The accuracy of intraoral scanners is a prerequisite for the fabrication of dental restorations in computer-aided design and computer-aided manufacturing (CAD-CAM) dentistry. While the precision of intraoral scanners has been investigated invitro, clinical data on the accuracy of intraoral scanning (IOS) are limited. The purpose of this clinical study was to determine the accuracy of intraoral scanning with different devices compared with extraoral scanning. An experimental appliance was fabricated for 11 participants and then scanned intraorally and extraorally with 3 different intraoral scanners and a reference scanner. Intraoral and extraoral scans were subdivided into complete-arch and short-span scans and compared with the reference scan to assess trueness. Repeated scans in each group were assessed for precision. Precision and trueness were higher for extraoral scans compared with intraoral scans, except for complete-arch scans with 1 intraoral scanner. The median precision of short-span scans was higher (extraoral 22 to 29 μm, intraoral 23 to 43 μm) compared with complete-arch scans (extraoral 81 to 165 μm, intraoral 80 to 198 μm). The median trueness of short-span scans (extraoral 28 to 40 μm, intraoral 38 to 47 μm) was higher than that of complete-arch scans (extraoral 118 to 581 μm, intraoral 147 to 433 μm) for intraoral and extraoral scanning. Intraoral conditions negatively influenced the accuracy of the scanning devices, which was also reduced for the complete-arch scans. Intraoral conditions negatively influenced the accuracy of the scanning devices, which was also reduced for the complete-arch scans. Monolithic zirconia restorations have been evaluated with invitro studies, but limited clinical evidence of their longevity and reliability is available. The purpose of this clinical study was to evaluate the clinical performance of posterior multiunit glazed monolithic zirconia fixed dental prostheses. A total of 20 participants received 33 monolithic posterior zirconia fixed dental prostheses (Zolid white; Amann Girrbach AG) with minimally invasive preparations. Bilaterally supported fixed dental prostheses with a connector area of at least 9 mm were luted with resin-modified glass ionomer cement. The clinical evaluations were performed after 1 week, 6 months, and then annually after completion of the treatment. The biologic outcomes were evaluated by assessing the pocket depth, attachment level, plaque control, bleeding on probing, caries, and tooth vitality. Esthetics and the functional performance of the prostheses (color match, cavosurface marginal discoloration, anatomic form, marginal adaptatical use for the replacement of missing posterior teeth. Monolithic zirconia restorations demonstrated a reliable treatment option after medium-term clinical use for the replacement of missing posterior teeth.Imaging plays a key role in oncology, including the diagnosis and detection of cancer, determining clinical management, assessing treatment response, and complications of treatment or disease. The current use of clinical oncology is predominantly qualitative in nature with some relatively crude size-based measurements of tumours for assessment of disease progression or treatment response; however, it is increasingly understood that there may be significantly more information about oncological disease that can be obtained from imaging that is not currently utilized. Artificial intelligence (AI) has the potential to harness quantitative techniques to improve oncological imaging. These may include improving the efficiency or accuracy of traditional roles of imaging such as diagnosis or detection. These may also include new roles for imaging such as risk-stratifying patients for different types of therapy or determining biological tumour subtypes. This review article outlines several major areas in oncological imaging where there may be opportunities for AI technology. These include (1) screening and detection of cancer, (2) diagnosis and risk stratification, (3) tumour segmentation, (4) precision oncology, and (5) predicting prognosis and assessing treatment response. This review will also address some of the potential barriers to AI research in oncological imaging. To calculate the quantitative liver-portal vein contrast ratio (Q-LPC) cut-off value based on tumour detectability by using receiver operating characteristic (ROC) curves. Seventy-four patients with tumours (46 men and 28 women; age, 71±8.1 years), who underwent liver magnetic resonance imaging (MRI) using gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid (Gd-EOB-DTPA) were enrolled. https://www.selleckchem.com/products/cmc-na.html Some patients were found to have multiple tumours. In total, 102 tumour images were evaluated for quantitative liver-spleen contrast ratio (Q-LSC) and Q-LPC 10 minutes after the administration of Gd-EOB-DTPA. Q-LPC and Q-LSC were compared to assess the cut-off values and usefulness. The ROC curve was evaluated using the method for continuously distributed test results, with a free scale of 50 mm. A score of ≥30 out of 50 points was considered good. Cut-off values of Q-LPC and Q-LSC were then calculated. The areas under the ROC curve (AUCs) were also examined and compared. The AUC-ROC for Q-LPC was 0.858 (95% confidence interval [CI], 0.783-0.933). The cut-off value was determined to be at 1.462. Sensitivity was 0.747, and specificity was 0.852 at the cut-off value. The AUC-ROC for Q-LSC was 0.710 (95% CI, 0.597-0.822). The cut-off value was at 1.543, the sensitivity was 0.560, and the specificity was 0.778 at the cut-off value. A significant difference was noted between the AUCs (p=0.0016). Q-LPC can be used for hepatobiliary phase MRI evaluation. Q-LPC can be used for hepatobiliary phase MRI evaluation. To explore the magnetic resonance imaging (MRI) differences between pancreatic neuroendocrine tumour grade 3 (pNET-G3) and pancreatic neuroendocrine carcinoma grade 3 (pNEC-G3). Between 2009 and 2019, 31 patients underwent pNEN-G3 resection with preoperative MRI in two local hospitals in China. The 31 patients were assigned to a pNET-G3 group (n=13) or a pNEC-G3 group (n=18). The MRI findings between the groups were compared. There was no statistically significant difference between the two groups in lesion size, clinical characteristics, or laboratory indexes. The lesions showed high or slightly higher signal on diffusion-weighted imaging and decreased apparent diffusion coefficient (ADC) values, which differed between the two groups (p=0.013). The difference between the groups regarding positive enhancement integral, arterial phase and portal phase signal enhancement ratio were statistically significant; however, the delayed phase signal enhancement ratio was not significantly different. pNET-G3 and pNEC-G3 showed different characteristics on MRI.