SMuRF: Software for Predicting Outcomes in Head and Neck Cancer


Novel data learning framework for integrating radiology and pathology data for discovering prognostic biomarkers and predicting outcomes in head and neck cancer.

Key Benefits

  • Can analyze multiple high-resolution images to identify specific and detailed areas of high prognostic value.
  • Concurrently processes radiology and pathology images to find associations at the micro- and macro- scales.
  • Versatile software allows for integration of additional information, such as genomic or clinical data, to bolster prognosis.
  • Has the potential for use in other cancers.

Market Summary

Head and neck cancer (HNC) is the seventh most common cancer in the world, with 1.1 million new diagnoses reported annually. In the US, the incidence is over 54,000 cases per year, resulting in over 11,000 annual deaths. HNC often spreads to lymph nodes in the neck, which may result in multiple regions of importance for radiology and pathology imaging. However, most data learning models for cancer diagnosis process medical images independently and are targeted at one specific region. Accurate grade classification and biomarker identification of HNC may inform clinicians as to the risk level of patients and inform treatment routes.

Technical Summary

Researchers at Emory developed Swin Transformer-based MultiModal and Multi-Region Data Fusion Framework (SMuRF) to help predict outcomes in head and neck cancer. This software can simultaneously process images from multiple anatomical sites, such as the primary tumor and associated lymph nodes, at the micro- and macro- scales. SMuRF integrates these multi-modal and multi-scale images for risk stratification and to identify specific regions of high prognostic value.

Patent Information

Tech ID: 23116
Published: 1/10/2024