# Radiomics as a Decision Support Tool for Detecting Occult Periapical Lesions on Intraoral Radiographs

## TL;DR

This study shows that radiomic analysis can detect hidden periapical lesions on regular dental X-rays, potentially reducing the need for more advanced imaging.

## Contribution

The study introduces radiomic texture analysis as a novel decision support tool for identifying occult periapical lesions on intraoral radiographs.

## Key findings

- 44 radiomic texture features showed significant differences between CBCT-only and lesion-free regions.
- A logistic regression model achieved a mean AUC of 0.71 and 68% accuracy for lesion detection.
- Smaller ROIs (20–40 pixels) improved classification performance compared to larger regions.

## Abstract

Background: Periapical lesions are common consequences of pulp necrosis but may remain undetectable on conventional intraoral radiographs, becoming evident only on cone-beam computed tomography (CBCT). Improving lesion recognition on plain radiographs is therefore of high clinical relevance. Methods: This retrospective, single-center study analyzed 56 matched pairs of intraoral periapical radiographs (RVG) and CBCT scans. A total of 109 regions of interest (ROIs) were included, which were classified as CBCT-positive/RVG-negative (onlyCBCT, n = 64) or true negative (noLesion, n = 45). Radiomic texture features were extracted from circular ROIs on RVG images using PyRadiomics. Feature distributions were compared using Mann–Whitney U tests with false discovery rate correction, and classification was performed using a logistic regression model with nested cross-validation. Results: Forty-four radiomic texture features showed statistically significant differences between onlyCBCT and noLesion ROIs, predominantly with small to medium effect sizes. For a 40-pixel ROI radius, the classifier achieved a mean area under the ROC curve of 0.71, mean accuracy of 68%, and mean sensitivity of 73%. Smaller ROIs (20–40 pixels) yielded higher AUCs and substantially better accuracy than larger sampling regions (≥60 pixels). Conclusions: Quantifiable radiomic signatures of periapical pathology are present on conventional radiographs even when lesions are visually occult. Radiomics may serve as a complementary decision support tool for identifying CBCT-only periapical lesions in routine clinical imaging.