Newer Diagnostic Methods to Detect Oral Cancer and Their Applications in Prevention and Treatment Strategies: A systematic review of systematic reviews Newer Diagnostic Methods to Detect Oral Cancer

Main Article Content

Dr. Sai Sree Kotari
Bharath Kumar Konakanchi
Supraja Salwaji
Mohan Kumar P
Prathyusha Kancherla
Maria Maddalena Marrapodi
Marco Cicciù
Giuseppe Minervini

Keywords

Clinical Applications, Diagnostic Methods, Early Detection, Non-invasive Techniques, Oral Cancer, Systematic Review

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.

Downloads

Download data is not yet available.
Abstract 24 | PDF Downloads 12

References

Global Burden of Disease Cancer Collaboration. Global, regional, and national cancer incidence, mortality, years of life lost, years lived with disability, and disability-adjusted life-years for 29 cancer groups, 1990 to 2017: A Systematic Analysis for the Global Burden of Disease Study. JAMA Oncol. 2019;5:1749–1768.
Bray, F., Ferlay, J., Soerjomataram, I., et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018;68(6):394–424. https://doi.org/10.3322/caac.21492.
Petersen, P.E. Oral cancer prevention and control the approach of the World Health Organization. Oral Oncol. 2009;45(4–5):454–60. https://doi.org/10.1016/j.oraloncology.2008.05.023.
Ferlay, J., Colombet, M., Soerjomataram, I., et al. Estimating the global cancer incidence and mortality in 2018: GLOBOCAN sources and methods. Int J Cancer. 2019;144(8):1941–1953. https://doi.org/10.1002/ijc.31937.
Brocklehurst, P., Kujan, O., O’Malley, L.A., et al. Screening programmes for the early detection and prevention of oral cancer. Cochrane Database Syst Rev. 2013;2013(11):CD004150. https://doi.org/10.1002/14651858.CD004150.pub4.
Ojeda, D., Huber, M.A., Kerr, A.R. Oral potentially malignant disorders and oral cavity cancer. Dermatol Clin. 2020;38(4):507–21. https://doi.org/10.1016/j.det.2020.05.011.
Jerjes, W., Stevenson, H., Ramsay, D., et al. Enhancing oral cancer detection: A systematic review of the diagnostic accuracy and future integration of optical coherence tomography with artificial intelligence. J Clin Med. 2024;13(19):5822. https://doi.org/10.3390/jcm13195822.
Balasubramaniam, A.M., Sriraman, R., Sindhuja, P., et al. Autofluorescence based diagnostic techniques for oral cancer. J Pharm Bioallied Sci. 2015;7(Suppl 2):S374–7. https://doi.org/10.4103/0975-7406.163456.
Bastías, D., Maturana, A., Marín, C., et al. Salivary biomarkers for oral cancer detection: An exploratory systematic review. Int J Mol Sci. 2024;25(5):2634. https://doi.org/10.3390/ijms25052634.
Kim, D.H., Kim, S.W., Hwang, S.H. Efficacy of optical coherence tomography in the diagnosing of oral cancerous lesion: Systematic review and meta-analysis. Head Neck. 2023;45(2):473–481. https://doi.org/10.1002/hed.27232.
Wu, C., Gleysteen, J., Teraphongphom, N.T., et al. In-vivo optical imaging in head and neck oncology: Basic principles, clinical applications and future directions. Int J Oral Sci. 2018;10(2):10. https://doi.org/10.1038/s41368-018-0011-4.
González-Moles, M.Á., Aguilar-Ruiz, M., Ramos-García, P. Challenges in the early diagnosis of oral cancer, evidence gaps and strategies for improvement: A scoping review of systematic reviews. Cancers (Basel). 2022;14(19):4967. https://doi.org/10.3390/cancers14194967.
