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Early detection is critical to improving prognosis and survival. Advances in radiomics and machine learning (ML) support earlier and more accurate diagnosis by extracting quantitative imaging data and learning predictive patterns. This review summarizes radiomics and ML methods for early OPMD detection, covering imaging modalities, data processing, current research, and future challenges.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/radiomics-and-machine-learning-in-early-detection-of-oral-potentially-malignant-disorders/460277/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/radiomics-and-machine-learning-in-early-detection-of-oral-potentially-malignant-disorders/460277.png","ImageObject",300,407,{"name":92,"@type":93},"A glass of water","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-07","2026-09-30",true,{"@type":102,"interactionType":103,"userInteractionCount":81},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"Why is early detection important for oral potentially malignant disorders (OPMDs)?","Question",{"text":112,"@type":113},"OPMDs have a risk of transforming into oral squamous cell carcinoma (OSCC). 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Priya J3, Sumit Bhatt4, Prasad C. Ingale5, Hiroj Bagde6  \n1Department of Oral Medicine and Radiology, Chhattisgarh Dental College and Research Institute, Rajnandgaon, Chhattisgarh, India, 2Department of Public Health Dentistry, Teerthanker Mahaveer Dental College and Research Centre, Moradabad, Uttar Pradesh, India, 3Department of Oral Medicine and Radiology, Tamil Nadu Government Dental College and Hospital, Chennai, Tamil Nadu, India, 4Department of Oral and Maxillofacial Surgery, Rajasthan Dental College and Hospital, Nirwan University, Jaipur, Rajasthan, India, 5Department of Conservative Dentistry and Endodontics, Bharati Vidyapeeth (Deemed to be University) Dental College and Hospital, Sangli, Maharashtra, India, 6Department of Periodontology and Implantology, Chhattisgarh Dental College and Research Institute, Rajnandgaon, Chhattisgarh, India  \nSubmitted: 10‑Aug‑2025  \nRevised: 15‑Aug‑2025  \nAccepted: 25‑Aug‑2025  \nPublished: 17‑Dec‑2025  \nAbstract  \nOral potentially malignant disorders (OPMDs) are a diverse group of conditions that carry a risk of transformation into oral squamous cell carcinoma (OSCC) . Early detection is vital to improving prognosis and survival. Recent advancementsin radiomics and machine learning (ML) have shown promise in enhancing the diagnostic accuracy and early detection of OPMDs. Radiomics enables the extraction of quantitative data from medical images, while ML algorithms analyze patterns and correlations for predictive modeling. This review highlights the application of radiomics and ML in early OPMD detection, focusing on imaging modalities, data processing techniques, current research findings, and future challenges.  \nKeywords: Imaging modalities, machine learning, oral potentially malignant disorders, oral squamous cell carcinoma, radiomics   \nIntroduction  \nOral  \na  \npotentially group of  \nmalignant disorders (OPMDs) are conditions, including leukoplakia,  \nerythroplakia, oral submucous fibrosis, and lichen planus, that have a recognized risk of developing into oral squamous cell carcinoma (OSCC) . OSCC  \n\n| Access this article online |  |\n| --- | --- |\n| Quick Response Code:\u003Cbr>| Website: [https://journals.lww.com/jpbs](https://journals.lww.com/jpbs) |\n|  | DOI: 10.4103/jpbs.jpbs_1442_25 |\n\nis one of the most common malignancies worldwide, especially in regions with high tobacco and areca nut  \n\n| Address for correspondence: Dr. Ashwini Dhopte, Department of Oral Medicine and Radiology, Chhattisgarh Dental College and Research Institute, Rajnandgaon, Chhattisgarh, India.\u003Cbr>E‑mail: [ashwinidhopte@gmail.com](ashwinidhopte@gmail.com) |\n| --- |\n| This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License (CC BY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal.\u003Cbr>For reprints [contact:](contact: WKHLRPMedknow_reprints@wolterskluwer.com)[ WKHLRPMedknow_reprints@wolterskluwer.com](contact: WKHLRPMedknow_reprints@wolterskluwer.com) |\n| How to cite this article: Dhopte A, Singh V, Priya JS, Bhatt S, Ingale PC, Bagde H. Radiomics and machine learning in early detection of oral potentially malignant disorders. J Pharm Bioall Sci 2025;17:S2852‑4. |\n\nS2852 © 2025 Journal of Pharmacy and Bioallied Sciences | Published by Wolters Kluwer-Medknow   \nconsumption. Early detection of OPMDs is crucial for timely intervention and improved prognosis. However, traditional diagnostic tools, including clinical examination and biopsy, have limitations related to inter‑observer variability, invasiveness, and sampling errors.[1,2]  \nAdvances in computational diagnostics—particularly radiomics and machine learning (ML)—offer new strategies for identify","cbCaigO15VkmAVuN","https://ap.wps.com/l/cbCaigO15VkmAVuN","pdf",479552,"English","# Introduction\n## Oral potentially malignant disorders and the need for early detection\n# Radiomics: A data-driven approach to image-based diagnosis\n## Radiomics workflow: acquisition, segmentation, feature extraction, modeling\n# Imaging modalities in oral medicine for radiomic analysis\n## Cone-beam computed tomography (CBCT)\n## Magnetic resonance imaging (MRI)","[{\"question\":\"Why is early detection important for oral potentially malignant disorders (OPMDs)?\",\"answer\":\"OPMDs have a risk of transforming into oral squamous cell carcinoma (OSCC). Early detection enables timely intervention and improves prognosis and survival.\"},{\"question\":\"What does radiomics do in image-based diagnosis?\",\"answer\":\"Radiomics converts standard medical images into quantitative, high-dimensional features that reflect underlying tissue pathophysiology, supporting analysis of tumor heterogeneity and microenvironmental changes.\"},{\"question\":\"How do machine learning (ML) methods contribute to OPMD detection?\",\"answer\":\"ML algorithms analyze complex feature sets to identify patterns and correlations, supporting predictive modeling for early detection of OPMDs.\"}]","Radiomics and Machine Learning in Early Detection of Oral Potentially Malignant Disorders | PDF"]