Szczegóły publikacji
Opis bibliograficzny
Radiomics as a decision support tool for detecting occult periapical lesions on intraoral radiographs / Barbara Obuchowicz, Joanna Zarzecka, Marzena Jakubowska, Rafał Obuchowicz, Michał Strzelecki, Adam PIÓRKOWSKI, Joanna Gołda, Karolina Nurzynska, Julia LASEK // Journal of Clinical Medicine [Dokument elektroniczny]. — Czasopismo elektroniczne ; ISSN 2077-0383 . — 2026 — vol. 15 iss. 3 art. no. 971, s. 1-17. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 15-17, Abstr. — Publikacja dostępna online od: 2026-01-25
Autorzy (9)
- Obuchowicz Barbara
- Zarzecka Joanna
- Jakubowska Marzena
- Obuchowicz Rafał
- Strzelecki Michał
- AGHPiórkowski Adam
- Gołda Joanna
- Nurzynska Karolina
- AGHLasek Julia
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 166793 |
|---|---|
| Data dodania do BaDAP | 2026-03-30 |
| Tekst źródłowy | URL |
| DOI | 10.3390/jcm15030971 |
| Rok publikacji | 2026 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Journal of Clinical Medicine |
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.
