Oral Potentially Malignant Disorders and Oral Cavity Cancer

Ojeda, David; Huber, Michaell A.; Kerr, Alexander Ross

doi:10.1016/j.det.2020.05.011

Cited by 18 publications

(27 citation statements)

References 70 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…5,[7][8][9] OLK is among the most common oral potentially malignant disorders (OPMDs), and its rate of malignant transformation ranges from 1-20% depending on the population and the length of followup. 4,99 OSCC represents the most common malignant neoplasm of the oral cavity and comprises 80-90% of head and neck cancers. Over the past two decades, the 5-year survival rate has remained at~50% due to its initially asymptomatic nature, leading to advanced stage diagnosis with few therapeutic options.…”

Section: Microbial Signals In Oral Leukoplakia and Oral Squamous Cell Carcinomamentioning

confidence: 99%

Crosstalk between the oral microbiota, mucosal immunity, and the epithelial barrier regulates oral mucosal disease pathogenesis

et al. 2021

View full text Add to dashboard Cite

Oral mucosal disease (OMD), which is also called soft tissue oral disease, is described as a series of disorders or conditions affecting the mucosa and soft tissue in the oral cavity. Its etiology is unclear, but emerging evidence has implicated the influence of the composition of the oral mucosa and saliva-resident microbiota. In turn, this dysbiosis effects the immune response balance and epithelial barrier function, followed by the occurrence and progression of OMD. In addition, oral microbial dysbiosis is diverse in different types of diseases and different disease progressions, suggesting that key causal pathogens may exist in various oral pathologies. This narrative literature review primarily discusses the most recent findings focusing on how microbial dysbiosis communicates with mucosal adaptive immune cells and the epithelial barrier in the context of five representative OMDs, including oral candidiasis (OC), oral lichen planus (OLP), recurrent aphthous ulcer (RAU), oral leukoplakia (OLK), and oral squamous cell carcinoma (OSCC), to provide new insight into the pathogenetic mechanisms of OMDs.

show abstract

Section: Microbial Signals In Oral Leukoplakia and Oral Squamous Cell Carcinomamentioning

confidence: 99%

Crosstalk between the oral microbiota, mucosal immunity, and the epithelial barrier regulates oral mucosal disease pathogenesis

et al. 2021

View full text Add to dashboard Cite

show abstract

“…The term oral cancer is rather a general term that includes cancers of the tongue, gingiva (maxilla and mandible), palate, buccal mucosa, floor of the mouth and lips. The majority of cancers arising from these sub‐sites are squamous cell carcinoma (SCC) and are preceded by noticeable mucosal lesions known as oral potentially malignant disorders (OPMDs; Ojeda et al., 2020; Rivera, 2015; Warnakulasuriya et al., 2007).…”

Section: Introductionmentioning

confidence: 99%

A novel lightweight deep convolutional neural network for early detection of oral cancer

et al. 2021

View full text Add to dashboard Cite

Objectives To develop a lightweight deep convolutional neural network (CNN) for binary classification of oral lesions into benign and malignant or potentially malignant using standard real‐time clinical images. Methods A small deep CNN, that uses a pretrained EfficientNet‐B0 as a lightweight transfer learning model, was proposed. A data set of 716 clinical images was used to train and test the proposed model. Accuracy, specificity, sensitivity, receiver operating characteristics (ROC) and area under curve (AUC) were used to evaluate performance. Bootstrapping with 120 repetitions was used to calculate arithmetic means and 95% confidence intervals (CIs). Results The proposed CNN model achieved an accuracy of 85.0% (95% CI: 81.0%–90.0%), a specificity of 84.5% (95% CI: 78.9%–91.5%), a sensitivity of 86.7% (95% CI: 80.4%–93.3%) and an AUC of 0.928 (95% CI: 0.88–0.96). Conclusions Deep CNNs can be an effective method to build low‐budget embedded vision devices with limited computation power and memory capacity for diagnosis of oral cancer. Artificial intelligence (AI) can improve the quality and reach of oral cancer screening and early detection.

show abstract

“…Such condition was mostly due to surgeons' false biopsy practices or lack of experiences for early OSCC diagnoses. Besides, sometimes the variety of clinical presentations of OSCC, and possible concurrence of other premalignant oral lesions will also confuse or delay the early clinical diagnosis [22]. It has been widely acknowledged that an early and correct diagnosis is of utmost importance in reducing pretreatment intervals, providing standardized care and reducing mortality [22][23][24].…”

Section: Discussionmentioning

confidence: 99%

“…Besides, sometimes the variety of clinical presentations of OSCC, and possible concurrence of other premalignant oral lesions will also confuse or delay the early clinical diagnosis [22]. It has been widely acknowledged that an early and correct diagnosis is of utmost importance in reducing pretreatment intervals, providing standardized care and reducing mortality [22][23][24]. Mistakes of wrong or delayed OSCC diagnosis can be avoided with heighten awareness, as well as with extended use of novel techniques.…”

Section: Discussionmentioning

confidence: 99%

Salvage surgery for patients with residual/persistent diseases after improper or insufficient treatment of oral squamous cell carcinoma: can we rectify these mistakes?

Liu

Sui

et al. 2021

BMC Cancer

View full text Add to dashboard Cite

Background Patterns of failure after treatment of oral and squamous cell carcinomas (OSCC) are diversified, with recurrences being one of the common causes. A special group of patients are sometimes encountered in the outpatient clinic for improper or insufficient initial treatment with reports of positive margins, implying residual/persistent diseases. The question of whether these patients can be surgically salvaged remain unanswered. Methods A retrospective study was performed between January 2013 and December 2017 for patients with residual or rapid recurrent (within 3 months) OSCCs, who received salvage surgeries in our institution. The patients with residual/persistent OSCCs were those with microscopic or macroscopic positive surgical margins, while those with rapid recurrent OSCCs were those with close or negative margins, but unabated painful symptoms right after treatment. Both clinicopathological and prognostic variables were analyzed. The focus was also directed towards lessons for possible initial mistakes, resulting in these residual/persistent diseases. Results Of 103 patients, 68 (66%) were men, with mean age of 56.3 years. The overall survival reached 60.2%. Regarding the primary OSCC status, most of our patients (n = 75, 72.8%) were diagnosed with ycT2–3 stages. Besides, most patients were found with macroscopic residual diseases (52.4%) before our salvage surgery. The sizes of the residual/persistent OSCCs were generally under 4 cm (87.3%) with minimally residual in 21 (20.4%). Among all the variables, primary T stage (p = 0.003), and residual lesion size (p < 0.001) were significantly associated with the prognosis in multivariate analysis. Though the causes for the initial surgical failure were multifactorial, most were stemmed from poor planning and unstandardized execution. Conclusions Cases with residual/persistent OSCCs were mostly due to mistakes which could have been avoided under well-round treatment plans and careful surgical practice. Salvage surgery for cases with smaller residual/persistent OSCCs is still feasible with acceptable outcomes.

show abstract

Oral Potentially Malignant Disorders and Oral Cavity Cancer

Cited by 18 publications

References 70 publications

Crosstalk between the oral microbiota, mucosal immunity, and the epithelial barrier regulates oral mucosal disease pathogenesis

Crosstalk between the oral microbiota, mucosal immunity, and the epithelial barrier regulates oral mucosal disease pathogenesis

A novel lightweight deep convolutional neural network for early detection of oral cancer

Salvage surgery for patients with residual/persistent diseases after improper or insufficient treatment of oral squamous cell carcinoma: can we rectify these mistakes?

Contact Info

Product

Resources

About