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Multimodal MR-PET Machine Learning Approaches for Primary Prostate Cancer Characterization

Award Number: R01CA218187
ORGANIZATION: NATIONAL CANCER INSTITUTE
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 02/21/2018
PERIOD OF PERFORMANCE END DATE: 01/31/2025

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Multimodal MR-PET Machine Learning Approaches for Primary Prostate Cancer Characterization - Project Summary Prostate cancer (PCa) is the most diagnosed form of non-cutaneous cancer in US men. The selection of patients who require immediate treatment from those suitable for active surveillance currently relies on non- specific and inaccurate measurements. A method that allows clinicians to more confidently discriminate clinically relevant from non-life-threatening tumors is needed to improve patient management. Multiparametric magnetic resonance imaging (mpMRI) is the preferred non-invasive imaging modality for characterizing primary PCa. However, its accuracy for detecting clinically significant PCa is variable. We propose to address this limitation by combining mpMRI with positron emission tomography (PET) with a PCa-specific radiotracer and using advanced multimodal machine learning models (i.e. radiomics and deep learning) to characterize tumor aggressiveness based on the imaging data. Recently, scanners capable of simultaneous PET and MR data acquisition in human subjects have become commercially available. An integrated MR-PET scanner is the ideal tool for comparing MR and PET derived image features to identify those that provide complementary information and build a hybrid PET-mpMRI model that most accurately identifies clinically significant tumors. While this novel technology allows the acquisition of perfectly coregistered complementary anatomical, functional and metabolic data in a single imaging session, a new challenge needs to first be addressed to obtain quantitatively accurate PET data. In an integrated MR-PET scanner, the information needed for PET attenuation correction (AC) has to be derived from the MR data and the methods currently available for this task are inadequate for advanced quantitative studies. We have formed an academic-industrial partnership to accelerate the translation of multimodal MR-PET machine learning approaches into PCa research and clinical applications by addressing the AC challenge and validating machine learning models for detecting clinically significant disease against gold standard histopathology in patients undergoing radical prostatectomy. Specifically, we will: (1) Develop and validate an MR-based approach for obtaining quantitatively accurate PET data. We hypothesize that attenuation maps as accurate as those obtained using a 511 keV transmission source – the true gold standard for PET AC – will be obtained; (2) Identify the multimodal radiomics model that most accurately predicts PCa aggressiveness. We hypothesize that the diagnostic accuracy of this approach will be superior to that offered by the stand-alone modalities; (3) Evaluate radiomics and deep learning approaches for predicting pPCa aggressiveness. We hypothesize that machine learning approaches will achieve a higher predictive accuracy when applied to data acquired simultaneously than sequentially.


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: 2025 ( Subtotal = -$16,092 )
2025	2023	THE GENERAL HOSPITAL CORPORATION	55 FRUIT ST	BOSTON	MA	02114	SUFFOLK	USA	Cancer Detection and Diagnosis Research	000	6	6/11/2025	NON-COMPETING CONTINUATION	-$16,092
														Subtotal = -$16,092

Issue Date FY: 2023 ( Subtotal = $230,094 )
2023	2023	THE GENERAL HOSPITAL CORPORATION	55 FRUIT ST	BOSTON	MA	02114	SUFFOLK	USA	Cancer Detection and Diagnosis Research	000	6	2/9/2023	NON-COMPETING CONTINUATION	$230,094
														Subtotal = $230,094

Issue Date FY: 2022 ( Subtotal = $682,160 )
2022	2022	MASSACHUSETTS GENERAL HOSPITAL, THE	55 FRUIT ST	BOSTON	MA	02114	SUFFOLK	USA	Cancer Detection and Diagnosis Research	001	5	6/9/2022	NON-COMPETING CONTINUATION	$55,687
2022	2022	MASSACHUSETTS GENERAL HOSPITAL, THE	55 FRUIT ST	BOSTON	MA	02114	SUFFOLK	USA	Cancer Detection and Diagnosis Research	000	5	1/27/2022	NON-COMPETING CONTINUATION	$626,473
														Subtotal = $682,160

Issue Date FY: 2021 ( Subtotal = $414,490 )
2021	2021	MASSACHUSETTS GENERAL HOSPITAL, THE	55 FRUIT ST	BOSTON	MA	02114	SUFFOLK	USA	Cancer Detection and Diagnosis Research	000	4	2/23/2021	NON-COMPETING CONTINUATION	$414,490
														Subtotal = $414,490

Issue Date FY: 2020 ( Subtotal = $671,823 )
2020	2020	MASSACHUSETTS GENERAL HOSPITAL, THE	55 FRUIT ST	BOSTON	MA	02114	SUFFOLK	USA	Cancer Detection and Diagnosis Research	000	3	2/25/2020	NON-COMPETING CONTINUATION	$671,823
														Subtotal = $671,823

Issue Date FY: 2019 ( Subtotal = $663,746 )
2019	2019	MASSACHUSETTS GENERAL HOSPITAL, THE	55 FRUIT ST	BOSTON	MA	02114	SUFFOLK	USA	Cancer Detection and Diagnosis Research	000	2	1/24/2019	NON-COMPETING CONTINUATION	$663,746
														Subtotal = $663,746

Issue Date FY: 2018 ( Subtotal = $684,119 )
2018	2018	MASSACHUSETTS GENERAL HOSPITAL, THE	55 FRUIT ST	BOSTON	MA	02114	SUFFOLK	USA	Cancer Detection and Diagnosis Research	001	1	3/20/2018	NEW	-$8,599
2018	2018	MASSACHUSETTS GENERAL HOSPITAL, THE	55 FRUIT ST	BOSTON	MA	02114	SUFFOLK	USA	Cancer Detection and Diagnosis Research	000	1	2/21/2018	NEW	$692,718
														Subtotal = $684,119

Grand Total All Awards = $3,330,340

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Multimodal MR-PET Machine Learning Approaches for Primary Prostate Cancer Characterization

Award Number: R01CA218187

ORGANIZATION: NATIONAL CANCER INSTITUTE

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

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

PERIOD OF PERFORMANCE START DATE: 02/21/2018

PERIOD OF PERFORMANCE END DATE: 01/31/2025

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