Background: Oral cancer, which includes cancers of the lips, tongue, mouth, throat, and other oral tissues, is a serious health concern globally. It is one of the major causes of cancer-related mortality because of several factors, including the severity of certain oral malignancies and their late-stage detection. Objective: To comprehensively investigate recently developed technologies for detecting oral cancer and evaluate their accuracy, reliability, and potential application in both therapeutic and preventive contexts. Methods: A thorough literature search was performed using the PubMed, Scopus, and Web of Science databases, focusing on works published between 2014 and 2024. This review evaluates various methods for diagnosing oral cancer, including advanced imaging techniques (MRI and CT scans), biomarker testing, molecular diagnostics, noninvasive salivary diagnostics, optical coherence tomography (OCT), and the application of artificial intelligence (AI) and machine learning (ML) to enhance diagnostic accuracy. Results: All relevant studies meeting the inclusion criteria were analyzed. Several important findings regarding confocal laser scanning microscopy (CLSM) and OCT demonstrated high sensitivity and specificity in identifying oral cancer. This systematic review also highlights the promise of fluorescence spectroscopy, salivary biomarkers, genetic markers, and AI/ML technologies in early disease detection and monitoring. Conclusion: New diagnostic procedures outperform traditional ones in accuracy and reliability in the detection of oral cancer. These innovations enable earlier diagnosis, facilitate targeted therapies, and support personalized treatment strategies. As preventive and monitoring strategies evolve, treatment efficacy improves, and patient trust and engagement increase, ultimately leading to better outcomes and enhanced quality of life for patients.
Newer Diagnostic Methods to Detect Oral Cancer and Their Applications in Prevention and Treatment Strategies: A systematic review of systematic reviews
Marrapodi, Maria Maddalena;Minervini, Giuseppe
2025
Abstract
Background: Oral cancer, which includes cancers of the lips, tongue, mouth, throat, and other oral tissues, is a serious health concern globally. It is one of the major causes of cancer-related mortality because of several factors, including the severity of certain oral malignancies and their late-stage detection. Objective: To comprehensively investigate recently developed technologies for detecting oral cancer and evaluate their accuracy, reliability, and potential application in both therapeutic and preventive contexts. Methods: A thorough literature search was performed using the PubMed, Scopus, and Web of Science databases, focusing on works published between 2014 and 2024. This review evaluates various methods for diagnosing oral cancer, including advanced imaging techniques (MRI and CT scans), biomarker testing, molecular diagnostics, noninvasive salivary diagnostics, optical coherence tomography (OCT), and the application of artificial intelligence (AI) and machine learning (ML) to enhance diagnostic accuracy. Results: All relevant studies meeting the inclusion criteria were analyzed. Several important findings regarding confocal laser scanning microscopy (CLSM) and OCT demonstrated high sensitivity and specificity in identifying oral cancer. This systematic review also highlights the promise of fluorescence spectroscopy, salivary biomarkers, genetic markers, and AI/ML technologies in early disease detection and monitoring. Conclusion: New diagnostic procedures outperform traditional ones in accuracy and reliability in the detection of oral cancer. These innovations enable earlier diagnosis, facilitate targeted therapies, and support personalized treatment strategies. As preventive and monitoring strategies evolve, treatment efficacy improves, and patient trust and engagement increase, ultimately leading to better outcomes and enhanced quality of life for patients.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
