# Enhancing malignant transformation predictions in oral potentially malignant disorders: A novel machine learning framework using real-world data

## TL;DR

This study introduces a new machine learning model called SANN that better predicts which oral potentially malignant disorders will become cancerous compared to traditional methods.

## Contribution

A novel Self Attention Artificial Neural Network (SANN) model is proposed for predicting malignant transformation in OPMDs.

## Key findings

- The SANN model achieved an AUC of 0.9877, outperforming traditional methods like the Cox-PH nomogram.
- SANN demonstrated high sensitivity, specificity, accuracy, and precision, all exceeding 0.96.
- Comprehensive evaluations confirmed SANN's robustness, generalizability, and superior predictive efficacy.

## Abstract

This study addresses the challenge of accurately predicting malignant transformation risk in patients with oral potentially malignant disorders (OPMDs). Using data from 1,094 patients across three institutions (2004–2023), the researchers compared traditional statistical methods, including a Cox proportional hazards (Cox-PH) nomogram, with machine learning (ML) algorithms. A novel Self Attention Artificial Neural Network (SANN) model was developed, trained, and validated alongside other ML models including ANN, RF, and DeepSurv. The SANN model outperformed all other approaches, achieving an AUC of 0.9877, with sensitivity, specificity, accuracy, and precision exceeding 0.96. In comparison, the Cox-PH nomogram achieved AUCs of 0.880–0.902. Comprehensive evaluations using Receiver Operating Characteristic, calibration curves, and decision curve analysis demonstrated SANN’s superior predictive efficacy, robustness, and generalizability. These findings highlight the potential of customized ML models, particularly SANN, to enhance early identification and management of high-risk OPMD patients, outperforming conventional statistical methods.

•The malignancy potential of OPMD highlights the importance of early detection and management•Machine learning models outperform traditional statistics in predicting cancer progression•The proposed SANN model provides accurate predictions, supporting clinical decision-making•SANN shows strong generalizability and is well suited for clinical integration

The malignancy potential of OPMD highlights the importance of early detection and management

Machine learning models outperform traditional statistics in predicting cancer progression

The proposed SANN model provides accurate predictions, supporting clinical decision-making

SANN shows strong generalizability and is well suited for clinical integration

Public health; Artificial intelligence