Population Survey of Knowledge about Oral Cancer and Related Factors in the Capital of Iran

Intelligent deep learning supports biomedical image detection and classification of oral cancer

BACKGROUND

Oral cancer is a malignant tumor that usually occurs within the tissues of the mouth. This type of cancer mainly includes tumors in the lining of the mouth, tongue, lips, buccal mucosa and gums. Oral cancer is on the rise globally, especially in some specific risk groups. The early stage of oral cancer is usually asymptomatic, while the late stage may present with ulcers, lumps, bleeding, etc.

OBJECTIVE

The objective of this paper is to propose an effective and accurate method for the identification and classification of oral cancer.

METHODS

We applied two deep learning methods, CNN and Transformers. First, we propose a new CANet classification model for oral cancer, which uses attention mechanisms combined with neglected location information to explore the complex combination of attention mechanisms and deep networks, and fully tap the potential of attention mechanisms. Secondly, we design a classification model based on Swim transform. The image is segmented into a series of two-dimensional image blocks, which are then processed by multiple layers of conversion blocks.

RESULTS

The proposed classification model was trained and predicted on Kaggle Oral Cancer Images Dataset, and satisfactory results were obtained. The average accuracy, sensitivity, specificity and F1-Socre of Swin transformer architecture are 94.95%, 95.37%, 95.52% and 94.66%, respectively. The average accuracy, sensitivity, specificity and F1-Score of CANet model were 97.00%, 97.82%, 97.82% and 96.61%, respectively.

CONCLUSIONS

We studied different deep learning algorithms for oral cancer classification, including convolutional neural networks, converters, etc. Our Attention module in CANet leverages the benefits of channel attention to model the relationships between channels while encoding precise location information that captures the long-term dependencies of the network. The model achieves a high classification effect with an accuracy of 97.00%, which can be used in the automatic recognition and classification of oral cancer.

A Comparative Analysis of Dominant Dietary Patterns in Patients with and without Oral Squamous Cell Carcinoma

Background

As a modifiable risk factor, nutrition plays a pivotal role in the prevention or delay of oral squamous cell carcinoma (OSCC). This study was aimed to investigate and compare the dominant dietary patterns in the patients with and without OSCC.

Materials and Methods

This case-control study evaluated the usual dietary intake of 80 cases and 120 controls during 2019-2020 using the 117-item Food Frequency Questionnaire, with confirmed validity and reliability. Factor analysis was used to detect the dominant dietary patterns. Data analysis was done by SPSS (version 21) using the Chi-square test, ANOVA, logistic regression analysis, and independent t-test (P < 0.05).

Results

Three dietary patterns were identified, including the western dietary pattern, health dietary pattern, and traditional dietary pattern. The odds ratio (OR) = 1.181 and confidence interval (CI) = 0.671 and 2.082 were found for the western dietary pattern, OR = 1.087 and CI = 0.617 and 1.914 were detected for the healthy dietary pattern, and OR = 0.846 and CI = 0.480 and 1.491 were reported for the traditional dietary pattern. No significant difference was found between the study groups in the dietary pattern and the risk of disease. This relationship remained insignificant after adjustment for the energy intake and confounding factors.

Conclusion

There was no significant relationship between adherence to healthy, traditional, and western dietary patterns and OSCC. Consumption of vegetables and nuts had a protective role against the disease, but risky behaviors such as smoking and alcohol use were directly associated with the incidence of the disease.

Artificial-Intelligence-Based Decision Making for Oral Potentially Malignant Disorder Diagnosis in Internet of Medical Things Environment

Oral cancer is considered one of the most common cancer types in several counties. Earlier-stage identification is essential for better prognosis, treatment, and survival. To enhance precision medicine, Internet of Medical Things (IoMT) and deep learning (DL) models can be developed for automated oral cancer classification to improve detection rate and decrease cancer-specific mortality. This article focuses on the design of an optimal Inception-Deep Convolution Neural Network for Oral Potentially Malignant Disorder Detection (OIDCNN-OPMDD) technique in the IoMT environment. The presented OIDCNN-OPMDD technique mainly concentrates on identifying and classifying oral cancer by using an IoMT device-based data collection process. In this study, the feature extraction and classification process are performed using the IDCNN model, which integrates the Inception module with DCNN. To enhance the classification performance of the IDCNN model, the moth flame optimization (MFO) technique can be employed. The experimental results of the OIDCNN-OPMDD technique are investigated, and the results are inspected under specific measures. The experimental outcome pointed out the enhanced performance of the OIDCNN-OPMDD model over other DL models.

Oral Cancer Awareness and Individuals' Inclination to Its Screening and Risk Prediction in Hong Kong

Assessing the baseline knowledge status and expectations of the target population of any health promotion and secondary prevention program is essential to the success of such intervention. To obtain this information about the Hong Kong population a priori to implementing these preventive strategies for oral cancer in addition to determining the willingness of potential screening participants to take risk-profiling assessments, a cross-sectional survey was conducted between November 2019 and March 2020. A total of 964 residents between the ages 18 and 86 years were invited to participate in this study across the three geographical areas in Hong Kong. Most participants self-reported being aware of oral cancer (86.3%), although the proportion of those with substantial knowledge on salient risk factors and early identifiable signs were very low (2.9%). Age and level of education were the only demographic characteristics associated with the knowledge status. The proportion of participants willing to attend community screening and partake in risk profiling assessment was high (83.9% and 80.9% respectively). Willingness to attend community screening was directly associated with respondents' self-reported oral cancer awareness status (OR: 1.9, 95% CI: 1.22-2.96). Also, we observed that those participants who were willing to attend screening are more inclined to take risk prediction assessments that those not willing to attend. These findings have showcased the need to intensify health promotion via personal skills development to encourage early disease presentation and will assist in the planning of these programs accordingly in the Hong Kong population.

A Snapshot of Knowledge about Oral Cancer in Italy: A 505 Person Survey

Objectives: Patients' knowledge about oral squamous cell carcinoma (OSCC) plays an important role in primary prevention, early diagnosis, and prognosis and survival rate. The aim of this study was to assess OSCC awareness attitudes among general population in order to provide information for educational interventions. Methods: A survey delivered as a web-based questionnaire was submitted to 505 subjects (aged from 18 to 76 years) in Italy, and the answers collected were statistically analyzed. Information was collected about existence, incidence, features of lesions, risk factors of oral cancer, and self-inspection habits, together with details about professional reference figures and preventive behaviors. Results: Chi-square tests of independence with adjusted standardized residuals highlighted correlations between population features (age, gender, educational attainment, provenance, medical relationship, or previous diagnoses of oral cancer in family) and knowledge about oral cancer. Conclusions: Knowledge about OSCC among the Italian population is limited, and it might be advisable to implement nudging and sensitive customized campaigns in order to promote awareness and therefore improve the prognosis of this disease.