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Open Access
CC BY 4.0 · Indian J Med Paediatr Oncol
DOI: 10.1055/s-0045-1812849
Brief Communication

Oral Biopsy Score: A Quantitative Approach to Clinical Decision Making for Biopsies in Suspicious Oral Lesions

Authors

  • Hitesh R. Singhavi 

    1   Department of Oncosciences, Krishna Institute Medical Science, Thane, Maharashtra, India

  • Chanchal Dharia

    2   Department of Oral Surgery, Terna Dental College, Navi Mumbai Nerul, Maharashtra, India

  • Anil Heroor

    1   Department of Oncosciences, Krishna Institute Medical Science, Thane, Maharashtra, India

Funding None.
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Abstract

Oral potentially malignant lesions are often underdiagnosed, leading to malignant transformation, progression, and spread of disease. It is also responsible for poor survival outcomes. To save these life years, early detection is a must. Unfortunately, it requires specialist access in many clinical settings. For faster, uniform screening, a quantitative approach, the oral biopsy score (OBS), has been formulated for risk stratification and sooner decision making. The OBS consists of risk factors such as tobacco/alcohol/areca nut use, size of the lesion, morphology, duration, location, and induration of the lesion. Each of these categories has an upper and lower score assigned. Depending on the added score, the inference will guide us in performing the biopsy or keeping it under surveillance. We advocate for validation of the OBS through multicentric prospective studies to assess its diagnostic performance. Incorporation into artificial intelligence-assisted image analysis will enhance the accuracy of the diagnosis. Ultimately, we believe the OBS can play a pivotal role in early detection of oral cancer, leading to reduction in morbidity and improvement in survival.

Keywords

biopsy - oral lesions - oral cancer - leukoplakia - erythroplakia - oral potentially malignant disorder - oral biopsy score

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Patient consent is not required.




Publication History

Article published online:
03 November 2025

© 2025. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/)

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