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Predicting the Presence of Clinically Significant Thyroid Cancer using Ultrasound Imaging

Award Number: R21EB030691
ORGANIZATION: NATIONAL INSTITUTE Of BIOMEDICAL IMAGING & BIOENGINEERING
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

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PROJECT SUMMARY/ABSTRACT There has been significant work in creating tools that leverage computer vision algorithms to automate medical image analysis. Most of these algorithms have been developed for natural images, which are usually single static images that can be treated individually. However, medical images are usually part of a study that may include various views and orientations that are considered together with other clinical data when making a diagnosis. Three dimensional convolution neural networks (CNN) can address this issue in part when images are evenly spaced, but many medical imaging modalities such as ultrasound (US), fluoroscopy, and biopsy imaging have variable orientations and irregular spacing. Graph convolutional networks (GCN) have the potential to address this issue as they generalize the assumptions of CNNs to work on arbitrarily structured graphs. Automatic thyroid nodule detection in ultrasound (US) is one application that such a graph-based approach could have a large impact. The thyroid cancer incidence rate has tripled in the past thirty years, with an estimated cost of $18-21 billon in 2019. US is the imaging modality of choice, which consists of multiple 2D images of different locations and orientations. US readings are often vague and subjective in nature, which has resulted in a steady increase in the number of biopsies performed over the past 20 years. It is estimated that about one-third of all thyroid biopsy procedures performed in the United States are medically unnecessary, leading to the unmet need for noninvasive diagnostic tests that can reliably identify which nodules require a biopsy. The research objective of this R21 is to develop a new graph-based approach to leverage spatial information contained within imaging studies that will be combined with biomarkers and other known risk factors. Our graph model will enable more complete detection of thyroid cancer, as well as the prediction of future cancer aggression, both with spatially localized explanations. GCN features will be used to predict voxel-level cancer suspicion, thereby enabling a novel method for performing “imaging biopsy.” Finally, voxel-level suspicion maps will be aggregated into patient-level quantitative imaging biomarkers and combined with clinical data to create a multimodal nomogram for performing risk stratification.


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: 2023 ( Subtotal = $175,454 )
2023	2023	UNIVERSITY OF CALIFORNIA, LOS ANGELES	10889 WILSHIRE BLVD STE 700	LOS ANGELES	CA	90024	LOS ANGELES	USA	Discovery and Applied Research for Technological Innovations to Improve Human Health	000	3	3/15/2023	NON-COMPETING CONTINUATION	$175,454
														Subtotal = $175,454

Issue Date FY: 2022 ( Subtotal = $185,566 )
2022	2022	UNIVERSITY OF CALIFORNIA, LOS ANGELES	10889 WILSHIRE BLVD STE 700	LOS ANGELES	CA	90024	LOS ANGELES	USA	Discovery and Applied Research for Technological Innovations to Improve Human Health	000	2	4/6/2022	NON-COMPETING CONTINUATION	$185,566
														Subtotal = $185,566

Issue Date FY: 2021 ( Subtotal = $224,566 )
2021	2021	UNIVERSITY OF CALIFORNIA, LOS ANGELES	10889 WILSHIRE BLVD STE 700	LOS ANGELES	CA	90095	LOS ANGELES	USA	Discovery and Applied Research for Technological Innovations to Improve Human Health	000	1	6/4/2021	NEW	$224,566
														Subtotal = $224,566

Grand Total All Awards = $585,586

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Predicting the Presence of Clinically Significant Thyroid Cancer using Ultrasound Imaging

Award Number: R21EB030691

ORGANIZATION: NATIONAL INSTITUTE Of BIOMEDICAL IMAGING & BIOENGINEERING

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

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

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