Emerging Diagnostic Modalities in Oral Cancer Detection and Management

Modalities in Oral Cancer Detection and Management

Authors

  • Ashkan Badkoobeh Department of Oral and Maxillofacial Surgery, School of Dentistry, Qom University of Medical Sciences, Qom, Iran
  • Shadi Zarakhsh Faculty of Dentistry, Isfahan, Khorasgan Branch, Islamic Azad University, Isfahan, Iran
  • Mehrnaz Fayazi School of Dentistry, Islamic Azad University of Medical sciences, Tehran, Iran
  • Pouya Kavianpour Department of Endodontics, School of Dentistry, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
  • Zahra Bahman Faculty of Dentistry, Belarusian State Medical University, Belarus, Minsk
  • Maryam Onsori Department of Operative Dentistry, Shahid Sadoughi University of Medical Sciences, Yazd, Iran
  • Mahsa Etemadi Department of Periodontology, School of Dentistry, Tehran University of Medical Sciences, Tehran, Iran

DOI:

https://doi.org/10.31661/gmj.v13i.3423

Keywords:

Oral Cancer; Diagnostics; Salivary Biomarkers; Optical Imaging; Liquid Biopsy; PET-CT; MRI Spectroscopy; Artificial Intelligence; Early Detection; Personalized Medicine

Abstract

Oral cancer remains a significant global health concern, yet it is often detected at an advanced stage. The limitations of traditional diagnostic techniques have prompted increased research efforts towards the development of more efficient and early detection methods. Recent advancements in oral cancer diagnosis include the use of salivary biomarkers, optical imaging, liquid biopsy, advanced imaging, and AI algorithms. These non-invasive and painless sampling methods have shown high sensitivity and specificity, particularly in the case of salivary biomarkers. Clinical trials and cooperation are necessary to demonstrate the effectiveness of these technologies and gain approval from relevant authorities and acceptance among clinicians. This review highlights the potential of these new modalities in transforming the approach to oral cancer diagnosis, leading to early detection, accurate diagnosis, and quality treatment for patients, ultimately reducing the global burden of this disease.

References

Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: A Cancer Journal for Clinicians. 2018;68(6):394-424.

https://doi.org/10.3322/caac.21492

Jemal A, Bray F, Center MM, Ferlay J, Ward E, Forman D. Global cancer statistics. CA: A Cancer Journal for Clinicians. 2011;61(2):69-90.

https://doi.org/10.3322/caac.20107

Lingen MW, Kalmar JR, Karrison T, Speight PM. Critical evaluation of diagnostic aids for the detection of oral cancer. Oral Oncol. 2008;44(1):10-22.

https://doi.org/10.1016/j.oraloncology.2007.06.011

Sujir N, Ahmed J, Pai K, Denny C, Shenoy N. Challenges in Early Diagnosis of Oral Cancer: Cases Series. Acta Stomatol Croat. 2019;53(2):174-80.

https://doi.org/10.15644/asc53/2/10

Khan NH, Zareef U, Khan HF, Rehman A, Afridi A, Azeem S, et al. Evaluation of minimally invasive oropharyngeal specimens and extraction of nucleic acids for molecular diagnostic analysis. Evaluation. 2024;55(04):1429-38.

Saravanan S, Babu NA, T L, Dharmalingam Jothinathan MK. Leveraging advanced technologies for early detection and diagnosis of oral cancer: Warning alarm. Oral Oncol Rep. 2024;10:100260.

https://doi.org/10.1016/j.oor.2024.100260

Kinane DF, Gabert J, Xynopoulos G, Guzeldemir‐Akcakanat E. Strategic approaches in oral squamous cell carcinoma diagnostics using liquid biopsy. Periodontology. 2024 Apr 27;prd:12567.

https://doi.org/10.1111/prd.12567

Dhiman S, Sharma S, Mehta R. Artificial Intelligence In Oral Medicine And Radiology. JOMDR. 2024;5(1):1-6.

https://doi.org/10.52793/JOMDR.2024.5(1)-53

Karimi A, HaddadPajouh H. Artificial Intelligence, Important Assistant of Scientists and Physicians. GMJ. 2020;9:e2048.

https://doi.org/10.31661/gmj.v9i0.2048

Hoda N, Moza A, Byadgi AA, Sabitha KS. Artificial intelligence based assessment and application of imaging techniques for early diagnosis in oral cancers. Int Surg J. 2024;11(2):318-22.

https://doi.org/10.18203/2349-2902.isj20240195

Lauritzen BB, Jensen JS, Grønhøj C, Wessel I, Von Buchwald C. Impact of delay in diagnosis and treatment-initiation on disease stage and survival in oral cavity cancer: a systematic review. Acta Oncologica. 2021;60(9):1083-90.

https://doi.org/10.1080/0284186X.2021.1931712

Khijmatgar S, Yong J, Rübsamen N, Lorusso F, Rai P, Cenzato N, et al. Salivary biomarkers for early detection of oral squamous cell carcinoma (OSCC) and head/neck squamous cell carcinoma (HNSCC): A systematic review and network meta-analysis. Jpn Dent Sci Rev. 2024 Dec;60:32-9.

https://doi.org/10.1016/j.jdsr.2023.10.003

Seo BY, Lee CO, Kim JW. Changes in the management and survival rates of patients with oral cancer: a 30-year single-institution study. J Korean Assoc Oral Maxillofac Surg. 2016;42(1):31-7.

