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Optimization and Validation of a Cost-effective Image-Guided Automated Extracapsular Extension Detection Framework through Interpretable Machine Learning in Head and Neck Cancer

Award Number: R03DE032766
ORGANIZATION: NATIONAL INSTITUTE OF DENTAL AND CRANIOFACIAL RESEARCH
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 09/01/2023
PERIOD OF PERFORMANCE END DATE: 08/31/2025

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Optimization and Validation of a Cost-effective Image-Guided Automated Extracapsular Extension Detection Framework through Interpretable Machine Learning in Head and Neck Cancer - 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.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2026 ( Subtotal = -$31,457 )
2026	2024	MISSISSIPPI STATE UNIVERSITY	245 BARR AVE	MISSISSIPPI STATE	MS	39762	OKTIBBEHA	USA	Oral Diseases and Disorders Research	000	2	2/19/2026	NON-COMPETING CONTINUATION	-$31,457
														Subtotal = -$31,457

Issue Date FY: 2024 ( Subtotal = $147,937 )
2024	2024	MISSISSIPPI STATE UNIVERSITY	245 BARR AVE	MISSISSIPPI STATE	MS	39762	OKTIBBEHA	USA	Oral Diseases and Disorders Research	001	2	9/5/2024	NON-COMPETING CONTINUATION	$0
2024	2024	MISSISSIPPI STATE UNIVERSITY	245 BARR AVE	MISSISSIPPI STATE	MS	39762	OKTIBBEHA	USA	Oral Diseases and Disorders Research	000	2	8/8/2024	NON-COMPETING CONTINUATION	$147,937
														Subtotal = $147,937

Issue Date FY: 2023 ( Subtotal = $159,664 )
2023	2023	MISSISSIPPI STATE UNIVERSITY	245 BARR AVE MCARTHUR HALL	MISSISSIPPI STATE	MS	39762	OKTIBBEHA	USA	Oral Diseases and Disorders Research	000	1	8/24/2023	NEW	$159,664
														Subtotal = $159,664

Grand Total All Awards = $276,144

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Optimization and Validation of a Cost-effective Image-Guided Automated Extracapsular Extension Detection Framework through Interpretable Machine Learning in Head and Neck Cancer

Award Number: R03DE032766

ORGANIZATION: NATIONAL INSTITUTE OF DENTAL AND CRANIOFACIAL RESEARCH

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

PERIOD OF PERFORMANCE START DATE: 09/01/2023

PERIOD OF PERFORMANCE END DATE: 08/31/2025

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