Machine Learning Algorithms as a Computer-Assisted Decision Tool for Oral Cancer Prognosis and Management Decisions: A Systematic Review

Chiesa‐Estomba, Carlos Miguel; Graña, Manuel; Medela, Alfonso; Sistiaga-Suárez, Jon Alexander; Lechien, J; Calvo‐Henríquez, Christian; Mayo‐Yáñez, Miguel; Vaira, Luigi Angelo; Grammatica, Alberto; Cammaroto, Giovanni; Ayad, Tareck; Fagan, Johannes J.

doi:10.1159/000520672

Cited by 11 publications

(9 citation statements)

References 57 publications

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“…All the articles included in this review were published in English. Of the 17 included systematic reviews [ 1 , 5 , 7 , 13 – 26 ], 11 were conducted in Europe [ 1 , 5 , 7 , 14 , 15 , 17 – 19 , 21 , 22 , 24 ] while 4 were conducted in Asia [ 16 , 23 , 25 , 26 ] and 2 in the United States [ 13 , 20 ] (Table 2 ). All but one of the included systematic reviews showed high-quality appraisal and low risk of bias (Tables 3 , 4 ).…”

Section: Resultsmentioning

confidence: 99%

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Artificial Intelligence in Head and Neck Cancer: A Systematic Review of Systematic Reviews

Mäkitie

Alabi

et al. 2023

Adv Ther

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Introduction Several studies have emphasized the potential of artificial intelligence (AI) and its subfields, such as machine learning (ML), as emerging and feasible approaches to optimize patient care in oncology. As a result, clinicians and decision-makers are faced with a plethora of reviews regarding the state of the art of applications of AI for head and neck cancer (HNC) management. This article provides an analysis of systematic reviews on the current status, and of the limitations of the application of AI/ML as adjunctive decision-making tools in HNC management. Methods Electronic databases (PubMed, Medline via Ovid, Scopus, and Web of Science) were searched from inception until November 30, 2022. The study selection, searching and screening processes, inclusion, and exclusion criteria followed the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines. A risk of bias assessment was conducted using a tailored and modified version of the Assessment of Systematic Review (AMSTAR-2) tool and quality assessment using the Risk of Bias in Systematic Reviews (ROBIS) guidelines. Results Of the 137 search hits retrieved, 17 fulfilled the inclusion criteria. This analysis of systematic reviews revealed that the application of AI/ML as a decision aid in HNC management can be thematized as follows: (1) detection of precancerous and cancerous lesions within histopathologic slides; (2) prediction of the histopathologic nature of a given lesion from various sources of medical imaging; (3) prognostication; (4) extraction of pathological findings from imaging; and (5) different applications in radiation oncology. In addition, the challenges in implementation of AI/ML models for clinical evaluations include the lack of standardized methodological guidelines for the collection of clinical images, development of these models, reporting of their performance, external validation procedures, and regulatory frameworks. Conclusion At present, there is a paucity of evidence to suggest the adoption of these models in clinical practice due to the aforementioned limitations. Therefore, this manuscript highlights the need for development of standardized guidelines to facilitate the adoption and implementation of these models in the daily clinical practice. In addition, adequately powered, prospective, randomized controlled trials are urgently needed to further assess the potential of AI/ML models in real-world clinical settings for the management of HNC.

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Section: Resultsmentioning

confidence: 99%

“…All but one of the included systematic reviews showed high-quality appraisal and low risk of bias (Tables 3 , 4 ). Seven of the systematic reviews were conducted in the year 2021 [ 1 , 5 , 16 – 20 ], 6 in 2022 [ 21 – 26 ], and the remaining 4 before the year 2021 [ 7 , 13 – 15 ].…”

Section: Resultsmentioning

confidence: 99%

Artificial Intelligence in Head and Neck Cancer: A Systematic Review of Systematic Reviews

Mäkitie

Alabi

et al. 2023

Adv Ther

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“…These limitations included a limited amount of data available, the retrospective collection of data, image quality, the comparison of images taking into consideration that healthy tissue could be present with mucosal alterations, imbalances in the data used to train, the risk of missing data, the lack of studies collaborating with clinical diagnosis and biopsy findings, the task of automation, infrastructure requirements for data storage, building and training AI models, and patient privacy and ethical issues. 3 , 39 …”

Section: Discussionmentioning

confidence: 99%

“…[38] Chiesa-Estomba et al analyzed eight studies and concluded that machine learning (ML) had the potential to considerably advance the field of OC research due to the ability of ML models to constantly learn with additional data. [39] J o u r n a l P r e -p r o o f 9…”

Section: Ai In Oral Cancer Screening and Detectionmentioning

confidence: 99%

Artificial intelligence in early diagnosis and prevention of oral cancer

Hegde

Ajila

Zhu³

et al. 2022

Asia-Pacific Journal of Oncology Nursing

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“…Both the management in the treatment of oral carcinoma patients and the predictions and forecasts could be significantly improved. The prerequisite for this is to improve and democratise the use of algorithms [ 10 ].…”

Section: Future Challenges and Innovative Strategies – Perspectives I...mentioning

confidence: 99%

Best practices and future challenges in the treatment of oral cancer

Kröplin,

Reppenhagen

2023

Innovative Surgical Sciences

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Objectives Oral cancer is among the most common tumour worldwide. Due to the anatomical peculiarities of the head and neck region, the treatment of oral cancer is a major challenge with regard to the preservation of aesthetics and function. The aim of the present study is to analyze currently practiced therapeutic strategies as well as current and future challenges in the therapy of oral cancer. Methods A Pubmed-based selective literature search was performed considering literature predominantly from 2021 to 2022. Search terms were “oral cancer,” “oral cavity cancer,” and “head and neck cancer.” Results Head and neck tumours are the seventh most common cancer worldwide. The suspected diagnosis of oral cancer is often made by outpatient dentists during routine examinations. With the outbreak of the 2020 COVID 19 pandemic, risk behaviour has changed with regard to the development and diagnosis of oral cancer. The gold standard of therapy is surgical resection. The need for adjuvant therapy measures depends on the histopathological TNM stage and other defined risk factors. Recurrences occur frequently and should be evaluated with regard to renewed surgical therapy. Future treatment strategies are aimed at early diagnosis, precision in resection, the use of digital technologies, and aspects of quality assurance. The economic importance in the treatment of oral cancer is currently given little consideration. Conclusions The study presents a selective portfolio of treatment strategies currently practiced in Germany and in many parts of the world. In addition, future challenges in the therapy of oral cancer, in particular squamosa cell carcinoma, are presented.

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Machine Learning Algorithms as a Computer-Assisted Decision Tool for Oral Cancer Prognosis and Management Decisions: A Systematic Review

Cited by 11 publications

References 57 publications

Artificial Intelligence in Head and Neck Cancer: A Systematic Review of Systematic Reviews

Artificial Intelligence in Head and Neck Cancer: A Systematic Review of Systematic Reviews

Artificial intelligence in early diagnosis and prevention of oral cancer

Best practices and future challenges in the treatment of oral cancer

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