Diffusion-weighted magnetic resonance imaging of the paranasal sinuses: A systematic review

Munhoz, Luciana; Júnior, Reinaldo Abdala; Abdala, Rogério; Arita, Emiko Saito

doi:10.1016/j.oooo.2018.07.004

Cited by 18 publications

(9 citation statements)

References 36 publications

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“…DWI is an imaging method that detects the motion of water molecules and allows calculation with the apparent diffusion coefficient (ADC). DWI, which can determine the cellularity and microstructural properties of tissues, can contribute to the differentiation of tumors[ 8 - 11 ]. When the studies on SGT were reviewed, some studies stated that the ADC values of malignant and benign tumors (BTs) were significantly different[ 12 - 16 ], while in some studies no significant difference was found[ 17 , 18 ], but in most of these studies, it was reported that ADC values were more effective in separating subgroups [pleomorphic adenomas (PMAs), Warthin’s tumors (WTs) and lymphoma].…”

Section: Dwimentioning

confidence: 99%

Advanced magnetic resonance imaging findings in salivary gland tumors

Gökçe

Beyhan

2022

WJR

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Salivary gland tumors (SGTs) make up a small portion (approximately 5%) of all head and neck tumors. Most of them are located in the parotid glands, while they are less frequently located in the submandibular glands, minor salivary glands or sublingual gland. The incidence of malignant or benign tumors (BTs) in the salivary glands varies according to the salivary gland from which they originate. While most of those detected in the parotid gland tend to be benign, the incidence of malignancy increases in other glands. The use of magnetic resonance imaging (MRI) in the diagnosis of SGTs is increasing every day. While conventional sequences provide sufficient data on the presence, localization, extent and number of the tumor, they are insufficient for tumor specification. With the widespread use of advanced techniques such as diffusion-weighted imaging, semi-quantitative and quantitative perfusion MRI, studies and data have been published on the differentiation of malignant or BTs and the specificity of their subtypes. With diffusion MRI, differentiation can be made by utilizing the cellularity and microstructural properties of tumors. For example, SGTs such as high cellular Warthin’s tumor (WT) or lymphoma on diffusion MRI have been reported to have significantly lower apparent diffusion values than other tumors. Contrast agent uptake and wash-out levels of tumors can be detected with semi-quantitative perfusion MRI. For example, it is reported that almost all of the pleomorphic adenomas show an increasing enhancement time intensity curve and do not wash-out. On quantitative perfusion MRI studies using perfusion parameters such as Ktrans, Kep, and Ve, it is reported that WTs can show higher Kep and lower Ve values than other tumors. In this study, the contribution of advanced MRI to the diagnosis and differential diagnosis of SGTs will be reviewed.

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Section: Dwimentioning

confidence: 99%

Advanced magnetic resonance imaging findings in salivary gland tumors

Gökçe

Beyhan

2022

WJR

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“…Wang et al reported a discrimination accuracy of 70.4% and an AUC value of 0.693 using semiquantitative DCE‐MRI parameters 30 . Although diffusion‐weighted imaging and ADC are promising methods, 31 the radiomic nomogram exhibited superior discrimination performance in differentiating benign from malignant sinonasal tumors in terms of the AUC value and accuracy, which indicates that the nomogram has the potential to improve the treatment and prognosis of sinonasal tumors by making full use of intratumor information.…”

Section: Discussionmentioning

confidence: 99%

AnMRI‐Based Radiomic Nomogram for Discrimination Between Malignant and Benign Sinonasal Tumors

