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Cone-beam computed tomography-based radiomic analysis of architectural phenotypes in jaw cysts and tumors using interpretable artificial intelligence models
Sivan Sathish, Haritma Nigam, Department of Oral Medicine and Radiology, Teerthanker Mahaveer Dental College and Research Centre, Teerthanker Mahaveer University, Moradabad 244001, Uttar Pradesh, India
Rupal Gupta, Department of Computer Science, TMU College of Computing Sciences and IT, Teerthanker Mahaveer University, Moradabad 244001, Uttar Pradesh, India
Author contributions: Sathish S conceptualized and designed the study, performed radiological evaluation, model development, lesion phenotyping, volumetric segmentation, radiomic analysis, data interpretation, literature review, manuscript drafting, and final manuscript preparation; Nigam H supervised the study methodology, evaluated the radiological findings, critically reviewed the manuscript, and provided overall academic guidance throughout the study; Gupta R supervised the artificial intelligence and statistical components of the study, evaluated the machine learning methodology and analytical workflow, and critically reviewed the manuscript.
AI contribution statement: The authors declare that no AI tools were used in any part of manuscript or image preparation.
Institutional review board statement: This retrospective study was conducted in accordance with the ethical standards for research involving human participants and was approved by the Institutional Ethics Committee of the institution’s review board (approval No. TMDCRC/IEC/PHD/24-25/DENTAL02; IEC Proposal No. S-002/24). All CBCT datasets were anonymized prior to analysis, and no patient-identifiable information was used in the study.
Informed consent statement: Due to the retrospective nature of the study, the Institutional Review Board Committee waived the need of obtaining informed consent.
Conflict-of-interest statement: The author declared that there is no competing interest in the publication of this article.
STROBE statement: The authors have read the STROBE Statement-checklist of items, and the manuscript was prepared and revised according to the STROBE Statement- checklist of items.
Data sharing statement: All data supporting the findings of this study are available within the paper and the provided Supplementary material.
Corresponding author: Sivan Sathish, Doctorate Student, Department of Oral Medicine and Radiology, Teerthanker Mahaveer Dental College and Research Centre, Teerthanker Mahaveer University, Delhi Road, Moradabad 244001, Uttar Pradesh, India. drsivan.dental@tmu.ac.in
Received: May 28, 2026
Revised: June 26, 2026
Accepted: July 16, 2026
Published online: August 28, 2026
Processing time: 92 Days and 1 Hours
Revised: June 26, 2026
Accepted: July 16, 2026
Published online: August 28, 2026
Processing time: 92 Days and 1 Hours
Core Tip
Core Tip: Radiomic analysis of cone-beam computed tomographic images allows objective evaluation of architecture in jaw cysts and tumors through analysis of heterogeneity, spatial distribution, and texture of voxels. In contrast to traditional qualitative radiological interpretation, this phenotypic approach uses radiomics to classify jaw lesions into three groups: Homogenous with fluid predominance, intermediate with septations, and complex or heterogenous. Radiomic signatures based on texture have proved highly effective and biologically relevant, making them promising tools for imaging bio