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

> **NIH NIH R01** · MASSACHUSETTS GENERAL HOSPITAL · 2020 · $671,823

## Abstract

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.

## Key facts

- **NIH application ID:** 9853761
- **Project number:** 5R01CA218187-03
- **Recipient organization:** MASSACHUSETTS GENERAL HOSPITAL
- **Principal Investigator:** Ciprian Catana
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $671,823
- **Award type:** 5
- **Project period:** 2018-02-21 → 2023-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9853761

## Citation

> US National Institutes of Health, RePORTER application 9853761, Multimodal MR-PET Machine Learning Approaches for Primary Prostate Cancer Characterization (5R01CA218187-03). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9853761. Licensed CC0.

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