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Related Experiment Video

Updated: Jul 3, 2026

A Postoperative Evaluation Guideline for Computer-Assisted Reconstruction of the Mandible
10:42

A Postoperative Evaluation Guideline for Computer-Assisted Reconstruction of the Mandible

Published on: January 28, 2020

CBCT-Based Clinico-Radiomic Nomogram Predicting Preoperative Mandibular Third Molar Difficulty:

Mingchen Xu1, Yijun Wu1

  • 1Department of Emergency General, Wuxi Stomatological Hospital, Wuxi City, Jiangsu Province, China.

International Dental Journal
|July 1, 2026
PubMed
Summary
This summary is machine-generated.

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A new Cone-Beam Computed Tomography (CBCT) based nomogram accurately predicts mandibular third molar (MTM) extraction difficulty. This tool integrates radiomic features and clinical data for better surgical planning and patient care.

Area of Science:

  • Oral and Maxillofacial Surgery
  • Radiology
  • Medical Imaging

Background:

  • Predicting mandibular third molar (MTM) extraction difficulty is crucial for surgical planning.
  • Current methods may lack precision in assessing complex cases.
  • Cone-Beam Computed Tomography (CBCT) offers detailed anatomical information.

Purpose of the Study:

  • To develop and validate a clinico-radiomic nomogram for predicting MTM extraction difficulty.
  • To integrate CBCT radiomic features with clinical data for enhanced prediction.
  • To provide a noninvasive tool for preoperative stratification.

Main Methods:

  • Retrospective analysis of 600 patients undergoing MTM extraction.
  • Extraction of radiomic features from preoperative CBCT images.
Keywords:
Cone-beam computed tomographyMandibular third molarNomogramRadiomicsTooth extraction

Related Experiment Videos

Last Updated: Jul 3, 2026

A Postoperative Evaluation Guideline for Computer-Assisted Reconstruction of the Mandible
10:42

A Postoperative Evaluation Guideline for Computer-Assisted Reconstruction of the Mandible

Published on: January 28, 2020

  • Utilized Least Absolute Shrinkage and Selection Operator (LASSO) for feature selection and nomogram construction combining radiomic score and clinical predictors.

    • Main Results:

    • A clinico-radiomic nomogram integrating 5 predictors (Age, BMI, Pell & Gregory classification, Root Curvature, and radiomic score) was developed.
    • The nomogram achieved high predictive performance (AUC training=0.892, AUC validation=0.865), outperforming models with single data types.
    • Demonstrated excellent calibration and significant clinical utility via Decision Curve Analysis.

    Conclusions:

    • The developed CBCT-based clinico-radiomic nomogram is an accurate and noninvasive tool for preoperative prediction of MTM extraction difficulty.
    • This nomogram aids in informed surgical planning and personalized patient counseling.
    • Facilitates improved patient outcomes through precise difficulty stratification.