Friday, April 26, 2024 4/26/2024

A Quantitative Risk Model for Predicting Outcome and Identifying Structural Biomarkers of Treatment Targets in Oral Cancer on a Large Multi-Center Patient Cohort

Award Number: R01DE028741
ORGANIZATION: NATIONAL INSTITUTE OF DENTAL AND CRANIOFACIAL RESEARCH
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

Group Awards By Issue Date FY or Funding FY:

View Award Abstract

Post-resection prognostication for oral cavity cancers (OCC) is qualitative and potentially ambiguous. A significant subset (25-37%) of Stage I/II patients still develop local recurrence after treatment with surgery alone. The long-term goal of this proposal will be to create a Quantitative Risk Model (QRM) using machine learning and artificial intelligence to predict recurrence risk for Stage I/II patients using image-based biomarkers of aggression. The objective is to develop and validate state-of-the-art systems for biomarker imaging, quantification, and modeling to accurately predict risk of recurrence in cancer patients based on image analytics. The central hypothesis is that a quantitative, artificial intelligence approach to pathology will result in significantly greater prognostic value compared with manual microscope-based analysis. The rationale for this work is that tumor aggression can be predicted from patterns present in pathology images, given the existence of histological risk models that have been clinically validated in the past; however, these risk models are not in widespread use because they are less accurate, robust, and transportable to the larger community of pathologists. This proposal will test the central hypothesis through three specific aims: (1) Develop an analysis pipeline that can accurately predict recurrence risk for Stage I/II OCC patients and identify treatment targets (e.g. adaptive local immune response and angiogenesis); (2) Demonstrate robust performance across a multi-site data cohort collected from seven national and international centers; and (3) Distil the results of QRM analysis to synoptic pathology reporting, demonstrating the ability of QRM to interface with standard clinical reporting tools. The innovation for addressing these aims comes from a unique application of active learning for training artificial intelligence to recognize tissue structures, new features for quantifying tissue architecture based on the interface between tumor and host, and a novel approach for large cross-site validation. Moreover, this proposal develops a unique mapping between computational pathology and commonly-used synoptic reporting variables, enabling rapid uptake of this work into existing clinical workflows. This research is significant because it provides personalized outcome predictions for a niche group of undertreated patients with limited options and can serve as the foundation for designing future clinical trials through identification of treatment targets. Multi-site training and evaluation, combined with AI-to-report mapping, will be broadly applicable to a large group of computational approaches, bridging the gap between engineering research labs and clinical application. The expected outcome of this work is a trained model for predicting Stage I/II OCC recurrence, identification of treatment targets, and mapping to synoptic reports, as well as a broadly-applicable workflow for the broader computational pathology community. This project will have a large positive impact on patients and surgical pathologists by enabling rapid, accurate prognosis and directed treatment plans in an easy-to-use pipeline that integrates seamlessly into existing clinical workflows.


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: 2024 ( Subtotal = $341,950 )
2024	2024	RESEARCH FOUNDATION FOR THE STATE UNIVERSITY OF NEW YORK, THE	520 LEE ENTRANCE STE 211	AMHERST	NY	14228	ERIE	USA	Oral Diseases and Disorders Research	000	5	3/19/2024	NON-COMPETING CONTINUATION	$341,950
														Subtotal = $341,950

Issue Date FY: 2023 ( Subtotal = $379,976 )
2023	2023	RESEARCH FOUNDATION FOR THE STATE UNIVERSITY OF NEW YORK, THE	520 LEE ENTRANCE STE 211	AMHERST	NY	14228	ERIE	USA	Oral Diseases and Disorders Research	000	4	4/6/2023	NON-COMPETING CONTINUATION	$379,976
														Subtotal = $379,976

Issue Date FY: 2022 ( Subtotal = $376,210 )
2022	2022	RESEARCH FOUNDATION FOR THE STATE UNIVERSITY OF NEW YORK, THE	520 LEE ENTRANCE STE 211	AMHERST	NY	14228	ERIE	USA	Oral Diseases and Disorders Research	001	3	4/21/2022	NON-COMPETING CONTINUATION	$34,201
2022	2022	RESEARCH FOUNDATION FOR THE STATE UNIVERSITY OF NEW YORK, THE	520 LEE ENTRANCE STE 211	AMHERST	NY	14228	ERIE	USA	Oral Diseases and Disorders Research	000	3	3/2/2022	NON-COMPETING CONTINUATION	$342,009
														Subtotal = $376,210

Issue Date FY: 2021 ( Subtotal = $380,042 )
2021	2021	RESEARCH FOUNDATION FOR THE STATE UNIVERSITY OF NEW YORK, THE	520 LEE ENTRANCE STE 211	AMHERST	NY	14228	ERIE	USA	Oral Diseases and Disorders Research	000	2	3/16/2021	NON-COMPETING CONTINUATION	$380,042
														Subtotal = $380,042

Issue Date FY: 2020 ( Subtotal = $382,900 )
2020	2020	RESEARCH FOUNDATION FOR THE STATE UNIVERSITY OF NEW YORK, THE	520 LEE ENTRANCE STE 211	AMHERST	NY	14228	ERIE	USA	Oral Diseases and Disorders Research	001	1	8/18/2020	NEW	$0
2020	2020	RESEARCH FOUNDATION FOR THE STATE UNIVERSITY OF NEW YORK, THE	520 LEE ENTRANCE STE 211	AMHERST	NY	14228	ERIE	USA	Oral Diseases and Disorders Research	000	1	4/23/2020	NEW	$382,900
														Subtotal = $382,900

Grand Total All Awards = $1,861,078

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A Quantitative Risk Model for Predicting Outcome and Identifying Structural Biomarkers of Treatment Targets in Oral Cancer on a Large Multi-Center Patient Cohort

Award Number: R01DE028741

ORGANIZATION: NATIONAL INSTITUTE OF DENTAL AND CRANIOFACIAL RESEARCH

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

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

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