Collaborative machine learning-guided overall survival prediction of oral squamous cell carcinoma.

Background: There is a lack of prognosticators of overall survival (OS) for Oral Squamous Cell Carcinoma (OSCC).

Objectives: We examined collaborative machine learning (cML) in estimating the OS of OSCC patients. The prognostic significance of the clinicopathological parameters was examined.

Artificial Intelligence-Driven Radiomics in Head and Neck Cancer: Current Status and Future Prospects.

Background: Radiomics is a rapidly growing field used to leverage medical radiological images by extracting quantitative features. These are supposed to characterize a patient's phenotype, and when combined with artificial intelligence techniques, to improve the accuracy of diagnostic models and clinical outcome prediction.

Objectives: This review aims at examining the application areas of artificial intelligence-based radiomics (AI-based radiomics) for the management of head and neck cancer (HNC).

Interpretable machine learning model for prediction of overall survival in laryngeal cancer.

The mortality rates of laryngeal squamous cell carcinoma cancer (LSCC) have not significantly decreased in the last decades.: We primarily aimed to compare the predictive performance of DeepTables with the state-of-the-art machine learning (ML) algorithms (Voting ensemble, Stack ensemble, and XGBoost) to stratify patients with LSCC into chance of overall survival (OS). In addition, we complemented the developed model by providing interpretability using both global and local model-agnostic techniques.

SCADA securing system using deep learning to prevent cyber infiltration.

Supervisory Control and Data Acquisition (SCADA) systems are computer-based control architectures specifically engineered for the operation of industrial machinery via hardware and software models. These systems are used to project, monitor, and automate the state of the operational network through the utilization of ethernet links, which enable two-way communications. However, as a result of their constant connectivity to the internet and the lack of security frameworks within their internal architecture, they are susceptible to cyber-attacks.

Machine learning explainability in nasopharyngeal cancer survival using LIME and SHAP.

Nasopharyngeal cancer (NPC) has a unique histopathology compared with other head and neck cancers. Individual NPC patients may attain different outcomes. This study aims to build a prognostic system by combining a highly accurate machine learning model (ML) model with explainable artificial intelligence to stratify NPC patients into low and high chance of survival groups.

Application of artificial intelligence for overall survival risk stratification in oropharyngeal carcinoma: A validation of ProgTOOL.

Background: In recent years, there has been a surge in machine learning-based models for diagnosis and prognostication of outcomes in oncology. However, there are concerns relating to the model's reproducibility and generalizability to a separate patient cohort (i.e.

Advanced-stage tongue squamous cell carcinoma: a machine learning model for risk stratification and treatment planning.

Background: A significant number of tongue squamous cell carcinoma (TSCC) patients are diagnosed at late stage.

Objectives: We primarily aimed to develop a machine learning (ML) model based on ensemble ML paradigm to stratify advanced-stage TSCC patients into the likelihood of overall survival (OS) for evidence-based treatment. We compared the survival outcome of patients who received either surgical treatment only (Sx) or surgery combined with postoperative radiotherapy (Sx + RT) or postoperative chemoradiotherapy (Sx + CRT).

Proposed algorithm for smart grid DDoS detection based on deep learning.

The Smart Grid's objective is to increase the electric grid's dependability, security, and efficiency through extensive digital information and control technology deployment. As a result, it is necessary to apply real-time analysis and state estimation-based techniques to ensure efficient controls are implemented correctly. These systems are vulnerable to cyber-attacks, posing significant risks to the Smart Grid's overall availability due to their reliance on communication technology.

An interpretable machine learning prognostic system for risk stratification in oropharyngeal cancer.

Background: The optimal management of oropharyngeal squamous cell carcinoma (OPSCC) includes both surgical and non-surgical, that is, (chemo)radiotherapy treatment options and their combinations. These approaches carry a risk of specific treatment-related side effects. HPV-positive OPSCC has been reported to be more sensitive to (chemo)radiotherapy-based treatment modalities.

