# Oral cancer risk stratification: A cross‐sectional population‐based screening study in Northeast India

**Authors:** Kunal Oswal, Satirtha Barman, Alexandar R. Kerr, Murad Zaman, Jnyanashree Patowary, Debasis D. Barali, Nipam Barman, Ashok Das, Umakant Nadkar, Rajesh Dikshit, Jennifer E. Gallagher, Mark W. Lingen, Richard Muwonge, Philip E. Castle, Li C. Cheung, Kelly J. Yu, Anil K. Chaturvedi, Arnie Purushotham

PMC · DOI: 10.1002/ijc.70160 · 2025-09-19

## TL;DR

This study developed a risk model for oral cancer in Northeast India, showing it works well locally but poorly in other regions, suggesting the need for region-specific models.

## Contribution

The study highlights the importance of region-specific oral cancer risk models due to poor transportability of the Northeast India model to Southern India.

## Key findings

- The risk model had good internal discrimination (AUC = 0.83) but poor external calibration in Southern India (E/O ratio = 0.52).
- Tobacco pouch keratosis was identified as an early carcinogenic event that could be targeted for behavioral intervention.
- 30% of individuals at highest risk accounted for 81.8% of prevalent lesions in the study population.

## Abstract

We conducted a cross‐sectional oral cancer screening study in Northeast India to develop and validate an oral precancer/cancer risk prediction model. We compared epidemiologic profiles between tobacco pouch keratosis and oral precancer/cancer. During 2018–2022, we recruited 14,749 participants who underwent an interviewer‐administered questionnaire and oral examination (visual inspection and autofluorescence). Logistic regression was used to compare risk factors between tobacco pouch keratosis and precancer/cancer and risk model development for prevalent lesions (keratosis and oral precancer/cancer, combined). Model validation was conducted internally and externally (Kerala oral cancer screening trial). Among the 14,749 participants, as per dentists' diagnosis, 1365 lesions were identified. These included 249 benign lesions (prevalence = 1.6%), 795 tobacco pouch keratosis (prevalence = 5.4%), and 321 precancers/cancers (prevalence = 2.2%). Agreement between dentists and health workers was high for visual diagnosis of prevalent lesions (keratotic/precancer/cancer; positive‐agreement = 87.5%; kappa = 0.77; 95% confidence interval [CI] = 0.75–0.78). Risk factor profiles were similar between tobacco pouch keratosis and oral precancer/cancer. The risk prediction model (based on age, sex, education, income, chewing duration, chewing type, smoking duration and intensity, alcohol duration and intensity) had good discrimination (area under the curve [AUC] = 0.83) and calibration (E/O ratio = 1.00) internally. Further, 30% of individuals at the highest model‐predicted risk accounted for 81.8% of prevalent lesions. However, in external validation, the risk model had modest discrimination (AUC = 0.67; 95% CI = 0.66–0.68) and poor calibration (E/O ratio = 0.52; 95% CI = 0.50–0.54). Our results suggest tobacco pouch keratosis as an early carcinogenic event amenable for behavioral interception. Poor transportability of our risk model reflects the need for prediction models that account for geographic differences in risk factors within regions in India.

What's new?

Oral cancer burden is high in India. Risk stratification for selection of high‐risk individuals could enable early detection through efficient screening programs. We conducted a cross‐sectional screening study in Northeast India to develop and validate an oral precancer/cancer risk prediction model. We show that despite good internal validation, the model was poorly calibrated externally in Southern India, highlighting the need for prediction models that account for geographic differences in risk behaviors across India.

## Linked entities

- **Diseases:** oral cancer (MONDO:0023644)

## Full-text entities

- **Diseases:** benign lesions (MESH:D001932), carcinogenic (MESH:D011230), keratotic (MESH:C537526), Oral cancer (MESH:D009062), cancer (MESH:D009369), keratosis (MESH:D007642)
- **Chemicals:** alcohol (MESH:D000438)

## Figures

2 figures with captions in the complete paper: https://tomesphere.com/paper/PMC12811214/full.md

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Source: https://tomesphere.com/paper/PMC12811214