Comet Assay Assessment of DNA Damage in Buccal Mucosa Cells Exposed to X-Rays via Panoramic Radiography

Yanuaryska, Ryna Dwi

doi:10.14693/jdi.v25i1.1124

Cited by 5 publications

(5 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Comet assay data suggest that a radiography scan results in a significant increase in DNA DSBs in adults. 131 Similar data were obtained from children. 50 To our knowledge, these are the only two studies that reported on this and therefore should be interpreted with caution, given the many observed contradictions from the other assays that are described in this section.…”

Section: Plain Dentomaxillofacial Radiographysupporting

confidence: 56%

Radiobiological risks following dentomaxillofacial imaging: should we be concerned?

Belmans

Salmon

Baselet

et al. 2021

Dentomaxillofacial Radiology

View full text Add to dashboard Cite

Objectives: This review aimed to present studies that prospectively investigated biological effects in patients following diagnostic dentomaxillofacial radiology (DMFR). Methods: Literature was systematically searched to retrieve all studies assessing radiobiological effects of using X-ray imaging in the dentomaxillofacial area, with reference to radiobiological outcomes for other imaging modalities and fields. Results: There is a lot of variability in the reported radiobiological assessment methods and radiation dose measures, making comparisons of radiobiological studies challenging. Most radiological DMFR studies are focusing on genotoxicity and cytotoxicity, data for 2D dentomaxillofacial radiographs, albeit with some methodological weakness biasing the results. For CBCT, available evidence is limited and few studies include comparative data on both adults and children. Conclusions In the future, one will have to strive towards patient-specific measures by considering age, gender and other individual radiation sensitivity-related factors. Ultimately, future radioprotection strategies should build further on the concept of personalized medicine, with patient-specific optimization of the imaging protocol, based on radiobiological variables.

show abstract

Section: Plain Dentomaxillofacial Radiographysupporting

confidence: 56%

Radiobiological risks following dentomaxillofacial imaging: should we be concerned?

Belmans

Salmon

Baselet

et al. 2021

Dentomaxillofacial Radiology

View full text Add to dashboard Cite

show abstract

“…Most imaging techniques in the dental and medical examination have utilized X-ray radiography, which may induce adverse biological changes related to the oxidative reaction after ionizing radiation exposure in living tissues. 34,35 Instead of using ionizing radiation in X-ray radiography, PAI utilizes non-ionizing laser irradiation as an EM source. 15 However, we found that studies in PA image quality are still limited.…”

Section: Discussionmentioning

confidence: 99%

The Influence of Diode Laser Intensity Modulation on Photoacoustic Image Quality for Oral Soft Tissue Imaging

et al. 2020

View full text Add to dashboard Cite

Introduction: Imaging technologies have been developed to assist physicians and dentists in detecting various diseases. Photoacoustic imaging (PAI) is a new technique that shows great applicability to soft tissues. This study aimed to investigate the effect of diode laser intensity modulation on photoacoustic (PA) image quality. Methods: The prototype of the PAI system in this study utilized a non-ionizing 532 nm continuouswave (CW) diode laser illumination. Samples in this study were oral soft tissues of Sprague-Dawley rats fixed in 10% formalin solution. PA images were taken ex vivo by using the PAI system. The laser exposure for oral soft tissue imaging was set in various duty cycles (16%, 24%, 31%, 39%, and 47%). The samples were embedded in paraffin, and PA images were taken from the paraffinembedded tissue blocks in a similar method by using duty cycles of 40%, 45%, 50%, 55%, 60% respectively to reveal the influence of the laser duty cycle on PA image quality. Results: The oral soft tissue is clearly shown as a yellow to red area in PA images, whereas the nonbiological material appears as a blue background. The color of the PA image is determined by the PA intensity. Hence, the PA intensity of oral soft tissue was generally higher than that of the nonbiological material around it. The Kruskal-Wallis test followed by Mann-Whitney post-hoc analysis revealed significant differences (P < 0.05) in the quality of PA images produced by using a 16%-47% duty cycle of laser intensity modulation for direct imaging of oral soft tissue fixed in 10% formalin solution. The PA image quality of paraffin-embedded tissue was higher than that of direct oral soft tissue images, but no significant differences in PA image quality were found between the groups. Conclusion:The PAI system built in this study can image oral soft tissue. The sample preparation and the diode laser intensity modulation may influence the PA image quality for oral soft tissue imaging. Nonetheless, the influence of diode laser intensity modulation is not significant for the PA image quality of paraffin-embedded tissue.

show abstract

“…The inner lining of the cheeks, known as the buccal mucosa, is an area that can develop cancer. Buccal mucosal cancer is a rare and dangerous form of oral cancer that makes up a small percentage of patients diagnosed with mouth cancer [ 97 ]. There is a correlation between local relapse and margin condition for both small and large buccal cancers that are treated only with surgery; however, adjuvant therapy can improve local control in early stage buccal cancers [ 98 ].…”

Section: Region-based Oral Cancermentioning

confidence: 99%

A Current Review of Machine Learning and Deep Learning Models in Oral Cancer Diagnosis: Recent Technologies, Open Challenges, and Future Research Directions

2023

View full text Add to dashboard Cite

Cancer is a problematic global health issue with an extremely high fatality rate throughout the world. The application of various machine learning techniques that have appeared in the field of cancer diagnosis in recent years has provided meaningful insights into efficient and precise treatment decision-making. Due to rapid advancements in sequencing technologies, the detection of cancer based on gene expression data has improved over the years. Different types of cancer affect different parts of the body in different ways. Cancer that affects the mouth, lip, and upper throat is known as oral cancer, which is the sixth most prevalent form of cancer worldwide. India, Bangladesh, China, the United States, and Pakistan are the top five countries with the highest rates of oral cavity disease and lip cancer. The major causes of oral cancer are excessive use of tobacco and cigarette smoking. Many people’s lives can be saved if oral cancer (OC) can be detected early. Early identification and diagnosis could assist doctors in providing better patient care and effective treatment. OC screening may advance with the implementation of artificial intelligence (AI) techniques. AI can provide assistance to the oncology sector by accurately analyzing a large dataset from several imaging modalities. This review deals with the implementation of AI during the early stages of cancer for the proper detection and treatment of OC. Furthermore, performance evaluations of several DL and ML models have been carried out to show that the DL model can overcome the difficult challenges associated with early cancerous lesions in the mouth. For this review, we have followed the rules recommended for the extension of scoping reviews and meta-analyses (PRISMA-ScR). Examining the reference lists for the chosen articles helped us gather more details on the subject. Additionally, we discussed AI’s drawbacks and its potential use in research on oral cancer. There are methods for reducing risk factors, such as reducing the use of tobacco and alcohol, as well as immunization against HPV infection to avoid oral cancer, or to lessen the burden of the disease. Additionally, officious methods for preventing oral diseases include training programs for doctors and patients as well as facilitating early diagnosis via screening high-risk populations for the disease.

show abstract

Comet Assay Assessment of DNA Damage in Buccal Mucosa Cells Exposed to X-Rays via Panoramic Radiography

Cited by 5 publications

References 14 publications

Radiobiological risks following dentomaxillofacial imaging: should we be concerned?

Radiobiological risks following dentomaxillofacial imaging: should we be concerned?

The Influence of Diode Laser Intensity Modulation on Photoacoustic Image Quality for Oral Soft Tissue Imaging

A Current Review of Machine Learning and Deep Learning Models in Oral Cancer Diagnosis: Recent Technologies, Open Challenges, and Future Research Directions

Contact Info

Product

Resources

About