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.

National Institute of Health (NIH)
National Cancer Institute (NCI)
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Special Emphasis Panel (ZRG1)
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Redmond, George O
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Case Western Reserve University
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Alilou, Mehdi; Orooji, Mahdi; Beig, Niha et al. (2018) Quantitative vessel tortuosity: A potential CT imaging biomarker for distinguishing lung granulomas from adenocarcinomas. Sci Rep 8:15290
Antunes, Jacob; Viswanath, Satish; Brady, Justin T et al. (2018) Coregistration of Preoperative MRI with Ex Vivo Mesorectal Pathology Specimens to Spatially Map Post-treatment Changes in Rectal Cancer Onto In Vivo Imaging: Preliminary Findings. Acad Radiol 25:833-841
Leo, Patrick; Elliott, Robin; Shih, Natalie N C et al. (2018) Stable and discriminating features are predictive of cancer presence and Gleason grade in radical prostatectomy specimens: a multi-site study. Sci Rep 8:14918
Lu, Cheng; Romo-Bucheli, David; Wang, Xiangxue et al. (2018) Nuclear shape and orientation features from H&E images predict survival in early-stage estrogen receptor-positive breast cancers. Lab Invest 98:1438-1448
Beig, Niha; Patel, Jay; Prasanna, Prateek et al. (2018) Radiogenomic analysis of hypoxia pathway is predictive of overall survival in Glioblastoma. Sci Rep 8:7
Bera, Kaustav; Velcheti, Vamsidhar; Madabhushi, Anant (2018) Novel Quantitative Imaging for Predicting Response to Therapy: Techniques and Clinical Applications. Am Soc Clin Oncol Educ Book :1008-1018
Algohary, Ahmad; Viswanath, Satish; Shiradkar, Rakesh et al. (2018) Radiomic features on MRI enable risk categorization of prostate cancer patients on active surveillance: Preliminary findings. J Magn Reson Imaging :
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

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