Diagnosis Of Prostate Cancer Using Diffusion Mri And Dynamic Contrast Enhanced Mri

MRI Atlas of Prostate Cancer analyses high-resolution MRI scanning and dynamic contrast-enhanced (DCE) MRI. This combination improves the diagnosis and staging of prostate cancer and may soon replace PSA testing and digital rectal examination. The first two chapters focus on normal anatomy, anatomic variations, benign disease and intraprostatic tumors. The subsequent chapters on MRI of extracapsular disease create a useful atlas of pathologic anatomy. This is the first text of its kind to show color-coded DCE-MRI scans of prostate cancer and to correlate these imaging findings with tumor grading. The chapters on the post-treatment prostate clearly display the increasing incidence of post-therapy recurrences. This book is intended for internists, radiologists, radiotherapists, oncologists, urologists, family practitioners, and general surgeons. Ultrasound, MRI, and radiotherapy technicians will find it extremely useful as a reference guide.

This book covers novel strategies and state of the art approaches for automated non-invasive systems for early prostate cancer diagnosis. Prostate cancer is the most frequently diagnosed malignancy after skin cancer and the second leading cause of cancer related male deaths in the USA after lung cancer. However, early detection of prostate cancer increases chances of patients’ survival. Generally, The CAD systems analyze the prostate images in three steps: (i) prostate segmentation; (ii) Prostate description or feature extraction; and (iii) classification of the prostate status. Explores all of the latest research and developments in state-of-the art imaging of the prostate from world class experts. Contains a comprehensive overview of 2D/3D Shape Modeling for MRI data. Presents a detailed examination of automated segmentation of the prostate in 3D imaging. Examines Computer-Aided-Diagnosis through automated techniques. There will be extensive references at the end of each chapter to enhance further study.

This book constitutes the refereed proceedings of the 25th Conference on Medical Image Understanding and Analysis, MIUA 2021, held in July 2021. Due to COVID-19 pandemic the conference was held virtually. The 32 full papers and 8 short papers presented were carefully reviewed and selected from 77 submissions. They were organized according to following topical sections: biomarker detection; image registration, and reconstruction; image segmentation; generative models, biomedical simulation and modelling; classification; image enhancement, quality assessment, and data privacy; radiomics, predictive models, and quantitative imaging.

Learn the fundamental principles and clinical applications of the diagnostic and risk assessment imaging test of choice for prostate cancer: prostate MRI Although prostate cancer is the second leading cause of cancer death in men in the USA, it can be treated successfully if detected early. Disease management has gradually changed to a paradigm that relies on close monitoring through active surveillance in select patients, as well as ongoing refinements in treatment interventions, including minimally invasive procedures. This has resulted in a critical need for a more exacting methodology for performing targeted biopsies, assessing risk levels, and devising treatment strategies. Prostate MRI has emerged as the most precise, state-of-the-art imaging modality for prostate cancer diagnosis and management, thereby creating an immediate demand for radiologists to become proficient in its use. Conceived and edited by a leading authority, with contributions from renowned experts in the field, MRI of the Prostate: A Practical Approach is the first book to tackle this important topic. It provides an overview of the fundamentals of prostate MRI acquisition, interpretation, and reporting. Readers will benefit from a wide range of insightful perspectives gleaned from years of hands-on experience. Key Highlights Prostate Imaging Reporting and Data System (PI-RADS) for prostate MRI interpretation and cancer risk scoring Clinical pearls on the optimization and application of prostate MRI for risk assessment, disease staging, MRI-targeted biopsy, recurrent disease, and active surveillance The emerging utilization of PET and PET/MRI for primary prostate cancer evaluation More than 700 illustrations with one entirely image-based chapter featuring educational case studies Radiologists will learn how to optimally perform and interpret prostate MRI, and referring physicians will learn to integrate it into day-to-day practice. This book is an essential resource for radiologists and radiology residents, as well as urologists, oncologists, MRI technicians, and other medical practitioners who treat patients with genitourinary disorders.

Learn the fundamental principles and clinical applications of the diagnostic and risk assessment imaging test of choice for prostate cancer: prostate MRI Although prostate cancer is the second leading cause of cancer death in men in the USA, it can be treated successfully if detected early. Disease management has gradually changed to a paradigm that relies on close monitoring through active surveillance in select patients, as well as ongoing refinements in treatment interventions, including minimally invasive procedures. This has resulted in a critical need for a more exacting methodology for performing targeted biopsies, assessing risk levels, and devising treatment strategies. Prostate MRI has emerged as the most precise, state-of-the-art imaging modality for prostate cancer diagnosis and management, thereby creating an immediate demand for radiologists to become proficient in its use. Conceived and edited by a leading authority, with contributions from renowned experts in the field, MRI of the Prostate: A Practical Approach is the first book to tackle this important topic. It provides an overview of the fundamentals of prostate MRI acquisition, interpretation, and reporting. Readers will benefit from a wide range of insightful perspectives gleaned from years of hands-on experience. Key Highlights Prostate Imaging Reporting and Data System (PI-RADS) for prostate MRI interpretation and cancer risk scoring Clinical pearls on the optimization and application of prostate MRI for risk assessment, disease staging, MRI-targeted biopsy, recurrent disease, and active surveillance The emerging utilization of PET and PET/MRI for primary prostate cancer evaluation More than 700 illustrations with one entirely image-based chapter featuring educational case studies Radiologists will learn how to optimally perform and interpret prostate MRI, and referring physicians will learn to integrate it into day-to-day practice. This book is an essential resource for radiologists and radiology residents, as well as urologists, oncologists, MRI technicians, and other medical practitioners who treat patients with genitourinary disorders.

