Oral Cancer: Integration of Studies for Diagnostic and Therapeutic Precision

Precision Medicine in Oral Health and Diseases: A Systematic Review

Precision medicine (PM) is personalized medicine that can develop targeted medical therapies for the individual patient, in which "omics" sciences lead to an integration of data that leads to highly predictive models of the functioning of the individual biological system. They enable rapid diagnosis, assessment of disease dynamics, identification of targeted treatment protocols, and reduction of costs and psychological stress. "Precision dentistry" (DP) is one promising application that need further investigation; the purpose of this paper is therefore to give physicians an overview of the knowledge they need to enhance treatment planning and patient response to therapy. A systematic literature review was conducted on the PubMed, Scopus, and Web of Science databases by analyzing the articles examining the role of precision medicine in dentistry. PM aims to shed light on cancer prevention strategies, by identifying risk factors, and on malformations such as orofacial cleft. Another application is pain management by repurposing drugs created for other diseases to target biochemical mechanisms. The significant heritability of traits regulating bacterial colonization and local inflammatory responses is another result of genomic research, and is useful for DP in the field of caries and periodontitis. This approach may also be useful in the field of orthodontics and regenerative dentistry. The possibility of creating an international network of databases will lead to the diagnosis, prediction, and prevention of disease outbreaks, providing significant economic savings for the world's health care systems.

The 3D's of Neural Phenotypes in Oral Cancer: Distance, Diameter, and Density

Squamous cell carcinoma of the oral cavity (OSCC) is the most common type of head and neck cancer; survival is poor, and response to treatment varies. Metastasis or recurrence in the regional lymph nodes is associated with poor survival. Consequently, overt or occult spread to the lymph nodes is used to identify patients who will receive adjuvant radiation therapy. Perineural invasion and the diameter of nerves exhibiting perineural invasion have also been suggested to be of prognostic significance. The explosion of interest in cancer neuroscience in the last two decades has led to novel biological insights into interactions between nerves and tumor cells. However, the criteria for defining perineural invasion have lagged behind current knowledge. It is important to re-evaluate the concept of perineural invasion and identify other neural phenotypes in OSCC that can impact treatment selection and prognosis. In addition to perineural invasion, neural phenotypes that are of potential relevance to tumor progression include nerve-tumor distance, nerve diameter, and nerve density. This manuscript discusses the translational significance of recent mechanistic studies on the progression of oral cancer.

Personalized Oral and Dental Care

Recent advances in genomics, data analytics technologies, and biotechnology have been unprecedented, ushering in a new era of healthcare in which interventions are increasingly tailored to individual patients [...].

Nanostructured lipid carrier-loaded metformin hydrochloride: Design, optimization, characterization, assessment of cytotoxicity and ROS evaluation

Metformin hydrochloride (MET) is commonly used in diabetes treatment. Recently, it has gained interest for its anticancer potential against a wide range of cancers. Owing to its hydrophilic nature, the delivery and clinical actions of MET are limited. Therefore, the present work aims to develop MET-encapsulated NLCs using the hot-melt emulsification and probe-sonication method. The optimization was accomplished by 3³ BB design wherein lipid ratio, surfactant concentration, and sonication time were independent variables while the PS (nm), PDI, and EE (%) were dependent variables. The PS, PDI, % EE and ZP of optimized _GMSMET-NLCs were found to be 114.9 ± 1.32 nm, 0.268 ± 0.04 %, 60.10 ± 2.23 %, and ZP - 15.76 mV, respectively. The morphological features, DSC and PXRD, and FTIR analyses suggested the confirmation of formation of the NLCs. Besides, optimized _GMSMET-NLCs showed up to 88 % MET release in 24 h. Moreover, _GMSMET-NLCs showed significant cell cytotoxicity against KB oral cancer cells compared with MET solution as shown by the reduction of IC₅₀ values. Additionally, _GMSMET-NLCs displayed significantly increased intracellular ROS levels suggesting the _GMSMET-NLCs induced cell death in KB cells. _GMSMET-NLCs can therefore be explored to deliver MET through different routes of administration for the effective treatment of oral cancer.

Comparison of machine learning algorithms for the prediction of five-year survival in oral squamous cell carcinoma

BACKGROUND/AIM

Machine learning analyses of cancer outcomes for oral cancer remain sparse compared to other types of cancer like breast or lung. The purpose of the present study was to compare the performance of machine learning algorithms in the prediction of global, recurrence-free five-year survival in oral cancer patients based on clinical and histopathological data.

