Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Jun 12, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

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
PubMed
Summary

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

LLM-Driven Extraction of NI-RADS and Imaging Tumor Characteristics to Enhance Oropharyngeal Cancer Survivorship Surveillance.

medRxiv : the preprint server for health sciences·2026
Same author

Automated identification of bolus types in modified barium swallow studies using deep learning: a preliminary study.

medRxiv : the preprint server for health sciences·2026
Same author

Interpreting Treatment Effects Using Posterior Probabilities: A Bayesian Reanalysis of 230 Phase III Oncology Trials.

JCO clinical cancer informatics·2026
Same author

Expiratory Muscle Strength Training in Head and Neck Cancer Survivors With Radiation-Associated Dysphagia: Results of a Pilot Prospective Trial.

Head & neck·2026
Same author

Technical Development and Implementation of 3D-QALAS on a 1.5T MR-Linac for the Brain: A Prospective R-IDEAL Stage 0/1 Technology Development Report.

medRxiv : the preprint server for health sciences·2026
Same author

Designing an Electronic Patient-Reported Outcomes Information Infrastructure Supported by the RE-AIM Implementation Framework.

Advances in cancer education and quality improvement·2026
Same journal

Towards Superhuman Imitation Learning for Sequential Head-and-Neck Cancer Treatment Decisions.

Proceedings of the ... Symposium on Applied Computing. Symposium on Applied Computing·2026
Same journal

Patient-GAT: Sarcopenia Prediction using Multi-modal Data Fusion and Weighted Graph Attention Networks.

Proceedings of the ... Symposium on Applied Computing. Symposium on Applied Computing·2023
Same journal

SymptomGraph: Identifying Symptom Clusters from Narrative Clinical Notes using Graph Clustering.

Proceedings of the ... Symposium on Applied Computing. Symposium on Applied Computing·2023
Same journal

Semantic Table-of-Contents for Efficient Web Screen Reading.

Proceedings of the ... Symposium on Applied Computing. Symposium on Applied Computing·2022
Same journal

Ranking Novel Regulatory Genes in Gene Expression Profiles using NetExpress.

Proceedings of the ... Symposium on Applied Computing. Symposium on Applied Computing·2021
Same journal

Parallel Materialization of Large ABoxes.

Proceedings of the ... Symposium on Applied Computing. Symposium on Applied Computing·2011
See all related articles
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.

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.
Keywords:
Contrastive LearningDose-Volume HistogramNormal Tissue ComplicationOsteoradionecrosis

More Related Videos

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

Related Experiment Videos

Last Updated: Jun 12, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

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.