{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T22:13:16Z","timestamp":1790115196357,"version":"4.0.1"},"reference-count":30,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T00:00:00Z","timestamp":1785110400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Clin. Med.","JCM"],"abstract":"<jats:p>Background: Radiographic differentiation of odontogenic cysts remains challenging due to overlapping imaging features and limited specificity of conventional assessment. Radiomics offers a quantitative approach to extract imaging biomarkers that may improve non-invasive lesion characterization. Accurate differentiation among cyst subtypes is clinically relevant, as different odontogenic cysts may warrant different management and follow-up strategies. Methods: This study included 63 patients with histopathologically confirmed odontogenic cysts (odontogenic keratocyst [OKC], n = 33; radicular cyst [RC], n = 12; dentigerous cyst [DC], n = 10; odontogenic cyst, not otherwise specified [OC-NOS], n = 8). Panoramic radiographs were manually segmented to define cyst and anatomical regions of interest. Radiomic features were extracted using three groups: agnostic texture features, morphological\/intensity descriptors, and perilesional features. Classification was performed using logistic regression with leave-one-out cross-validation. Pairwise and four-class classification analyses were conducted, and feature importance was evaluated. Results: Radiomic features demonstrated significant differences between cyst and anatomical regions, with approximately 60% of features remaining significant after correction. Pairwise classification performance varied across lesion types, achieving the highest balanced accuracy for DC vs. OC-NOS (0.838) and OKC vs. OC-NOS (0.752), while comparisons involving OKC vs. RC (0.602) and OKC vs. DC (0.644) were less discriminative. Texture-based features, particularly NGTDM-derived metrics, were among the most informative. The integration of morphological and perilesional features improved performance: four-class balanced accuracy increased from 0.416 with texture-only features to 0.487 with the combined feature set (p = 0.002). Conclusions: Radiomics-based analysis of panoramic radiographs enables moderate but preliminary differentiation of odontogenic cysts, with performance comparable to prior studies. The inclusion of perilesional and spatial features enhances classification, highlighting the importance of contextual information. Despite these advances, substantial overlap between cyst types persists, underscoring the need for multimodal diagnostic approaches and further validation in larger cohorts.<\/jats:p>","DOI":"10.3390\/jcm15155858","type":"journal-article","created":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T09:47:41Z","timestamp":1785145661000},"page":"5858","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Perilesional and Contextual Radiomic Features Improve Differentiation of Odontogenic Cysts on Panoramic Radiographs"],"prefix":"10.3390","volume":"15","author":[{"given":"Barbara","family":"Obuchowicz","sequence":"first","affiliation":[{"name":"Department of Conservative Dentistry with Endodontics, Institute of Dentistry, Jagiellonian University Medical College, Montelupich 4, 31-155 Krakow, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joanna","family":"Zarzecka","sequence":"additional","affiliation":[{"name":"Department of Conservative Dentistry with Endodontics, Institute of Dentistry, Jagiellonian University Medical College, Montelupich 4, 31-155 Krakow, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-2055-4391","authenticated-orcid":false,"given":"Marzena","family":"Jakubowska","sequence":"additional","affiliation":[{"name":"Imaging Diagnostics Laboratory, University Dental Clinic in Krakow, Montelupich 4, 31-155 Krakow, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5883-5551","authenticated-orcid":false,"given":"Rafa\u0142","family":"Obuchowicz","sequence":"additional","affiliation":[{"name":"College of Medical Sciences, 02-678 Warsaw, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2516-1823","authenticated-orcid":false,"given":"Julia","family":"Lasek","sequence":"additional","affiliation":[{"name":"Faculty of Geology, Geophysics and Environmental Protection, AGH University of Krakow, 30-059 Krakow, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,7,27]]},"reference":[{"key":"ref_1","first-page":"321","article-title":"What is currently known about odontogenic keratocysts?","volume":"20","author":"Valdivia","year":"2022","journal-title":"Oral Health Prev. Dent."