Zhang

Wang

Hao

et al. 2020

Magnetic Resonance Imaging

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Background: Preoperative discrimination between malignant and benign sinonasal tumors is important for treatment plan selection. Purpose: To build and validate a radiomic nomogram for preoperative discrimination between malignant and benign sinonasal tumors. Study Type: Retrospective. Population: In all, 197 patients with histopathologically confirmed 84 benign and 113 malignant sinonasal tumors. Field Strength/Sequences: Fast-spin-echo (FSE) T 1-weighted and fat-suppressed FSE T 2-weighted imaging on a 1.5T and 3.0T MRI. Assessment: T 1 and fat-suppressed T 2-weighted images were selected for feature extraction. The least absolute shrinkage selection operator (LASSO) algorithm was applied to establish a radiomic score. Multivariate logistic regression analysis was applied to determine independent risk factors, and the radiomic score was combined to build a radiomic nomogram. The nomogram was assessed in a training dataset (n = 138/3.0T MRI) and tested in a validation dataset (n = 59/1.5T MRI). Statistical Tests: Independent t-test or Wilcoxon's test, chi-square-test, or Fisher's-test, univariate analysis, LASSO, multivariate logistic regression analysis, area under the curve (AUC), Hosmer-Lemeshow test, decision curve, and the Delong test. Results: In the validation dataset, the radiomic nomogram could differentiate benign from malignant sinonasal tumors with an AUC of 0.91. There was no significant difference in AUC between the combined radiomic score and radiomic nomogram (P > 0.05), and the radiomic nomogram showed a relatively higher AUC than the combined radiomic score. There was a significant difference in AUC between each two of the following models (the radiomic nomogram vs. the clinical model, all P < 0.001; the combined radiomic score vs. the clinical model, P = 0.0252 and 0.0035, respectively, in the training and validation datasets). The radiomic nomogram outperformed the radiomic scores and clinical model. Data Conclusion: The radiomic nomogram combining the clinical model and radiomic score is a simple, effective, and reliable method for patient risk stratification. Level of Evidence: 4 Technical Efficacy Stage: 2

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“…On the other hand, Predictive analytics based on artificial neural networks and machine learning algorithms, can be the next step to be deployed in continuation of the real-time systems to prognosticate changes in the temporal trends and the digital epidemiology of phenomena of interest in medicine, dentistry, as well as other subdisciplines of biological and life sciences. The paranasal sinuses are of prime importance for the region of the head (Rudmik & Soler, 2015;Munhoz, Júnior, Abdala, & Arita, 2018). Leonardo da Vinci (1452-1519) made the very first illustration of the maxillary sinus as well as other paranasal sinuses ( Figure 1) (Chai & Song, 2016;Zanon & Marchese-Ragona, 2018).…”

Section: Introductionmentioning

confidence: 99%

Real-time Inferential Analytics Based on Online Databases of Trends: A Breakthrough Within the Discipline of Digital Epidemiology of Dentistry and Oral-Maxillofacial Surgery

Al-Imam¹,

U²,

Kaouche³

et al. 2019

MAS

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Background: Epidemiological sciences have been evolving at an exponential rate paralleled only by the comparable growth within the discipline of data science. Digital epidemiological studies are playing a vital role in medical science analytics for the past few decades. To date, there are no published attempts at deploying the use of real-time analytics in connection with the disciplines of Dentistry or Medicine. Aims and Objectives: We deployed a real-time statistical analysis in connection with topics in Dental Anatomy and Dental Pathology represented by the maxillary sinus, posterior maxillary teeth, related oral pathology. The purpose is to infer the digital epidemiology based on a continuous stream of raw data retrieved from Google Trends database. Materials and Methods: Statistical analysis was carried out via Microsoft Excel 2016 and SPSS version 24. Google Trends database was used to retrieve data for digital epidemiology. Real-time analysis and the statistical inference were based on encoding a programming script using Python high-level programming language. A systematic review of the literature was carried out via PubMed-NCBI, the Cochrane Library, and Elsevier databases. Results: The comprehensive review of the literature, based on specific keywords search, yielded 491813 published studies. These were distributed as 488884 (PubMed-NCBI), 1611 (the Cochrane Library), and 1318 (Elsevier). However, there was no single study attempting real-time analytics. Nevertheless, we succeeded in achieving an automated real-time stream of data accompanied by a statistical inference based on data extrapolated from Google Trends. Conclusion: Real-time analytics are of considerable impact when implemented in biological and life sciences as they will tremendously reduce the required resources for research. Predictive analytics, based on artificial neural networks and machine learning algorithms, can be the next step to be deployed in continuation of the real-time systems to prognosticate changes in the temporal trends and the digital epidemiology of phenomena of interest.

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Diffusion-weighted magnetic resonance imaging of the paranasal sinuses: A systematic review

Cited by 18 publications

References 36 publications

Advanced magnetic resonance imaging findings in salivary gland tumors

Advanced magnetic resonance imaging findings in salivary gland tumors

AnMRI‐Based Radiomic Nomogram for Discrimination Between Malignant and Benign Sinonasal Tumors

Real-time Inferential Analytics Based on Online Databases of Trends: A Breakthrough Within the Discipline of Digital Epidemiology of Dentistry and Oral-Maxillofacial Surgery

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