Evidence map›Paper›PMID 40974103›Full record

ArticleInternational journal of cancer2026

Oral cancer risk stratification: A cross-sectional population-based screening study in Northeast India.

Kunal Oswal, Satirtha Barman, Alexandar R Kerr, Murad Zaman, Jnyanashree Patowary, Debasis D Barali, Nipam Barman, Ashok Das, Umakant Nadkar, Rajesh Dikshit and 8 more

Abstract read
In one paragraph

Article in International journal of cancer, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

18 authors.

Kunal OswalKarkinos Healthcare Private Limited, Mumbai, India.
Satirtha BarmanDr B Borooah Cancer Institute, Guwahati, India.
Alexandar R KerrNew York University College of Dentistry, New York, USA.
Murad ZamanDr B Borooah Cancer Institute, Guwahati, India.
Jnyanashree PatowaryDr B Borooah Cancer Institute, Guwahati, India.
Debasis D BaraliDr B Borooah Cancer Institute, Guwahati, India.
Nipam BarmanDr B Borooah Cancer Institute, Guwahati, India.
Ashok DasDr B Borooah Cancer Institute, Guwahati, India.
Umakant NadkarAlamelu Charitable Trusts, Mumbai, India.
Rajesh DikshitCenter for Cancer Epidemiology, Mumbai, India.
Jennifer E GallagherKing's College London, London, UK.
Mark W LingenUniversity of Chicago, Chicago, USA.
Richard MuwongeInternational Agency for Research on Cancer, Lyon, France.
Philip E CastleDivision of Cancer Epidemiology and Genetics, National Cancer Institute, National Institute of Health, Bethesda, USA.
Li C CheungDivision of Cancer Epidemiology and Genetics, National Cancer Institute, National Institute of Health, Bethesda, USA.
Kelly J YuDivision of Cancer Epidemiology and Genetics, National Cancer Institute, National Institute of Health, Bethesda, USA.ORCID https://orcid.org/0000-0003-3832-6586
Anil K ChaturvediDivision of Cancer Epidemiology and Genetics, National Cancer Institute, National Institute of Health, Bethesda, USA.
Arnie PurushothamKing's College London, London, UK.ORCID https://orcid.org/0000-0001-8577-9655

Funding

Medical Research Council MR/S014438/1NCI NIH HHSTata Trusts CAN/000/000/TEDT/0032/DI/B030/MWorld Health Organization 001
6 · The paper itself

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.

Indexed as

Early Detection of CancerMouth NeoplasmsPrecancerous ConditionsAdultAgedCross-Sectional StudiesFemaleHumansIndiaMaleMiddle AgedPrevalenceRisk AssessmentRisk FactorsSurveys and QuestionnairesIndiaoral cancer screeningrisk prediction model

Identifiers

PMID40974103
PMCPMC12811214

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.