

Targeted Next-Generation Sequencing Analysis Predicts the Recurrence in Resected Lung Adenocarcinoma Harboring EGFR Mutations.

Targeted NGS, widely applied to identify driver oncogenes in advanced lung adenocarcinoma, may also be applied to resected early stage cancers. We investigated resected EGFR-mutated lung adenocarcinoma mutation profiles to evaluate prognostic impacts. Tissues from 131 patients who had complete resection of stage I-IIIA EGFR-mutated lung adenocarcinoma were analyzed by targeted NGS for 207 cancer-related genes. Recurrence free survival (RFS) was estimated according to genetic alterations using the Kaplan-Meier method and Cox proportional regression analysis. The relapse rate was 25.2% (33/131). Five-year RFS of stages IA, IB, II, and IIIA were 82%, 75%, 35%, and 0%, respectively (p < 0.001). RFS decreased with the number of co-mutations (p = 0.025). Among co-mutations, the CTNNB1 mutation was associated with short RFS in a multivariate analysis (hazard ratio: 5.4, 95% confidence interval: 2.1-14.4; p = 0.001). TP53 mutations were associated with short RFS in stage IB-IIIA (p = 0.01). RFS was shorter with EGFR exon 19 deletion (19-del) than with mutation 21-L858R in stage IB-IIIA tumors (p = 0.008). Among 19-del subtypes, pL747_P753delinS (6/56, 8.9%) had shorter RFS than pE746_A750del (39/56, 69.6%), the most frequent subtype (p = 0.004).

Clinical significance of C-Reactive Protein to Lymphocyte Count Ratio as a prognostic factor for Survival in Non-small Cell Lung Cancer Patients undergoing Curative Surgical Resection.

Purpose: We assessed the clinical feasibility of C-reactive protein to lymphocyte ratio (CLR) as a determinant of survival in patients with non-small cell lung cancer (NSCLC) undergoing curative surgical resection. Methods: A retrospective study was conducted on patients with stage I and II NSCLC undergoing curative resection. Demographic and clinical variables, including CLR, were collected and analyzed. The Cox proportional hazards model was used to calculate hazard ratios for overall survival (OS) and cancer-specific survival (CSS). The Mann-Whitney U test was used to compare differences between two independent groups. Results: The median age of the patients was 69.0 years, and male patients comprised 63.9% of all patients. A total of 164 (75.9%) patients were categorized as having stage I disease and 52 (24.1%) as having stage II disease. Using the multivariate Cox model, age (hazard ratio [HR] 1.08, p<0.001), lymphatic invasion (HR 3.12, p=0.004), stage (HR 5.10, p<0.001), and CLR (HR 1.01, p=0.003) were significant determinants of OS. In addition, age (HR 1.11, p=0.002), lymphatic invasion (HR 3.16, p=0.010), stage (HR 6.89, p<0.001), and CLR (HR 1.05, p=0.002) were significant determinants of CSS. Conclusions: Our findings show that CLR could be a determinant of survival in NSCLC patients undergoing curative surgical resection.

Authentication of differential gene expression in oral squamous cell carcinoma using machine learning applications.

BACKGROUND: Recently, the possibility of tumour classification based on genetic data has been investigated. However, genetic datasets are difficult to handle because of their massive size and complexity of manipulation. In the present study, we examined the diagnostic performance of machine learning applications using imaging-based classifications of oral squamous cell carcinoma (OSCC) gene sets. METHODS: RNA sequencing data from SCC tissues from various sites, including oral, non-oral head and neck, oesophageal, and cervical regions, were downloaded from The Cancer Genome Atlas (TCGA). The feature genes were extracted through a convolutional neural network (CNN) and machine learning, and the performance of each analysis was compared. RESULTS: The ability of the machine learning analysis to classify OSCC tumours was excellent. However, the tool exhibited poorer performance in discriminating histopathologically dissimilar cancers derived from the same type of tissue than in differentiating cancers of the same histopathologic type with different tissue origins, revealing that the differential gene expression pattern is a more important factor than the histopathologic features for differentiating cancer types. CONCLUSION: The CNN-based diagnostic model and the visualisation methods using RNA sequencing data were useful for correctly categorising OSCC. The analysis showed differentially expressed genes in multiwise comparisons of various types of SCCs, such as KCNA10, FOSL2, and PRDM16, and extracted leader genes from pairwise comparisons were FGF20, DLC1, and ZNF705D.

Tomographic similarity scan with a computed modified absolute mandibular midsagittal plane for precise and objective localization of mandibular asymmetry.

The application of 3D imaging is at its cusp in craniofacial diagnosis and treatment planning. However, most applications are limited to simple subjective superimposition-based analysis. As the diagnostic accuracy dictates the precision in operability, we propose a novel method that enables objective clinical decision making for patients with mandibular asymmetry. We analyzed cone-beam computed tomography (CBCT) scans of 34 patients who underwent surgical correction for mandibular asymmetry using a high-throughput computing algorithm. Radiomic segmentation of quantitative features of surface and volume followed by exploration resulted in identification of a computed modified absolute mandibular midsagittal plane (cmAMP). Tomographic similarity scan (ToSS) curves were generated via bilateral equidistant scanning in an antero-posterior direction with cmAMP as the reference. ToSS comprised of a comprehensive similarity index (SI) score curve and a segment-wise volume curve. The SI score was computed using the Sørensen-Dice similarity coefficient ranging from 0 to 1. The volumetric analysis was represented as the non-overlapping volume (NOV) and overlapping volume (OV) for each segment, with two segmentation lines, at the mental foramen anteriorly and the intraoral vertical ramus osteotomy region posteriorly. Statistical analysis showed strong negative correlation between the NOV and SI scores for the anterior, middle, and total mandible (P < 0.001). Additionally, a significant correlation was observed between the change in the SI scores for anterior (P = 0.044) and middle segments (P < 0.001) to the total mandible when comparing the data before and after the surgery. This work demonstrated the potential of incorporating ToSS curves in surgical simulation software to improve precision in the clinical decision-making process.