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1951","DOI":"10.1016\/j.joen.2015.08.036","article-title":"Radicular cyst: An update of the biological factors related to lining epithelium","volume":"41","author":"Bernardi","year":"2015","journal-title":"J. Endod."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"462","DOI":"10.1111\/jop.12042","article-title":"A clinicopathological study of 338 dentigerous cysts","volume":"42","author":"Lin","year":"2013","journal-title":"J. Oral Pathol. Med."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2160","DOI":"10.1097\/SCS.0b013e3182323fbe","article-title":"Pattern of odontogenic and nonodontogenic cysts","volume":"22","author":"Butt","year":"2011","journal-title":"J. Craniofac. Surg."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.cden.2019.08.002","article-title":"Odontogenic cysts","volume":"64","year":"2020","journal-title":"Dent. Clin. N. Am."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"883","DOI":"10.1007\/s13244-018-0644-z","article-title":"Odontogenic keratocyst: Imaging features of a benign lesion with an aggressive behaviour","volume":"9","author":"Borghesi","year":"2018","journal-title":"Insights Imaging"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"WS66","DOI":"10.2214\/AJR.09.7216","article-title":"Cystic and cystic-appearing lesions of the mandible: Review","volume":"196","author":"Subramaniam","year":"2011","journal-title":"AJR Am. J. Roentgenol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"77","DOI":"10.4103\/2321-3841.144673","article-title":"Dentigerous cysts of the jaws: Clinical and radiological findings of 18 cases","volume":"2","author":"Zerrin","year":"2014","journal-title":"J. Oral Maxillofac. Radiol."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Perez, A., Lenoir, V., and Lombardi, T. (2022). Dentigerous cysts with diverse radiological presentation highlighting diagnostic challenges. Diagnostics, 12.","DOI":"10.3390\/diagnostics12082006"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"825","DOI":"10.1016\/j.jds.2021.04.008","article-title":"Clinicopathological study of radicular cysts with actinomycosis","volume":"16","author":"Hwang","year":"2021","journal-title":"J. Dent. Sci."},{"key":"ref_11","first-page":"325","article-title":"Odontogenic keratocyst: A review of histogenesis, classification, clinical presentation, genetic aspect, radiographic picture, histopathology and treatment","volume":"45","author":"Ghazy","year":"2021","journal-title":"Egypt. J. Histol."},{"key":"ref_12","first-page":"1","article-title":"Keratocystic odontogenic tumour: Systematic review","volume":"40","year":"2011","journal-title":"Dentomaxillofac. Radiol."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Shear, M., and Speight, P. (2007). Cysts of the Oral and Maxillofacial Regions, Wiley-Blackwell. [1st ed.].","DOI":"10.1002\/9780470759769"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"213","DOI":"10.5005\/jp-journals-10005-1438","article-title":"Role of cone beam computed tomography in evaluation of radicular cyst mimicking dentigerous cyst in a 7-year-old child: A case report and literature review","volume":"10","author":"Mahesh","year":"2017","journal-title":"Int. J. Clin. Pediatr. Dent."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"S96","DOI":"10.1038\/modpathol.2016.191","article-title":"Diagnosing the most common odontogenic cystic and osseous lesions of the jaws for the practicing pathologist","volume":"30","author":"Robinson","year":"2017","journal-title":"Mod. Pathol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1628","DOI":"10.1016\/j.jobcr.2025.09.028","article-title":"Discrepancy in diagnosis and characteristics of odontogenic cystic lesions in mixed dentition period: A retrospective study","volume":"15","author":"Mukhtar","year":"2025","journal-title":"J. Oral Biol. Craniofac. Res."