Seasonality of head and neck cancers

Jabbour, Nicolette; Mur, Taha; Tracy, Jeremiah C.; Tracy, Lauren F.

doi:10.1016/j.amjoto.2022.103745

Cited by 3 publications

(3 citation statements)

References 19 publications

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“…In a study by Jabbour et al, the authors investigated whether the season of diagnosis affected the characteristics of HNCs. 3 The study reported that HNC diagnosis did not show any seasonal pattern, but oral and salivary gland carcinomas displayed a seasonal pattern.…”

Section: Correlation Between Oral Cancer Diagnosis and Seasonal Patte...mentioning

confidence: 90%

“…5 Jabbour et al attempted to relate this seasonality pattern with HNCs. 3 It was observed that HNCs were mostly diagnosed in spring followed by summer. The study reported that OSCC was significantly diagnosed in spring and this cancer depicted a seasonal pattern of diagnosis.…”

Section: Discussionmentioning

confidence: 99%

“…Jabbour et al found oral cancer diagnosis associated with the season of spring in America while this study found an association with monsoon season in India. 3 The two distinct parts of the world with varying features might have affected the season of diagnosis. Future studies are required and needed to be conducted in various parts of the world to deduce the seasonality pattern of oral cancer diagnosis.…”

Section: Discussionmentioning

confidence: 99%

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Correlation between oral cancer diagnosis and seasonal patterns: An institutional experience

et al. 2023

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Section: Correlation Between Oral Cancer Diagnosis and Seasonal Patte...mentioning

confidence: 90%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Correlation between oral cancer diagnosis and seasonal patterns: An institutional experience

et al. 2023

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Unveiling Cancer: A Data‐Driven Approach for Early Identification and Prediction Using F‐RUS‐RF Model

Javeed,

Anderberg,

Saleem

et al. 2024

Int J Imaging Syst Tech

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Globally, cancer is the second‐leading cause of death after cardiovascular disease. To improve survival rates, risk factors and cancer predictors must be identified early. From the literature, researchers have developed several kinds of machine learning‐based diagnostic systems for early cancer prediction. This study presented a diagnostic system that can identify the risk factors linked to the onset of cancer in order to anticipate cancer early. The newly constructed diagnostic system consists of two modules: the first module relies on a statistical F‐score method to rank the variables in the dataset, and the second module deploys the random forest (RF) model for classification. Using a genetic algorithm, the hyperparameters of the RF model were optimized for improved accuracy. A dataset including 10 765 samples with 74 variables per sample was gathered from the Swedish National Study on Aging and Care (SNAC). The acquired dataset has a bias issue due to the extreme imbalance between the classes. In order to address this issue and prevent bias in the newly constructed model, we balanced the classes using a random undersampling strategy. The model's components are integrated into a single unit called F‐RUS‐RF. With a sensitivity of 92.25% and a specificity of 85.14%, the F‐RUS‐RF model achieved the highest accuracy of 86.15%, utilizing only six highly ranked variables according to the statistical F‐score approach. We can lower the incidence of cancer in the aging population by addressing the risk factors for cancer that the F‐RUS‐RF model found.

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Internal Audit of an Oral Pathology Laboratory: Perspectives on Finances and Operational Management

Ramalingam,

Ramani

et al. 2024

Cureus

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Background Internal audits are essential tools for enhancing the operational efficiency, quality, and effectiveness of healthcare departments. Audits enable the departments and laboratories to meet the changing needs of the healthcare environment by giving a detailed picture of the department's operations and highlighting areas for possible growth and development. Aims and objectives This study focuses on the biopsies received in the Oral Pathology Department at Saveetha Dental College, aiming to evaluate biopsy trends, financial performance, and resource utilization over one year. Materials and methods The oral pathology department audit covered the period from 1st April 2023 to 31st March 2024. The institutional human ethical committee and scientific review board approved the retrospective audit. It involved a comprehensive analysis of biopsy data, financial records, and material usage. Data on different biopsy types (excisional, incisional, frozen sections), immunohistochemistry, cytology, and special stains were collected and analyzed across four quarters. Financial performance was assessed by comparing total income and expenses, while resource utilization was examined through the use of histopathological blocks and other consumables. Statistical analysis (chi-square) was performed using IBM SPSS Statistics for Windows, Version 23 (Released 2015; IBM Corp., Armonk, New York, United States). A P-value less than 0.05 was considered statistically significant. Results We received 1100 cases during the study period. Excisional biopsies were the most common, with 474 (43.09%) cases, followed by incisional biopsies with a total of 432 (39.27%). Out of total cases of 1100, the second quarter (July-September 2023) had the highest case volume of 305 (27.72%), while the third quarter (October-December 2023) recorded the lowest of 250 (22.72%) cases. A financial audit revealed an annual deficit of ₹1,03,321 primarily due to higher expenses towards laboratory reagents. The overall expense incurred per case was ₹448.5. Tissue blocks cost ₹85.23 per case (19.00%) of the average cost per case. The chi-square test analysis was insignificant among the different types of biopsies and the reagent consumption across the four quarters. Conclusion The audit identified critical areas for improvement in both clinical workload and financial management. High volumes of biopsies, but net financial deficits highlight the need for better cost management and resource utilization strategies to maintain sustainability without compromising diagnostic quality.

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Seasonality of head and neck cancers

Cited by 3 publications

References 19 publications

Correlation between oral cancer diagnosis and seasonal patterns: An institutional experience

Correlation between oral cancer diagnosis and seasonal patterns: An institutional experience

Unveiling Cancer: A Data‐Driven Approach for Early Identification and Prediction Using F‐RUS‐RF Model

Internal Audit of an Oral Pathology Laboratory: Perspectives on Finances and Operational Management

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