Thursday, November 21, 2024 11/21/2024

IMAT-ITCR Collaboration: Develop deep learning-based methods to identify subtypes of circulating tumor cells from optical microscope images

Award Number: R21CA240185
ORGANIZATION: NATIONAL CANCER INSTITUTE
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

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IMAT-ITCR Collaboration: Develop deep learning-based methods to identify subtypes of circulating tumor cells from optical microscope images Project Summary/Abstract The goal of the parent IMAT project (R21CA240185) is to develop a new platform for fractionation and profiling of CTC subpopulations and elucidate the metastatic potential of CTCs. Currently, this work requires researchers to record hundreds of individual microscope images of the cells captured on the microchip, integrate all images with flow fluid simulations, and analyze three features of the capture cells (including angular position, normalized velocity and shear) for identification of CTC subtypes. This process is very labor-intensive and time-consuming, as most of the steps rely on manual operations. The goal of the ITCR project (1U01CA249245) is to develop an informatics platform, iSEE-Cell (image-based Spatial pattern ExplorEr for Cells), which features a suite of informatics tools for tissue image analysis, visualization, exploration and spatial modeling at the single-cell level. This proposed Administrative Supplement application in support of collaboration between IMAT and ITCR-funded projects aims to develop deep learning-based methods to identify subtypes of CTCs from optical microscope images. The rationale underlying this proposal is that the development of deep learning methods will provide automatic characterization and classification of CTC captured on HU structured microchips. This proposed collaborative project will leverage the technologies developed by both projects, which will bring together and enhance the capabilities of complementary technology platforms and methodologies to advance cancer research. Innovation of the proposed methods include the following: 1) Identification of multiple subtypes of CTCs using their location information on an HU microchip without destructive immunostaining analysis; 2) Novel Restore-GAN model to improve quality of microscope image obtained in CTC capture experiments and enhance predication accuracy for CTC subtypes; 3) The proposed informatics tools will provide computer-assisted automated tools to empower CTC research with artificial intelligence. Specific aims include: Aim 1: Using the microscope images and analysis/prediction results (from the IMAT project) as data input to test whether algorithms to classify different types of cell from tumor tissue images (iSEE-Cell, developed in the ICTR project) can be applied for microscope images; Aim 2: Apply novel computational methods (Restore- GAN, developed in the ICTR project) to improve image quality of the images obtained from the IMAT project, and test whether they can improve prediction accuracy for CTC subtypes; Aim 3: Develop a user-friendly interface to incorporate the iSEE-Cell platform for analyzing optical/fluorescent microscope images remotely. The ability to automatically extract/analyze information from captured cells in the microscope images is urgently needed and will dramatically enhance the throughput and work efficiency of the IMAT project.


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 = $0 )
2024	2022	TEXAS TECH UNIVERSITY SYSTEM	2500 BROADWAY	LUBBOCK	TX	79409	LUBBOCK	USA	Cancer Biology Research	001	1	7/25/2024	SUPPLEMENT FOR EXPANSION	$0
2024	2020	TEXAS TECH UNIVERSITY SYSTEM	2500 BROADWAY	LUBBOCK	TX	79409	LUBBOCK	USA	Cancer Biology Research	000	1	7/25/2024	NEW	$0
														Subtotal = $0

Issue Date FY: 2023 ( Subtotal = $0 )
2023	2020	TEXAS TECH UNIVERSITY SYSTEM	2500 BROADWAY	LUBBOCK	TX	79409	LUBBOCK	USA	Cancer Biology Research	000	1	6/13/2023	NEW	$0
														Subtotal = $0

Issue Date FY: 2022 ( Subtotal = $71,919 )
2022	2022	TEXAS TECH UNIVERSITY SYSTEM	2500 BROADWAY	LUBBOCK	TX	79409	LUBBOCK	USA	Cancer Biology Research	000	1	9/8/2022	SUPPLEMENT FOR EXPANSION	$71,919
														Subtotal = $71,919

Issue Date FY: 2020 ( Subtotal = $553,092 )
2020	2020	TEXAS TECH UNIVERSITY SYSTEM	2500 BROADWAY	LUBBOCK	TX	79409	LUBBOCK	USA	Cancer Biology Research	000	1	8/7/2020	NEW	$553,092
														Subtotal = $553,092

Grand Total All Awards = $625,011

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IMAT-ITCR Collaboration: Develop deep learning-based methods to identify subtypes of circulating tumor cells from optical microscope images

Award Number: R21CA240185

ORGANIZATION: NATIONAL CANCER INSTITUTE

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

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

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