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Diagnostics
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Focus on Children's Oral Health: Advances in Pediatric Dentistry and...
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Focus on Children's Oral Health: Advances in Pediatric Dentistry and Imaging Assessments

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Medical Imaging and Theranostics".

Deadline for manuscript submissions: closed (31 March 2026) | Viewed by 287

Special Issue Editor

Dr. Iole Vozza

E-Mail Website
Guest Editor
Department of Oral and maxillofacial Sciences, Sapienza University of Rome, 00161 Rome, Italy
Interests: pediatric dentistry; oral cancer; oral pathology; dental materials; medical device
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims to explore the clinical management of oral healthcare pediatric patients populations, shedding light on the research status, core problems to be solved, and potential solutions within this field. Oral healthcare plays a crucial role in overall health and quality of life, and it is vital in addressing the unique challenges and considerations faced by diverse pediatric patient populations.

It will focus on gathering research and insights related to the effective management of oral healthcare for individuals with varying needs, including, but not limited to, special needs patients, medically compromised patients, and individuals from culturally diverse backgrounds. It seeks to explore evidence-based strategies, best practices, and innovative approaches that can improve oral healthcare outcomes and promote equitable access to quality care.

The scope of this Special Issue encompasses a wide range of topics, such as preventive measures, diagnostic techniques, treatment modalities, patient communication, interdisciplinary collaboration, and oral health promotion. We strongly encourage contributions from researchers and clinicians that address the existing gaps, identify barriers, propose practical solutions, and facilitate the development of patient-centered approaches.

We look forward to receiving your contributions.

Dr. Iole Vozza
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • special needs patients
  • oral healthcare
  • pediatric dentistry
  • dental hygiene
  • oral pathology
  • orthodontics
  • periodontics
  • imaging

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Published Papers (1 paper)

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Research

19 pages, 1337 KB  
Article
Radiomics in the Evaluation of Cystic and Neoplastic Lytic Lesions of the Jaws
by Paola Di Giacomo, Pasquale Frisina, Alberto Fratocchi, Pierluigi Barra, Cira Rosaria Tiziana Di Gioia, Flavia Adotti, Giovanni Falisi, Fabrizio Spallaccia, Iole Vozza, Antonella Polimeni, Carlo Di Paolo and Daniela Messineo
Diagnostics 2026, 16(8), 1222; https://doi.org/10.3390/diagnostics16081222 - 20 Apr 2026
Abstract
Background/Objectives. Radiomics is an emerging imaging-based tool that enhances lesion characterization beyond conventional diagnostic approaches. Its potential in evaluating osteolytic lesions of the jaws lies in improving discrimination between benign and malignant entities. This study aimed at developing a predictive model to identify [...] Read more.
Background/Objectives. Radiomics is an emerging imaging-based tool that enhances lesion characterization beyond conventional diagnostic approaches. Its potential in evaluating osteolytic lesions of the jaws lies in improving discrimination between benign and malignant entities. This study aimed at developing a predictive model to identify radiomic features capable of distinguishing benign from malignant lesions. Methods. Subjects with preoperative CT or CBCT and histopathological confirmation were included. A pilot cohort was used for feature selection via LASSO regression, which ranked features by frequency and absolute coefficient. Malignancy was coded as class 1, benign lesions as class 0. Positive coefficients indicated association with malignancy, while negative coefficients with benign characteristics. The most stable features were initially trained on the pilot cohort and then validated on an independent test set through machine learning classifiers as LASSO, support vector machine, artificial neural network, random forest e XGboost. Results. The sample comprised 69 subjects (pilot cohort = 57, test cohort = 12). The predictors selected from LASSO regression were: DifferenceEntropy_GLCM (−0.768), CenterOfMassShift_MORPHOLOGICAL (−1.390), INTENSITY-HISTOGRAM_MaximumHistogramGradientGrayLevel (1.139), GLRLM_ShortRunLowGrayLevelEmphasis (−0.742), and Maximum3DDiameter_MORPHOLOGICAL (0.932). As for model performance on test, LASSO achieved the best performance (AUC 0.83), with perfect specificity and sensitivity of 0.71. SVM showed good AUC but poor sensitivity, while random forest and XGBoost performed poorly (AUC 0.57 and 0.37, respectively). Conclusions. The LASSO model proved to be a transparent and robust classifier, suitable for both feature selection and external validation. The selected features demonstrated strong discriminative ability, supporting the potential of radiomics in improving lesion assessment and guiding clinical decision-making. Full article
(This article belongs to the Special Issue Focus on Children's Oral Health: Advances in Pediatric Dentistry and Imaging Assessments)
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