OSUVA - Selaus tekijän "Mäkitie, Antti A." mukaan
Aineistot 1-16 / 16
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Advanced-stage tongue squamous cell carcinoma : a machine learning model for risk stratification and treatment planning
(Taylor & Francis, 15.02.2023)
articleBackground 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 ... -
An interpretable machine learning prognostic system for risk stratification in oropharyngeal cancer
(Elsevier, 13.10.2022)
articleBackground 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 ... -
Application of artificial intelligence for overall survival risk stratification in oropharyngeal carcinoma : A validation of ProgTOOL
(Elsevier, 23.04.2023)
articleBackground 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 ... -
Artificial Intelligence-Driven Radiomics in Head and Neck Cancer : Current Status and Future Prospects
(Elsevier, 09.05.2024)
articleBackground 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 ... -
Clinical significance of tumor-stroma ratio in head and neck cancer : a systematic review and meta-analysis
(BioMed Central, 30.04.2021)
articleBackground The clinical significance of tumor-stroma ratio (TSR) has been examined in many tumors. Here we systematically reviewed all studies that evaluated TSR in head and neck cancer. Methods Four databases (Scopus, ... -
Collaborative machine learning-guided overall survival prediction of oral squamous cell carcinoma
(Taylor & Francis, 31.12.2024)
articleBackground 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 ... -
Comparison of nomogram with machine learning techniques for prediction of overall survival in patients with tongue cancer
(Elsevier, 01.01.2021)
articleBackground: 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 ... -
Comparison of supervised machine learning classification techniques in prediction of locoregional recurrences in early oral tongue cancer
(Elsevier, 01.04.2020)
articleBackground 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 ... -
Deep Machine Learning for Oral Cancer : From Precise Diagnosis to Precision Medicine
(Frontiers Media, 11.01.2022)
articleOral 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 ... -
Interpretable machine learning model for prediction of overall survival in laryngeal cancer
(Taylor & Francis, 27.01.2024)
articleBackground: The mortality rates of laryngeal squamous cell carcinoma cancer (LSCC) have not significantly decreased in the last decades. Objectives: We primarily aimed to compare the predictive performance of DeepTables ... -
Machine learning application for prediction of locoregional recurrences in early oral tongue cancer: a web-based prognostic tool
(Springer, 17.08.2019)
articleEstimation 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 ... -
Machine learning explainability for survival outcome in head and neck squamous cell carcinoma
(Elsevier, 22.03.2025)
articleBackground. Diagnosis and treatment of head and neck squamous cell carcinoma (HNSCC) induces psychological variables and treatment-related toxicity in patients. The evaluation of outcomes is warranted for effective treatment ... -
Machine learning explainability in nasopharyngeal cancer survival using LIME and SHAP
(Springer Nature, 02.06.2023)
articleNasopharyngeal 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 ... -
Machine learning in oral squamous cell carcinoma : current status, clinical concerns and prospects for future - A systematic review
(Elsevier, 01.05.2021)
articleBackground 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 ... -
Managing Cachexia in Head and Neck Cancer : a Systematic Scoping Review
(Springer, 27.02.2022)
articleIntroduction Patients with head and neck cancer (HNC) are usually confronted with functional changes due to the malignancy itself or its treatment. These factors typically affect important structures involved in speech, ... -
Mitigating Burnout in an Oncological Unit : A Scoping Review
(Frontiers Media, 01.10.2021)
articleObjectives: 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 ...