In this issue of Radiologic Clinics, guest editors Drs. Nicola Schieda and Andrei Purysko bring their considerable expertise to the topic of Prostate Imaging. Worldwide, prostate cancer is the second most common cancer diagnosed in males, and MRI scans can give doctors a very clear picture of the prostate and nearby areas. In this issue, top experts discuss key topics such as a contemporary approach to prostate imaging and data reporting system score 3 lesions; the expanding role of MRI and use of PRECISE criteria in the active surveillance of prostate cancer; MRI-guided prostate cancer therapies; and more. Contains 12 relevant, practice-oriented topics, including update on optimization of prostate MRI technique and image quality; prostate imaging and data reporting system (PI-RADS) Version 2.1 pitfalls in prostate MRI interpretation; prostate cancer staging with MRI; PSMA PET: pearls and pitfalls; and more. Provides in-depth clinical reviews on prostate imaging, offering actionable insights for clinical practice. Presents the latest information on this timely, focused topic under the leadership of experienced editors in the field. Authors synthesize and distill the latest research and practice guidelines to create clinically significant, topic-based reviews.

This atlas provides a comprehensive, state of the art review of the use of multiparametric MRI (mpMRI) for the imaging of prostate cancer, covering aspects from diagnosis and loco-regional staging through to the role of the technique after treatment and follow-up. The book contains a wealth of high-resolution images, many of them in color, and displays the anatomical-MRI–pathological correlation whenever appropriate. Readers will find a helpful overview on the current standardized method for reading and reporting on mpMRI, the Prostate Imaging Reporting and Data System (PI-RADS), version 2. Dedicated chapters focus on differential diagnosis and imaging pitfalls, and the inclusion of helpful diagrams and algorithms will further assist in image interpretation, enabling readers to ease and improve their use of mpMRI. Edited and written by very experienced radiologists, pathologists, and urologists; the Atlas of Multiparametric Prostate MRI will serve as a unique source of clinically relevant information and an aid to disease management for radiologists, urologists, pathologists, radiotherapists, and oncologists.

Prostate cancer (PCa) is one of the most common cancer-related diseases among men in the United States. Multi-parametric magnetic resonance imaging (mp-MRI) is considered the best non-invasive imaging modality for diagnosing PCa. The core components of mp-MRI include T2-weighted imaging (T2w), diffusion-weighted imaging (DWI), and dynamic contrast-enhanced imaging (DCE), each of which provides distinct anatomical or functional information. However, mp-MRI for PCa diagnosis is currently limited by the qualitative or semi-quantitative interpretation criteria, leading to inter-reader variability and a suboptimal ability to assess lesion aggressiveness. Deep learning is a class of methods designed to automatically learn multi-layer artificial neural networks from the training data for various tasks, including image classification, object detection, and segmentation. Here, deep learning methods specific to multi-parametric imaging were proposed to detect, segment PCa lesion and assess the lesion aggressiveness. In addition, an alternative learning method using unannotated dataset was designed, due to the inaccessibility of accurate annotated dataset in many institutions.

Prostate cancer is the second most fatal cancer experienced by American males. The average American male has a 16.15% chance of developing prostate cancer, which is 8.38% higher than lung cancer, the second most likely cancer. The current in-vitro techniques that are based on analyzing a patients blood and urine have several limitations concerning their accuracy. In addition, the prostate Specific Antigen (PSA) blood-based test, has a high chance of false positive diagnosis, ranging from 28%-58%. Yet, biopsy remains the gold standard for the assessment of prostate cancer, but only as the last resort because of its invasive nature, high cost, and potential morbidity rates. The major limitation of the relatively small needle biopsy samples is the higher possibility of producing false positive diagnosis. Moreover, the visual inspection system (e.g., Gleason grading system) is not quantitative technique and different observers may classify a sample differently, leading to discrepancies in the diagnosis. As reported in the literature that the early detection of prostate cancer is a crucial step for decreasing prostate cancer related deaths. Thus, there is an urgent need for developing objective, non-invasive image based technology for early detection of prostate cancer. The objective of this dissertation is to develop a computer vision methodology, later translated into a clinically usable software tool, which can improve sensitivity and specificity of early prostate cancer diagnosis based on the well-known hypothesis that malignant tumors are will connected with the blood vessels than the benign tumors. Therefore, using either Diffusion Weighted Magnetic Resonance imaging (DW-MRI) or Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI), we will be able to interrelate the amount of blood in the detected prostate tumors by estimating either the Apparent Diffusion Coefficient (ADC) in the prostate with the malignancy of the prostate tumor or perfusion parameters. We intend to validate this hypothesis by demonstrating that automatic segmentation of the prostate from either DW-MRI or DCE-MRI after handling its local motion, provides discriminatory features for early prostate cancer diagnosis. The proposed CAD system consists of three majors components, the first two of which constitute new research contributions to a challenging computer vision problem. The three main components are: (1) A novel Shape-based segmentation approach to segment the prostate from either low contrast DW-MRI or DCE-MRI data; (2) A novel iso-contours-based non-rigid registration approach to ensure that we have voxel-on-voxel matches of all data which may be more difficult due to gross patient motion, transmitted respiratory effects, and intrinsic and transmitted pulsatile effects; and (3) Probabilistic models for the estimated diffusion and perfusion features for both malignant and benign tumors. Our results showed a 98% classification accuracy using Leave-One-Subject-Out (LOSO) approach based on the estimated ADC for 30 patients (12 patients diagnosed as malignant; 18 diagnosed as benign). These results show the promise of the proposed image-based diagnostic technique as a supplement to current technologies for diagnosing prostate cancer.