Measuring the Usability and Quality of Explanations of a Machine Learning Web-Based Tool for Oral Tongue Cancer Prognostication.

: Machine learning models have been reported to assist in the proper management of cancer through accurate prognostication. Integrating such models as a web-based prognostic tool or calculator may help to improve cancer care and assist clinicians in making oral cancer management-related decisions. However, none of these models have been recommended in daily practices of oral cancer due to concerns related to machine learning methodologies and clinical implementation challenges.

Deep Machine Learning for Oral Cancer: From Precise Diagnosis to Precision Medicine.

Oral squamous cell carcinoma (OSCC) is one of the most prevalent cancers worldwide and its incidence is on the rise in many populations. The high incidence rate, late diagnosis, and improper treatment planning still form a significant concern. Diagnosis at an early-stage is important for better prognosis, treatment, and survival.

Utilizing Deep Machine Learning for Prognostication of Oral Squamous Cell Carcinoma-A Systematic Review.

The application of deep machine learning, a subfield of artificial intelligence, has become a growing area of interest in predictive medicine in recent years. The deep machine learning approach has been used to analyze imaging and radiomics and to develop models that have the potential to assist the clinicians to make an informed and guided decision that can assist to improve patient outcomes. Improved prognostication of oral squamous cell carcinoma (OSCC) will greatly benefit the clinical management of oral cancer patients.

Mitigating Burnout in an Oncological Unit: A Scoping Review.

The purpose of this study was to provide a scoping review on how to address and mitigate burnout in the profession of clinical oncology. Also, it examines how artificial intelligence (AI) can mitigate burnout in oncology. We searched Ovid Medline, PubMed, Scopus, and Web of Science, for articles that examine how to address burnout in oncology.

Machine learning in oral squamous cell carcinoma: Current status, clinical concerns and prospects for future-A systematic review.

Background: Oral cancer can show heterogenous patterns of behavior. For proper and effective management of oral cancer, early diagnosis and accurate prediction of prognosis are important. To achieve this, artificial intelligence (AI) or its subfield, machine learning, has been touted for its potential to revolutionize cancer management through improved diagnostic precision and prediction of outcomes.

Comparison of nomogram with machine learning techniques for prediction of overall survival in patients with tongue cancer.

Background: The prediction of overall survival in tongue cancer is important for planning of personalized care and patient counselling.

Objectives: This study compares the performance of a nomogram with a machine learning model to predict overall survival in tongue cancer. The nomogram and machine learning model were built using a large data set from the Surveillance, Epidemiology, and End Results (SEER) program database.

Comparison of supervised machine learning classification techniques in prediction of locoregional recurrences in early oral tongue cancer.

Background: The proper estimate of the risk of recurrences in early-stage oral tongue squamous cell carcinoma (OTSCC) is mandatory for individual treatment-decision making. However, this remains a challenge even for experienced multidisciplinary centers.

Objectives: We compared the performance of four machine learning (ML) algorithms for predicting the risk of locoregional recurrences in patients with OTSCC.

Cancer Modeling-on-a-Chip with Future Artificial Intelligence Integration.

Cancer is one of the leading causes of death worldwide, despite the large efforts to improve the understanding of cancer biology and development of treatments. The attempts to improve cancer treatment are limited by the complexity of the local milieu in which cancer cells exist. The tumor microenvironment (TME) consists of a diverse population of tumor cells and stromal cells with immune constituents, microvasculature, extracellular matrix components, and gradients of oxygen, nutrients, and growth factors.

Machine learning application for prediction of locoregional recurrences in early oral tongue cancer: a Web-based prognostic tool.

Estimation of risk of recurrence in early-stage oral tongue squamous cell carcinoma (OTSCC) remains a challenge in the field of head and neck oncology. We examined the use of artificial neural networks (ANNs) to predict recurrences in early-stage OTSCC. A Web-based tool available for public use was also developed.