ArticleDento maxillo facial radiology2023
Radiomics in bone pathology of the jaws.
Article in Dento maxillo facial radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Radiomic Analysis of Mandibular Bone Changes in Patients with Chronic Myeloid Leukemia on Panoramic Radiographs.Journal of imaging informatics in medicine · 2026Article
- Cone-beam computed tomography-based radiomic analysis of architectural phenotypes in jaw cysts and tumors using interpretable artificial intelligence models.World journal of radiology · 2026Article
- Radiomics-based quantitative assessment of the mandibular cortical index on panoramic radiographs.Scientific reports · 2026Article
- MRI-Based Radiomics Model for Predicting Eligibility for Conservative Management of Endometrial Cancer.Cancer medicine · 2026Article
- Radiomics in the Evaluation of Cystic and Neoplastic Lytic Lesions of the Jaws.Diagnostics (Basel, Switzerland) · 2026Article
- Multimodal Radiogenomic Imaging in Oropharyngeal Squamous Cell Carcinoma: Implications for Dentomaxillofacial Radiology.Medical sciences (Basel, Switzerland) · 2026Review
- Imaging Modalities in Medication-Related Osteonecrosis of the Jaw: A Narrative Review of Diagnostic Findings and Staging.Medicina (Kaunas, Lithuania) · 2025Review
- Identification of Novel Biomarkers for Malignant Thyroid Nodules: A Preliminary Study Based on Ultrasound Omics.Annals of biomedical engineering · 2025Article
- Radiomics and prognostic nutritional index for predicting postoperative survival in esophageal carcinoma.European journal of medical research · 2025Article
- CBCT image based radiomic analysis for condylar resorption after orthognathic surgery.Clinical oral investigations · 2025Article
- Unveiling the power of artificial intelligence for image-based diagnosis and treatment in endodontics: An ally or adversary?International endodontic journal · 2025Review
- Automatic segmentation and visualization of cortical and marrow bone in mandibular condyle on CBCT: a preliminary exploration of clinical application.Oral radiology · 2025Article
- Accuracy of web-based automated versus digital manual cephalometric landmark identification.Clinical oral investigations · 2024Article
- Texture Analysis in Volumetric Imaging for Dentomaxillofacial Radiology: Transforming Diagnostic Approaches and Future Directions.Journal of imaging · 2024Review
- Utilizing radiomics for differential diagnosis of inverted papilloma and chronic rhinosinusitis with polyps based on unenhanced CT scans.Scientific reports · 2024Article
- Classification of Ameloblastoma, Periapical Cyst, and Chronic Suppurative Osteomyelitis with Semi-Supervised Learning: The WaveletFusion-ViT Model Approach.Bioengineering (Basel, Switzerland) · 2024Article
- A diagnostic approach integrated multimodal radiomics with machine learning models based on lumbar spine CT and X-ray for osteoporosis.Journal of bone and mineral metabolism · 2023Article
- CBCT for Diagnostics, Treatment Planning and Monitoring of Sinus Floor Elevation Procedures.Diagnostics (Basel, Switzerland) · 2023Review
Corrections and comments
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Authors and funding
7 authors.
Funding
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Abstract
objectiveTo define which are and how the radiomics features of jawbone pathologies are extracted for diagnosis, predicting prognosis and therapeutic response.
methodsA comprehensive literature search was conducted using eight databases and gray literature. Two independent observers rated these articles according to exclusion and inclusion criteria. 23 papers were included to assess the radiomics features related to jawbone pathologies. Included studies were evaluated by using JBI Critical Appraisal Checklist for Analytical Cross-Sectional Studies.
resultsAgnostic features were mined from periapical, dental panoramic radiographs, cone beam CT, CT and MRI images of six different jawbone alterations. The most frequent features mined were texture-, shape- and intensity-based features. Only 13 studies described the machine learning step, and the best results were obtained with Support Vector Machine and random forest classifier. For osteoporosis diagnosis and classification, filtering, shape-based and Tamura texture features showed the best performance. For temporomandibular joint pathology, gray-level co-occurrence matrix (GLCM), gray level run length matrix (GLRLM), Gray Level Size Zone Matrix (GLSZM), first-order statistics analysis and shape-based analysis showed the best results. Considering odontogenic and non-odontogenic cysts and tumors, contourlet and SPHARM features, first-order statistical features, GLRLM, GLCM had better indexes. For odontogenic cysts and granulomas, first-order statistical analysis showed better classification results.
conclusionsGLCM was the most frequent feature, followed by first-order statistics, and GLRLM features. No study reported predicting response, prognosis or therapeutic response, but instead diseases diagnosis or classification. Although the lack of standardization in the radiomics workflow of the included studies, texture analysis showed potential to contribute to radiologists' reports, decreasing the subjectivity and leading to personalized healthcare.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.