The prostate remains the only organ in which blind untargeted biopsies are conducted without a pre-identified suspected focus of neoplasm. Men with palpable abnormalities or elevated prostate specific antigen must endure trans-rectal biopsy with related costs, discomfort, stress, and complications, because it is not impossible to objectively and reliably identify who does not require a biopsy. Well over 500,000 men in the US with no evidence of prostate cancer still undergo prostate biopsies, solely on account of PSA, a grade D test according to the USPTF. Consequently there is clearly an unmet need to develop both better imaging techniques and image analysis algorithms that can enable improved non-invasive characterization of prostate cancer and distinguish low grade indolent cancers from the more aggressive intermediate to high grade variants. This would help channel and monitor appropriate patients in less aggressive treatment protocols such as active surveillance. Currently MRI is excellent for detecting high grade prostate cancer (PCa), but is less accurate for low and intermediate grade disease. Definitive exclusion of disease, and thus the need for biopsy in a subset of patients, is not possible. Also, patients who opt for active surveillance cannot be followed by imaging alone and require repeated periodic biopsy. Magnetic resonance fingerprinting (MRF) is a framework pioneered by our team for simultaneously quantifying multiple tissue properties with MRI, and has been used to quantify T1 and T2 more efficiently, accurately, and precisely than previously possible. Extensive preliminary data show the utility of this technology in combination with apparent diffusion coefficient (ADC) mapping, to separate normal peripheral zone from potential cancer. In parallel our team has been developing and validating computerized decision support (CDS) tools which can diagnose, grade, and characterize PCa both in the peripheral and transitional zones on MRI. We propose to develop an MRF exam for prostate cancer that allows simultaneous mapping of T1, T2, and ADC for efficient and quantitative separation of PCa from normal prostate and to separate low risk and more aggressive disease. We will also develop integrated CDS tools to identify additional image derived features (radiomics) from the MRF derived maps to complement MRF measurements for more accurate detection and grading of PCa both in the peripheral and transitional zones. The accuracy of this combined MRF+CDS exam will be prospectively validated in a cohort of 250 men scanned prior to biopsy.

Public Health Relevance

Project Relevance We will develop a combined quantitative MRI and computerized decision support exam for more accurate detection and grading of prostate cancer both in the peripheral and transitional zones. This combined exam will make it possible to quantitatively diagnose prostate cancer and thus preclude biopsy in men who do not have the disease, and also provide an estimate of tumor grade to enable development of better surveillance protocols.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
1R01CA208236-01A1
Application #
9331273
Study Section
Special Emphasis Panel (ZRG1-DTCS-A (81)S)
Program Officer
Redmond, George O
Project Start
2017-03-02
Project End
2022-02-28
Budget Start
2017-03-02
Budget End
2018-02-28
Support Year
1
Fiscal Year
2017
Total Cost
$550,728
Indirect Cost
$193,483
Name
Case Western Reserve University
Department
Radiation-Diagnostic/Oncology
Type
Schools of Medicine
DUNS #
077758407
City
Cleveland
State
OH
Country
United States
Zip Code
44106
Corredor, Germán; Wang, Xiangxue; Zhou, Yu et al. (2018) Spatial architecture and arrangement of tumor-infiltrating lymphocytes for predicting likelihood of recurrence in early-stage non-small cell lung cancer. Clin Cancer Res :
Beig, Niha; Khorrami, Mohammadhadi; Alilou, Mehdi et al. (2018) Perinodular and Intranodular Radiomic Features on Lung CT Images Distinguish Adenocarcinomas from Granulomas. Radiology :180910
Orooji, Mahdi; Alilou, Mehdi; Rakshit, Sagar et al. (2018) Combination of computer extracted shape and texture features enables discrimination of granulomas from adenocarcinoma on chest computed tomography. J Med Imaging (Bellingham) 5:024501
Obmann, Verena C; Pahwa, Shivani; Tabayayong, William et al. (2018) Diagnostic Accuracy of a Rapid Biparametric MRI Protocol for Detection of Histologically Proven Prostate Cancer. Urology 122:133-138
Cruz-Roa, Angel; Gilmore, Hannah; Basavanhally, Ajay et al. (2018) High-throughput adaptive sampling for whole-slide histopathology image analysis (HASHI) via convolutional neural networks: Application to invasive breast cancer detection. PLoS One 13:e0196828
Shiradkar, Rakesh; Ghose, Soumya; Jambor, Ivan et al. (2018) Radiomic features from pretreatment biparametric MRI predict prostate cancer biochemical recurrence: Preliminary findings. J Magn Reson Imaging 48:1626-1636
Nirschl, Jeffrey J; Janowczyk, Andrew; Peyster, Eliot G et al. (2018) A deep-learning classifier identifies patients with clinical heart failure using whole-slide images of H&E tissue. PLoS One 13:e0192726
Peyster, Eliot G; Madabhushi, Anant; Margulies, Kenneth B (2018) Advanced Morphologic Analysis for Diagnosing Allograft Rejection: The Case of Cardiac Transplant Rejection. Transplantation 102:1230-1239
Penzias, Gregory; Singanamalli, Asha; Elliott, Robin et al. (2018) Identifying the morphologic basis for radiomic features in distinguishing different Gleason grades of prostate cancer on MRI: Preliminary findings. PLoS One 13:e0200730
Whitney, Jon; Corredor, German; Janowczyk, Andrew et al. (2018) Quantitative nuclear histomorphometry predicts oncotype DX risk categories for early stage ER+ breast cancer. BMC Cancer 18:610

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