{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T21:09:32Z","timestamp":1778533772878,"version":"3.51.4"},"reference-count":35,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T00:00:00Z","timestamp":1778112000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background\/Objectives: This study aims to evaluate the diagnostic performance of radiomic features derived from cone-beam computed tomography (CBCT) images in differentiating radicular cysts (RC) from periapical granulomas (PG). The study also compares the performance of traditional machine learning (ML) algorithms with a novel deep learning (DL) model, Radiomics Cyst Convolutional Neural Network (RadC-CNN). Methods: CBCT images of 98 patients (55 RC, 43 PG), confirmed by histopathological diagnosis, were retrospectively analyzed. Lesions were semi-automatically segmented in 3D Slicer, and 48 radiomic features were extracted. Features with high inter-observer agreement (Intraclass Correlation Coefficient ICC \u2265 0.80) were included in the analysis. Statistical tests and classification models (Decision Tree, K-Nearest Neighbors, Support Vector Machine) were used, and performance was compared to that of the proposed RadC-CNN architecture. Results: Among the 34 features with sufficient reliability, 18 showed statistically significant differences between RC and PG (p &lt; 0.05). Shape, first-order, and texture-based features, including the Gray Level Co-occurrence Matrix (GLCM), Gray Level Run Length Matrix (GLRLM), Gray Level Size Zone Matrix (GLSZM), and Neighboring Gray Tone Difference Matrix (NGTDM), were extracted. The RadC-CNN model demonstrated superior classification performance with an accuracy of 90%, sensitivity of 90%, and precision of 91.3%, outperforming all traditional ML algorithms. Conclusions: CBCT-based radiomic analysis, particularly when combined with DL techniques like RadC-CNN, offers a promising non-invasive approach to distinguish RC from PG.<\/jats:p>","DOI":"10.3390\/diagnostics16101428","type":"journal-article","created":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T20:14:16Z","timestamp":1778530456000},"page":"1428","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Radiomics-Based Differential Diagnosis of Radicular Cysts and Apical Granulomas on CBCT Images Using RadC-CNN Architecture"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1577-5568","authenticated-orcid":false,"given":"Bilg\u00fcn","family":"\u00c7etin","sequence":"first","affiliation":[{"name":"Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Selcuk University, 42250 Konya, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8043-288X","authenticated-orcid":false,"given":"Derya","family":"\u0130\u00e7\u00f6z","sequence":"additional","affiliation":[{"name":"Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Selcuk University, 42250 Konya, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4894-4962","authenticated-orcid":false,"given":"Kevser","family":"Din\u00e7","sequence":"additional","affiliation":[{"name":"Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Selcuk University, 42250 Konya, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1949-8211","authenticated-orcid":false,"given":"\u0130smail","family":"Kayadibi","sequence":"additional","affiliation":[{"name":"Department of Management Information Systems, Faculty of Economic and Administrative Sciences, Afyon Kocatepe University, 03200 Afyonkarahisar, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,5,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2329","DOI":"10.1016\/j.acra.2023.05.039","article-title":"Utility of ultrasonography for diagnosing and differentiating periapical granuloma from radicular cyst","volume":"30","author":"Jia","year":"2023","journal-title":"Acad. 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