Independent Multicentre Validation of the ‘Six-Point’ Model for Malignant Transformation Risk in Oral Epithelial Dysplasia

  • Hanya Mahmood
  • , Mike J. Bradburn
  • , Nadim Mohammed Islam
  • , Omar Kujan
  • , Nasir Rajpoot
  • , Alan Roger Santos-Silva
  • , Pablo Agustin Vargas
  • , Jacqueline James
  • , Paul Nankivell
  • , Hisham Mehanna
  • , Syed Ali Khurram

Research output: Contribution to journalArticlepeer-review

Abstract

Background: Histological grading of oral epithelial dysplasia (OED) is used for predicting malignant transformation risk. However, grading is associated with significant subjectivity and observer variability leading to inconsistency in prognosis pre-diction. Alternate histological feature-specific models (‘six-point’ and ‘two-point’) have been shown to have potentially better prognostic reliability than conventional grading. This study conducts a multicentre validation of these models.

Methods: 102 OED cases (dating 2012–2017) were acquired from 4 independent centres (13 (13%) Sheffield; 40 (39%) Belfast; 30(29%) Birmingham; 19 (19%) Piracicaba, Brazil) were independently scored using the ‘six-point’ and ‘two-point’ models. Feature prevalence, observer agreement and malignant transformation risk were evaluated and compared to conventional grading systems.

Results: The ‘six-point’ system demonstrated superior predictive value (AUROC of 0.81) compared to the ‘two-point’ system (AUROC = 0.73, p = 0.004), WHO grading (AUROC = 0.71, p = 0.03) and binary grading (AUROC = 0.68, p = 0.009).Transformation rate for the ‘six-point’ model was 50% (95% CI 27%–78%) when all 6 features were present compared to 14% (95%CI 5%–32%) when 2–3 features were present.

Conclusions: This study supports the superior performance of the ‘six-point’ system for transformation prediction on a multi-centric sample. Findings indicate that feature-specific models may be more reliable than existing histological grading systems for prognosis prediction.
Original languageEnglish
Number of pages10
JournalOral Diseases
DOIs
Publication statusE-pub ahead of print - 26 Dec 2025

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