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Computed tomography radiomics-based cross-sectional detection of mandibular osteoradionecrosis in head and neck cancer survivors

  • 1The University of Texas MD Anderson Cancer Center, Department of Radiation Oncology, Houston, USA.
  • 2The University of Texas MD Anderson Cancer Center, Department of Imaging Physics, Houston, USA.
  • 3The University of Texas MD Anderson Cancer Center, Department of Radiation Oncology, Houston, USA; The University of Texas MD Anderson Cancer Center, Department of Imaging Physics, Houston, USA.
  • 4The University of Texas MD Anderson Cancer Center, Department of Radiology, Houston, USA.
  • 5The University of Texas MD Anderson Cancer Center, Department of Head and Neck Surgery, Houston, USA.
  • 6Department of Radiation Oncology, Princess Margaret Cancer Centre, Toronto, Canada.
  • 7Department of Radiation Oncology, Princess Margaret Cancer Centre, Toronto, Canada; Faculty of Dentistry, University of Toronto, Toronto, Canada.
  • 8Department of Radiation Oncology, University Medical Center Groningen, the Netherlands.
  • 9The University of Texas MD Anderson Cancer Center, Department of Radiation Oncology, Houston, USA; The University of Texas MD Anderson Cancer Center, Department of Head and Neck Surgery, Houston, USA. Electronic address: sylai@mdanderson.org.
  • 10The University of Texas MD Anderson Cancer Center, Department of Radiation Oncology, Houston, USA. Electronic address: cdfuller@mdanderson.org.
  • 11The University of Texas MD Anderson Cancer Center, Department of Radiation Oncology, Houston, USA; Department of Radiation Oncology, Baylor College of Medicine, Houston, USA. Electronic address: ASMohamed@mdanderson.org.
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June 14, 2025

Abstract

PURPOSE

This study aims to identify radiomic features from contrast-enhanced CT (CECT) scans that differentiate osteoradionecrosis (ORN) from normal mandibular bone in head and neck cancer (HNC) patients treated with radiotherapy (RT).

MATERIALS AND METHODS

CECT images from 150 patients with confirmed ORN diagnosis (2008-2018) at MD Anderson Cancer Center (MDACC) were analyzed (80 % train, 20 % test). Radiomic features were extracted using PyRadiomics from manually segmented ORN regions and automated contralateral healthy mandible regions. Correlation analysis (r > 0.95) reduced features for model training. A random Forest (RF) classifier with Recursive Feature Elimination identified discriminative features. Explainability was assessed using SHapley Additive exPlanations (SHAP) analysis on the 20 most important features identified by the trained RF classifier.

RESULTS

Of the 1316 radiomic features extracted, 810 features were excluded for high collinearity. From a set of 506 pre-selected radiomic features, 67 were optimal for RF classification, yielding 88% accuracy and a ROC AUC of 0.96. The model well calibrated (Log Loss 0.296, ECE 0.125) and achieved an accuracy of 88% and a ROC AUC of 0.96. The SHAP analysis revealed that higher values of Wavelet-LLH First order Mean and Median were associated with ORN of the jaw (ORNJ). Conversely, higher Exponential GLDM Dependence Entropy and lower Square First-order Kurtosis were more characteristic of normal mandibular tissue.

CONCLUSION

This study successfully developed a CECT-based radiomics model for differentiating ORNJ from healthy mandibular tissue in HNC patients after RT. Future work will focus on detecting subclinical ORNJ regions to guide earlier interventions.