ReviewOdontology2026
Radiomics features as imaging biomarkers in alveolar and craniofacial bone healing: a scoping review.
Review in Odontology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Radiomics has emerged as a promising approach for quantifying bone regeneration by extracting high-dimensional features from routine imaging data. This scoping review aimed to map the current evidence on the application of radiomics in bone remodeling, identify methodological trends, and explore its clinical applicability in regenerative procedures. A comprehensive literature search was conducted in PubMed, Scopus, Web of Science, Elsevier, and Google Scholar up to July 2025. Eligible studies were original investigations applying radiomics features, such as texture analysis, fractal dimension, and histogram-based metrics, derived from CT or cone-beam CT (CBCT) images to assess bone healing or regenerative outcomes. Nine studies met the inclusion criteria, comprising seven retrospective and two prospective designs, with sample sizes ranging from 25 to 450 patients. All studies utilized CBCT imaging and evaluated bone healing following tooth extraction (n = 3), socket preservation (n = 2), or grafting procedures (n = 3). The most frequently extracted features were gray-level co-occurrence matrix (GLCM) parameters (n = 7), histogram of oriented gradients (HOG) (n = 3), and fractal dimensions (n = 4). Feature selection methods included least absolute shrinkage and selection operator (LASSO) regression and statistical filtering techniques. Four studies incorporated machine learning models, including 3D U-Net architectures; however, validation strategies and performance metrics were inconsistently reported. Overall, radiomics demonstrates potential for characterizing bone regeneration patterns, particularly through GLCM-based texture features. Nevertheless, validation was limited, reporting was heterogeneous, and clinical integration remains minimal. Future research should prioritize well-designed prospective studies, standardized radiomics workflows, and integration of multimodal biomarkers to facilitate reliable clinical translation.
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Identifiers
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Registered trials
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.