METHODS

Data were gathered retrospectively from 416 patients with oral squamous cell carcinoma. The data set was divided into training and test data set (75:25 split). Training performance of five machine learning algorithms (Logistic regression, K-nearest neighbours, Naïve Bayes, Decision tree and Random forest classifiers) for prediction was assessed by k-fold cross-validation. Variables used in the machine learning models were age, sex, pain symptoms, grade of lesion, lymphovascular invasion, extracapsular extension, perineural invasion, bone invasion and type of treatment. Variable importance was assessed and model performance on the testing data was assessed using receiver operating characteristic curves, accuracy, sensitivity, specificity and F1 score.

RESULTS

The best performing model was the Decision tree classifier, followed by the Logistic Regression model (accuracy 76% and 60%, respectively). The Naïve Bayes model did not display any predictive value with 0% specificity.

CONCLUSIONS

Machine learning presents a promising and accessible toolset for improving prediction of oral cancer outcomes. Our findings add to a growing body of evidence that Decision tree models are useful in models in predicting OSCC outcomes. We would advise that future similar studies explore a variety of machine learning models including Logistic regression to help evaluate model performance.

Therapeutic applications of herbal/synthetic/bio-drug in oral cancer: An update

Oral cancer, as one of the most prevalent and invasive cancers that invade local tissue, can cause metastasis, and have high mortality. In 2018, around 355,000 worldwide oral cancers occurred and resulted in 177,000 deaths. Estimates for the year 2020 include about 53,260 new cases added to previous year's cases, and the estimated death toll from this cancer in 2020 is about 10,750 deaths more than previous years. Despite recent advances in cancer diagnosis and treatment, unfortunately, 50% of people with cancer cannot be cured. Of course, it should be remembered that the type of treatment used greatly influences patient recovery. There are not many choices when it comes to treating oral cancer. Research efforts focusing on the discovery and evolution of innovative therapeutic approaches for oral cancer are essential. Such traditional methods of treating this type of cancer like surgery and chemotherapy, have evolved dramatically during the past thirty to forty years, but they continue to cause panic among patients due to their side effects. Therefore, it is necessary to study and use drugs that are less risky for the patient as well as to provide solutions to reduce chemotherapy-induced adverse events that prevent many therapeutic risks. As mentioned above, this study examines low-risk therapies such as herbal remedies, biological drugs, and synthetic drugs in the hope that they will be useful to physicians, researchers, and scientists around the world.

Oral Cancer: A Historical Review

Aim: This historical medical literature review aims at understanding the evolution of the medical existence of oral cancer over times, particularly better comprehending if the apparent lower prevalence of this type of cancer in antiquity is a real value due to the absence of modern environmental and lifestyle factors or it is linked to a misinterpretation of ancient foreign terms found in ancient medical texts regarding oral neoplasms. Methods: The databases MedLne, PubMed, Web of Science, Elsevier's EMBASE.com, Cochrane Review, National Library of Greece (Stavros Niarchos Foundation, Athens) and the Library of the School of Health Sciences of the National and Kapodistrian University of Athens (Greece) were extensively searched for relevant studies published during the past century on the history of oral cancer and its treatment from antiquity to modern times, in addition to the WHO website to analyse the latest epidemiological data. In addition, we included historical books on the topic of interest and original sources. Results: Historical references reveal that the cradle of the oral oncology was in ancient Egypt, the Asian continent and Greece and cancer management was confined to an approximate surgical practice, in order to remove abnormal masses and avoid bleeding with cauterization. In the Medieval Age, little progress occurred in medicine in general, oral cancers management included. It is only from the Renaissance to modern times that knowledge about its pathophysiological mechanisms and histopathology and its surgical and pharmacological treatment approaches became increasingly deep all over the world, evolving to the actual integrated treatment. Despite the abundant literature exploring oncology in past civilizations, the real prevalence of oral cancer in antiquity is much less known; but a literature analysis cannot exclude a consistent prevalence of this cancer in past populations, probably with a likely lower incidence than today, because many descriptions of its aggressiveness were found in ancient medical texts, but it is still difficult to be sure that each single description of oral masses could be associated to cancer, particularly for what concerns the period before the Middle Ages. Conclusions: Modern oncologists and oral surgeons must learn a lot from their historic counterparts in order to avoid past unsuccessful efforts to treatment oral malignancies. Several descriptions of oral cancers in the antiquity that we found let us think that this disease might be linked to mechanisms not strictly dependent on environmental risk factors, and this might guide future research on oral cavity treatments towards strategical cellular and molecular techniques.