https://doi.org/10.5125/jkaoms.2016.42.1.31

Flügge T, Gaudin R, Sabatakakis A, Tröltzsch D, Heiland M, van Nistelrooij N, et al. Detection of oral squamous cell carcinoma in clinical photographs using a vision transformer. Sci Rep. 2023;13(1):2296.

https://doi.org/10.1038/s41598-023-29204-9

Franzmann EJ, Donovan MJ. Effective early detection of oral cancer using a simple and inexpensive point of care device in oral rinses. Expert Review of Molecular Diagnostics. 2018;18(10):837-44.

https://doi.org/10.1080/14737159.2018.1523008

Uthoff RD, Song B, Sunny S, Patrick S, Suresh A, Kolur T, et al. Point-of-care, smartphone-based, dual-modality, dual-view, oral cancer screening device with neural network classification for low-resource communities. Maitland KC, editor PLoS ONE. 2018;13(12):e0207493.

https://doi.org/10.1371/journal.pone.0207493

Khurshid Z, Zafar MS, Khan RS, Najeeb S, Slowey PD, Rehman IU. Role of salivary biomarkers in oral cancer detection. Adv Clin Chem. 2018;86:23-70.

https://doi.org/10.1016/bs.acc.2018.05.002

Vonk J, De Wit JG, Voskuil FJ, Witjes MJH. Improving oral cavity cancer diagnosis and treatment with fluorescence molecular imaging. Oral Diseases. 2021;27(1):21-6.

https://doi.org/10.1111/odi.13308

Kim DH, Kim SW, Lee J, Hwang SH. Narrow‐band imaging for screening of oral premalignant or cancerous lesions: A systematic review and meta‐analysis. Clin Otolaryngol. 2021;46(3):501-7.

https://doi.org/10.1111/coa.13724

Villard A, Breuskin I, Casiraghi O, Asmandar S, Laplace-Builhe C, Abbaci M, et al. Confocal laser endomicroscopy and confocal microscopy for head and neck cancer imaging: Recent updates and future perspectives. Oral Oncol. 2022;127:105826.

https://doi.org/10.1016/j.oraloncology.2022.105826

Baby NT, Abdullah A, Kannan S. The scope of liquid biopsy in the clinical management of oral cancer. Int J Oral Maxillofac Surg. 2022;51(5):591-601.

https://doi.org/10.1016/j.ijom.2021.08.017

Chowdhury R, Bhatia S, Singh G, Nasreen S, De D. Circulating tumor cells: Screening and monitoring of oral cancers. J Stomatol Oral Maxillofac Surg. 2018;119(6):498-502.

https://doi.org/10.1016/j.jormas.2018.06.008

Perdomo S, Avogbe PH, Foll M, Abedi-Ardekani B, Facciolla VL, Anantharaman D, et al. Circulating tumor DNA detection in head and neck cancer: evaluation of two different detection approaches. Oncotarget. 2017;8(42):72621-32.

https://doi.org/10.18632/oncotarget.20004

Eljabo N, Nikolic N, Carkic J, Jelovac D, Lazarevic M, Tanic N, et al. Genetic and epigenetic alterations in the tumour, tumour margins, and normal buccal mucosa of patients with oral cancer. Int J Oral Maxillofac Surg. 2018;47(8):976-82.

https://doi.org/10.1016/j.ijom.2018.01.020

Burian E, Palla B, Callahan N, Pyka T, Wolff C, Von Schacky CE, et al. Comparison of CT, MRI, and F-18 FDG PET/CT for initial N-staging of oral squamous cell carcinoma: a cost-effectiveness analysis. Eur J Nucl Med Mol Imaging. 2022;49(11):3870-7.

https://doi.org/10.1007/s00259-022-05843-4

Schwaninger DR, Hüllner M, Bichsel D, Giacomelli-Hiestand B, Stutzmann NS, Balermpas P, et al. FDG-PET/CT for oral focus assessment in head and neck cancer patients. Clin Oral Invest. 2022;26(6):4407-18.

https://doi.org/10.1007/s00784-022-04403-2

King AD, Thoeny HC. Functional MRI for the prediction of treatment response in head and neck squamous cell carcinoma: potential and limitations. Cancer Imaging. 2016;16(1):23.

https://doi.org/10.1186/s40644-016-0080-6

Hegde S, Ajila V, Zhu W, Zeng C. Artificial intelligence in early diagnosis and prevention of oral cancer. Asia Oncology Nursing Society. 2022;9(12):100133.

https://doi.org/10.1016/j.apjon.2022.100133

Lee JH, Kim DH, Jeong SN, Choi SH. Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm. Journal of Dentistry. 2018;77:106-11.

https://doi.org/10.1016/j.jdent.2018.07.015

Olms C, Hix N, Neumann H, Yahiaoui-Doktor M, Remmerbach TW. Clinical comparison of liquid-based and conventional cytology of oral brush biopsies: a randomized controlled trial. Head Face Med. 2018;14(1):9.

https://doi.org/10.1186/s13005-018-0166-4

Takano JH, Yakushiji T, Kamiyama I, Nomura T, Katakura A, Takano N, et al. Detecting early oral cancer: narrowband imaging system observation of the oral mucosa microvasculature. Int J Oral Maxillofac Surg. 2010;39(3):208-13.

https://doi.org/10.1016/j.ijom.2010.01.007

Lingen MW, Kalmar JR, Karrison T, Speight PM. Critical evaluation of diagnostic aids for the detection of oral cancer. Oral Oncology. 2008;44(1):10-22.

https://doi.org/10.1016/j.oraloncology.2007.06.011

e

Downloads

Published

2024-09-25

Issue

Section

Review Article