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"108654","DOI":"10.1016\/j.ejrad.2019.108654","article-title":"Using CT texture analysis to differentiate cystic and cystic-appearing odontogenic lesions","volume":"120","author":"Oda","year":"2019","journal-title":"Eur. J. Radiol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"729","DOI":"10.1016\/j.joen.2012.02.028","article-title":"Frequency and distribution of radiolucent jaw lesions: A retrospective analysis of 9,723 cases","volume":"38","author":"Koivisto","year":"2012","journal-title":"J. Endod."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"e05194","DOI":"10.1016\/j.heliyon.2020.e05194","article-title":"Differentiation of periapical granuloma from radicular cyst using cone beam computed tomography images texture analysis","volume":"6","author":"Bergamini","year":"2020","journal-title":"Heliyon"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1111\/odi.13223","article-title":"Diagnosis of cystic lesions using panoramic and cone beam computed tomographic images based on deep learning neural network","volume":"26","author":"Lee","year":"2020","journal-title":"Oral Dis."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"20047","DOI":"10.1038\/s41598-022-20802-7","article-title":"Magnetic resonance imaging texture analysis to differentiate ameloblastoma from odontogenic keratocyst","volume":"12","author":"Gomes","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"20190107","DOI":"10.1259\/dmfr.20190107","article-title":"The use and performance of artificial intelligence applications in dental and maxillofacial radiology: A systematic review","volume":"49","author":"Hung","year":"2020","journal-title":"Dentomaxillofac. Radiol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"769","DOI":"10.1177\/0022034520915714","article-title":"Artificial intelligence in dentistry: Chances and challenges","volume":"99","author":"Schwendicke","year":"2020","journal-title":"J. Dent. Res."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"594","DOI":"10.4103\/ejd.ejd_325_18","article-title":"History and application of artificial neural networks in dentistry","volume":"12","author":"Park","year":"2018","journal-title":"Eur. J. Dent."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"e104","DOI":"10.1158\/0008-5472.CAN-17-0339","article-title":"Computational radiomics system to decode the radiographic phenotype","volume":"77","author":"Fedorov","year":"2017","journal-title":"Cancer Res."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Cohen, J. (2013). Statistical Power Analysis for the Behavioral Sciences, Routledge. [2nd ed.].","DOI":"10.4324\/9780203771587"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Pociask, E., Nurzynska, K., Obuchowicz, R., Ba\u0142on, P., Uryga, D., Strzelecki, M., Izworski, A., and Pi\u00f3rkowski, A. (2021). Differential diagnosis of cysts and granulomas supported by texture analysis of intraoral radiographs. Sensors, 21.","DOI":"10.3390\/s21227481"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Lopes, D.L.G., Lopes, S.L.P.D.C., Ungaro, D.M.D.T., Gomes, A.P.M., Moura, N.B.D., Gonc\u0327alves, B.C., and Costa, A.L.F. (2025). Radiomics-driven CBCT texture analysis as a novel biosensor for quantifying periapical bone healing: A comparative study of intracanal medications. Biosensors, 15.","DOI":"10.3390\/bios15020098"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1241","DOI":"10.1055\/s-0042-1759489","article-title":"Association between clinical\/radiographic characteristics and histopathological diagnoses of periapical granuloma and cyst","volume":"17","author":"Banomyong","year":"2023","journal-title":"Eur. J. Dent."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"e289","DOI":"10.5327\/2525-5711.289","article-title":"The use of artificial intelligence in the diagnosis of odontogenic cysts and tumors","volume":"10","author":"Souza","year":"2025","journal-title":"J. Oral Diagn."}],"container-title":["Journal of Clinical Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2077-0383\/15\/15\/5858\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T10:09:38Z","timestamp":1785146978000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2077-0383\/15\/15\/5858"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,27]]},"references-count":30,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2026,8]]}},"alternative-id":["jcm15155858"],"URL":"https:\/\/doi.org\/10.3390\/jcm15155858","relation":{},"ISSN":["2077-0383"],"issn-type":[{"value":"2077-0383","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,27]]}}}