AMO-ENE: Attention-based Multi-Omics Fusion Model for Outcome Prediction in Extra Nodal Extension and HPV-associated Oropharyngeal Cancer

2026-04-10

AI Summary

This study addresses the clinical challenge of assessing extranodal extension (ENE) in HPV-positive oropharyngeal cancer, which is hindered by low imaging contrast and labor-intensive manual annotation. To overcome this, the authors propose the first end-to-end automated prognostic prediction framework that integrates CT imaging with clinical data. The method employs a 3D semi-supervised segmentation model to automatically delineate ENE regions and combines radiomic and deep features from the primary tumor, using an attention mechanism for dynamic multimodal fusion. Evaluated on a cohort of 397 patients, the model achieved AUCs of 88.2%, 79.2%, and 78.1% for predicting two-year distant metastasis/recurrence, overall survival, and disease-free survival, respectively, with concordance indices exceeding 0.70 across all endpoints. These results substantially enhance the prognostic utility of ENE and demonstrate strong potential for clinical decision support.

Technology Category

• Machine Learning: Ensemble Methods
• Computer Vision: Segmentation
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Extranodal extension (ENE) is an emerging prognostic factor in human papillomavirus (HPV)-associated oropharyngeal cancer (OPC), although it is currently omitted as a clinical staging criteria. Recent works have advocated for the inclusion of iENE as a prognostic marker in HPV-positive OPC staging. However, several practical limitations continue to hinder its clinical integration, including inconsistencies in segmentation, low contrast in the periphery of metastatic lymph nodes on CT imaging, and laborious manual annotations. To address these limitations, we propose a fully automated end-to-end pipeline that uses computed tomography (CT) images with clinical data to assess the status of nodal ENE and predict treatment outcomes. Our approach includes a hierarchical 3D semi-supervised segmentation model designed to detect and delineate relevant iENE from radiotherapy planning CT scans. From these segmentations, a set of radiomics and deep features are extracted to train an imaging-detected ENE grading classifier. The predicted ENE status is then evaluated for its prognostic value and compared with existing staging criteria. Furthermore, we integrate these nodal features with primary tumor characteristics in a multimodal, attention-based outcome prediction model, providing a dynamic framework for outcome prediction. Our method is validated in an internal cohort of 397 HPV-positive OPC patients treated with radiation therapy or chemoradiotherapy between 2009 and 2020. For outcome prediction at the 2-year mark, our pipeline surpassed baseline models with 88.2% (4.8) in AUC for metastatic recurrence, 79.2% (7.4) for overall survival, and 78.1% (8.6) for disease-free survival. We also obtain a concordance index of 83.3% (6.5) for metastatic recurrence, 71.3% (8.9) for overall survival, and 70.0% (8.1) for disease-free survival, making it feasible for clinical decision making.

Problem

• Extranodal Extension
• HPV-associated Oropharyngeal Cancer
• Outcome Prediction
• Clinical Staging
• CT Imaging

Innovation

• attention-based fusion
• semi-supervised 3D segmentation
• radiomics
• multimodal outcome prediction
• extranodal extension