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  1. Home
  3. Advancing Normal Tissue Complication Probability Modeling With Supervised Contrastive Learning For Predicting Osteoradionecrosis.
  1. Home
  3. Advancing Normal Tissue Complication Probability Modeling With Supervised Contrastive Learning For Predicting Osteoradionecrosis.

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Advancing Normal Tissue Complication Probability Modeling with Supervised Contrastive Learning for Predicting

Eric Ababio Anyimadu1, Xinhua Zhang2, Clifton David Fuller3

  • 1Electrical and Computer Engineering, University of Iowa, Iowa City, Iowa, USA.

Proceedings of the ... Symposium on Applied Computing. Symposium on Applied Computing
|June 11, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

SC-NTCP, a new method using supervised contrastive learning, improves normal tissue complication probability (NTCP) modeling for head and neck cancer patients. It enhances prediction of osteoradionecrosis (ORN) by creating better dose-volume histogram (DVH) representations.

Keywords:
Contrastive LearningDose-Volume HistogramNormal Tissue ComplicationOsteoradionecrosis

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Area of Science:

  • Radiation oncology
  • Medical physics
  • Machine learning in healthcare

Background:

  • Normal tissue complication probability (NTCP) modeling using dose-volume histograms (DVHs) faces challenges like high dimensionality and multicollinearity.
  • Classical classification methods are limited by overlapping DVH profiles in patients with different toxicity outcomes.
  • Accurate prediction of radiation-induced toxicity is crucial for personalized cancer treatment planning.

Purpose of the Study:

  • To introduce SC-NTCP, a supervised contrastive learning framework for transforming DVH data into a compact, separable latent representation.
  • To optimize DVH data representation for predicting osteoradionecrosis (ORN) in head and neck cancer patients.
  • To improve the accuracy and interpretability of NTCP modeling compared to traditional approaches.

Main Methods:

  • Developed SC-NTCP, a supervised contrastive learning framework to create a latent representation of DVH data.
  • Maximized intra-class similarity and inter-class separability within the embedding space.
  • Benchmarked SC-NTCP against logistic regression, SVM, MLP, and CNN using a cohort of head and neck cancer patients, incorporating clinical covariates.

Main Results:

  • SC-NTCP achieved superior discrimination for ORN prediction with an AUC of 0.77.
  • The framework demonstrated improved calibration and enhanced interpretability through gradient-based feature attribution.
  • Integration of clinical covariates further augmented the predictive performance of SC-NTCP.

Conclusions:

  • SC-NTCP offers a principled and interpretable approach to robust radiation toxicity prediction, overcoming limitations of raw DVH data.
  • The method enhances NTCP modeling for osteoradionecrosis in head and neck cancer.
  • SC-NTCP has the potential to inform personalized treatment planning and improve clinical outcomes in radiation oncology.