Project Summary
Squamous cell carcinoma of the head and neck (SCCHN) is the 6th commonest cancer in the world,
leading to >300,000 deaths annually worldwide. The extracapsular extension (ECE) of the tumor in the
lymph nodes is a significantly high-risk feature in head and neck cancers. Several studies demonstrate
that ECE results in worse survival outcomes. Pathologic confirmation, such as neck dissection, is
currently required as the gold standard for clinical identification of ECE. However, if it is ECE positive,
postoperative chemoradiotherapy has to be considered. As a result, neck dissection followed by
chemoradiation increases the toxicity, especially late toxicity such as fibrosis, and lymphedema can get
worse due to the late toxicity. If we can detect ECE during preoperative evaluation, we can select those
patients for chemoradiation before surgery. Thus, predicting ECE becomes a piece of critical
information for clinicians planning treatment. The biggest obstacles to adopting AI/ML algorithms in the
clinic are concerns about security, reliability, and transparency. If an algorithm could be developed to
not only provide an accurate prediction of ECE (and/or clinical staging), but also to provide transparent
and clinically understandable communication of how the predictive conclusion was reached, then
clinicians would more readily adopt a tool utilizing that algorithm to be an ally. The purpose of this
proposal is to optimize and pathologically validate AI/ML approaches for prognoses and diagnoses of
ECE from medical images on head and neck cancer patients, which could be further implemented as a
tool for diagnostic assistance and precision medicine. This translational research project focuses on
filling the gap of AI/ML transparency and interpretability in the automated detection of ECE in head
and neck cancers. In addition, our interpretable ML algorithm will not require the pre-annotated lymph
node areas, which is a quite cost-effective technique by eliminating the time-consuming lymph node
annotation process. We propose the following aims: 1) Validate and interpret the image-based ECE
diagnosis model and its association with head and neck cancer anatomic organ sites and HPV status.
2) Optimize the cost-effective image-based ECE diagnosis model considering clinical markers within
pathology, PETCT, and MRI results for clinical implementation. We will validate our model based on the
dataset collected from both our team and existing data collected from The Cancer Image Archive. This
proposal aligns with the mission of Oral & Salivary Cancer Biology Program and the NIDCR Special
Interest in Supporting Dental, Oral, and Craniofacial Research Using Bioinformatic, Computational, and
Data Science Approaches (NOT-DE-20-006). This study will provide the preliminary results of our
future research on the precision treatment of high-risk head and neck cancer patients. We plan to
submit an NIDCR R01 proposal in Year 2 for the precision treatment of high-risk head and neck cancer.
