Accuracy of artificial intelligence-assisted detection of Oral Squamous Cell Carcinoma: A systematic review and meta-analysis

Elmakaty, Ibrahim; Elmarasi, Mohamed; Amarah, Ahmed; Abdo, Ruba Shakib; Malki, Mohammed Imad

doi:10.1016/j.critrevonc.2022.103777

Cited by 18 publications

(7 citation statements)

References 48 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%

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%

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

Mäkitie

Alabi

et al. 2023

Adv Ther

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“…[464][465][466][467][468] These technological advances may allow for the early detection and management of suspicious lesions. 469,470 Further explorations into the mechanisms of oncogenesis will assist and promote the accuracy of early diagnosis. Through the availability of a vast amount of information from transcriptomics, genomics, proteomics, epigenomics, and metabolomics, highthroughput sequencing technology will enable the development of novel therapeutic approaches for the treatment of OSCC.…”

Section: Therapeutic Interventions For Opmdsmentioning

confidence: 99%

Oral squamous cell carcinomas: state of the field and emerging directions

Tan,

Wang,

et al. 2023

Int J Oral Sci

151

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Oral squamous cell carcinoma (OSCC) develops on the mucosal epithelium of the oral cavity. It accounts for approximately 90% of oral malignancies and impairs appearance, pronunciation, swallowing, and flavor perception. In 2020, 377,713 OSCC cases were reported globally. According to the Global Cancer Observatory (GCO), the incidence of OSCC will rise by approximately 40% by 2040, accompanied by a growth in mortality. Persistent exposure to various risk factors, including tobacco, alcohol, betel quid (BQ), and human papillomavirus (HPV), will lead to the development of oral potentially malignant disorders (OPMDs), which are oral mucosal lesions with an increased risk of developing into OSCC. Complex and multifactorial, the oncogenesis process involves genetic alteration, epigenetic modification, and a dysregulated tumor microenvironment. Although various therapeutic interventions, such as chemotherapy, radiation, immunotherapy, and nanomedicine, have been proposed to prevent or treat OSCC and OPMDs, understanding the mechanism of malignancies will facilitate the identification of therapeutic and prognostic factors, thereby improving the efficacy of treatment for OSCC patients. This review summarizes the mechanisms involved in OSCC. Moreover, the current therapeutic interventions and prognostic methods for OSCC and OPMDs are discussed to facilitate comprehension and provide several prospective outlooks for the fields.

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“…Oral cancer imposes a heavy illness on people worldwide. According to the most recent predictions from the (GLOBCAN), Global Cancer Observatory, there were 377,713 new cases and 177,757 fatalities from oral cavity and lip cancers in 2020, making it the sixteenth most frequent cancer globally (Sung et al, 2021) [1]. Patients who received segmental or marginal mandibulectomy as the major surgical procedure between 2006 and 2015 and who had the floor of the mouth, lower alveolus, or retromolar trigone SCC met the inclusion criteria for this research.…”

Section: Introductionmentioning

confidence: 99%

Oral Cancer Detection in its Nascent Stages Using 3D CNN: A Survey

Sagari¹,

Solanki²,

Goel³

et al. 2023

IJSREM

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Abstract—oral cavity cancer also called squamous cell carcinoma is the sixth most frequent cancer overall, it is a rising public health concern that is spreading across the globe. If not detected in earlier stages, it might be severe generally, and underlying lesions induce it. Therefore, it might be difficult to locate the lesion area and if not cured in its nascent stages, it might reach stage IV cancer, called the metastatic stage. To come up with a solution, we tend to use the three- dimensional CNN processes malignancies with image data and 3DCNN structures to classify benign and aggressive oral tumors. By identifying the kind of tumor region susing various kinds of knowledge here on periodic alterations in tumor enhancers, CNN was utilized in Test with visual categorization improvement rates. Analyzed, categorized, and future study areas are identified. The datasets utilized in the review articles are covered in detail, as are the pre- processing methods, the deep learning frameworks employed, etc. It presents a comparison of the same. Index Terms—Oral Cancer, Oral lesions, Oral Cavity, 3D CNN, Pre-processing, tumors, malignant, deep-learning

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Accuracy of artificial intelligence-assisted detection of Oral Squamous Cell Carcinoma: A systematic review and meta-analysis

Cited by 18 publications

References 48 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

Oral squamous cell carcinomas: state of the field and emerging directions

Oral Cancer Detection in its Nascent Stages Using 3D CNN: A Survey

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