Li, J., Kot, W.Y., McGrath, C.P., et al. Diagnostic accuracy of artificial intelligence assisted clinical imaging in the detection of oral potentially malignant disorders and oral cancer: A systematic review and meta-analysis. Int J Surg. 2024;110(8):5034–5046. https://doi.org/10.1097/JS9.0000000000001469.
Gambino, A., Martina, E., Panzarella, V., et al. Potential use of optical coherence tomography in oral potentially malignant disorders: In-vivo case series study. BMC Oral Health. 2023;23(1):540. https://doi.org/10.1186/s12903-023-03263-w.
Jain, A.K. Oral cancer screening: Insights into epidemiology, risk factors, and screening programs for improved early detection. Cancer Screen Prev. 2024;3(2):97–105. https://doi.org/10.14218/CSP.2023.00029S.
Wang, S., Yang, M., Li, R., et al. Current advances in noninvasive methods for the diagnosis of oral squamous cell carcinoma: A review. EurJ MedRes. 2023;28(1):53. https://doi.org/10.1186/s40001-022-00916-4.
Mohamad, I., Glaun, M.D., Prabhash, K., et al. Current treatment strategies and risk stratification for oral carcinoma. ASCO Educational Book. 2023;43:e389810. https://doi.org/10.1200/EDBK_389810.
Chen, X.J., Zhang, X.Q., Liu, Q., et al. Nanotechnology: A promising method for oral cancer detection and diagnosis. J Nanobiotechnology. 2018;16:1–7. https://doi.org/10.1186/s12951-018-0378-6.
Jiang, W.P., Wang, Z., Xu L.X., et al. Diagnostic model of saliva peptide finger print analysis of oral squamous cell carcinoma patients using weak cation exchange magnetic beads. Biosci Rep. 2015;35:e00211. https://doi.org/10.1042/BSR20150023.
Li, X., Wei, L., Pan, L., et al. Homogeneous immunosorbent assay based on single-particle enumeration using upconversion nanoparticles for the sensitive detection of cancer biomarkers. Anal Chem. 2018;90:4807–14. https://doi.org/10.1021/acs.analchem.8b00251.
Janissen, R., Sahoo, P.K., Santos, C.A., et al. InP nanowire biosensor with tailored biofunctionalization: Ultrasensitive and highly selective disease biomarker detection. Nano Lett. 2017;17:5938–49. https://doi.org/10.1021/acs.nanolett.7b01803.
Almangush, A., Heikkinen, I., Makitie, A.A., et al. Prognostic biomarkers for oral tongue squamous cell carcinoma: A systematic review and meta-analysis. Br J Cancer. 2017;117:856–66. https://doi.org/10.1038/bjc.2017.244.
Kinane, D.F., Gabert, J., Xynopoulos, G., et al. Strategic approaches in oral squamous cell carcinoma diagnostics using liquid biopsy. Periodontology 2000. 2024. https://doi.org/10.1111/prd.12567.
Adeoye, J. and Thomson, P. Strategies to improve diagnosis and risk assessment for oral cancer patients. Fac Dent J. 2020;11(3):122–7. https://doi.org/10.1308/rcsfdj.2020.97.
Coletta, R.D., Yeudall, W.A., Salo. T. Current trends on prevalence, risk factors and prevention of oral cancer. Front Oral Health. 2024;5:1505833. https://doi.org/10.3389/froh.2024.1505833.
Naito, Y. and Honda, K. Liquid biopsy for oral cancer diagnosis: Recent advances and challenges. J Pers Med. 2023;13(2):303. https://doi.org/10.3390/jpm13020303.
Hegde, S., Ajila, V., Zhu, W., et al. Artificial intelligence in early diagnosis and prevention of oral cancer. Asia Pac J Oncol Nurs. 2022;9(12):100133. https://doi.org/10.1016/j.apjon.2022.100133.
Tobias, M.A., Nogueira, B.P., Santana, M.C., et al. Artificial intelligence for oral cancer diagnosis: What are the possibilities? Oral Oncol. 2022;134:106117. https://doi.org/10.1016/j.oraloncology.2022.106117.