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

用于原发性前列腺癌表征的多模态 MR-PET 机器学习方法

基本信息

批准号：
10358651
负责人：
Ciprian Catana
金额：
$ 68.22万
依托单位：
MASSACHUSETTS GENERAL HOSPITAL
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-02-21 至 2024-01-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10358651
关键词：
3-Dimensional Address Affect Algorithms Anatomy Biological Biopsy Cancer Death Rates Cancer Patient Classification Computer software Data Data Set Devices Diagnosis Diagnostic Discrimination Disease Early Diagnosis Early treatment FOLH1 gene Gold Guidelines Hand Histopathology Hybrids Image Individual Interobserver Variability Kinetics Lesion Life Expectancy Ligands Machine Learning Magnetic Resonance Imaging Malignant Neoplasms Malignant neoplasm of prostate Maps Measurement Meta-Analysis Metabolic Methodology Methods Modality Modeling Morphology PSA level Patient Selection Patients Pelvis Performance Phenotype Physiological Positron-Emission Tomography Prostate Quality of life Radical Prostatectomy Reproducibility Risk Scanning Source Testing Time Translations Work anticancer research attenuation base bone imaging cancer classification cancer imaging clinical application clinically relevant clinically significant data acquisition deep learning diagnostic accuracy human subject imaging modality improved industry partner machine learning model men multimodal data multimodality new technology non-invasive imaging radiologist radiomics radiotracer tool transmission process tumor

项目摘要

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.

项目总结