Anatomic Imaging of the Prostate

Bhavsar, Anil; Verma, Sadhna

doi:10.1155/2014/728539

Cited by 56 publications

(59 citation statements)

References 20 publications

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“…The T 2 W images were segmented first to provide a better delineation of the zonal anatomy for later segmentation on ADC map images where the zonal anatomy is subjectively less well depicted than on T 2 W images . In addition to segmentation of the PZ, radiologists also divided and labeled the segmentations into the PZ base, middle, and apical regions, as described previously . Lastly, for patients with significant tumors identified at RP, radiologists segmented tumors using existing mp‐MRI‐RP maps on both T 2 W and ADC map images.…”

Section: Methodsmentioning

confidence: 99%

“…16 In addition to segmentation of the PZ, radiologists also divided and labeled the segmentations into the PZ base, middle, and apical regions, as described previously. 17,18 Lastly, for patients with significant tumors identified at RP, radiologists segmented tumors using existing mp-MRI-RP maps on both T 2 W and ADC map images. An example of manual segmentation on the T 2 W and ADC map MR image along with their manual segmentation are shown in Fig.…”

Section: Manual Segmentation Of Prostate Boundaries and Tumorsmentioning

confidence: 99%

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Fully automated localization of prostate peripheral zone tumors on apparent diffusion coefficient map MR images using an ensemble learning method

Zabihollahy

Ukwatta

Krishna

et al. 2019

Magnetic Resonance Imaging

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Background Accurate detection and localization of prostate cancer (PCa) in men undergoing prostate MRI is a fundamental step for future targeted prostate biopsies and treatment planning. Fully automated localization of peripheral zone (PZ) PCa using the apparent diffusion coefficient (ADC) map might be clinically useful. Purpose/Hypothesis To describe automated localization of PCa in the PZ on ADC map MR images using an ensemble U‐Net‐based model. Study Type Retrospective, case–control. Population In all, 226 patients (154 and 72 patients with and without clinically significant PZ PCa, respectively), training, and testing was performed using dataset images of 146 and 80 patients, respectively. Field Strength 3T, ADC maps. Sequence ADC map. Assessment The ground truth was established by manual delineation of the prostate and prostate PZ tumors on ADC maps by dedicated radiologists using MRI‐radical prostatectomy maps as a reference standard. Statistical Tests: Performance of the ensemble model was evaluated using Dice similarity coefficient (DSC), sensitivity, and specificity metrics on a per‐slice basis. Receiver operating characteristic (ROC) curve and area under the curve (AUC) were employed as well. The paired t‐test was used to test the differences between the performances of constituent networks of the ensemble model. Results Our developed algorithm yielded DSC, sensitivity, and specificity of 86.72% ± 9.93%, 85.76% ± 23.33%, and 76.44% ± 23.70%, respectively (mean ± standard deviation) on 80 test cases consisting of 41 and 39 instances from patients with and without clinically significant tumors including 660 extracted 2D slices. AUC was reported as 0.779. Data Conclusion An ensemble U‐Net‐based approach can accurately detect and segment PCa in the PZ from ADC map MR prostate images. Level of Evidence: 4 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2020;51:1223–1234.

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Section: Methodsmentioning

confidence: 99%

Section: Manual Segmentation Of Prostate Boundaries and Tumorsmentioning

confidence: 99%

Fully automated localization of prostate peripheral zone tumors on apparent diffusion coefficient map MR images using an ensemble learning method

Zabihollahy

Ukwatta

Krishna

et al. 2019

Magnetic Resonance Imaging

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“…The prostate gland is the biggest accessory organ of the male regenerative framework. It secretes a thin, marginally soluble fluid that forms a segment of the seminal fluid 64 .…”

Section: Fig 2: Computed Tomography Images Of 30 Year Male's Bladdermentioning

confidence: 99%

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2018

IJPSR

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Now a day's urogenital cancer is the life threatening disease in worldwide, among them most four life threatening urogenital cancers are kidney cancer, bladder cancer, prostate cancer and testicular cancer. Kidney cancer or renal cell carcinoma (also called adenocarcinoma) is a disease in which malignant cancer cells are found in the living of tubules (renal epithelium). The kidney cancer has the highest mortality rate of the genito urinary cancers and the incidence of kidney cancer has risen steadily. Bladder cancer is a disease in which malignant cancer cells form in the tissues of the bladder. The bladder is a hollow organ in the lower part of the abdomen. Bladder cancer is seventh most basic cancer worldwide and fourth basic growth in men. Prostate cancer is a disease in which cancer cells shape in the tissues of the prostate. Prostate cancer is the fourth most common cancer in the world and more than 1.1 million cases of prostate cancer recorded in 2012. Testicular cancer is a disease when testicular cells become abnormal (malignant) in one or both testicles. It is a relatively rare tumour, approximately 1% of all male cancers globally. So, drugs are not only the choice for the management of cancers is required peoples/ patients' awareness, a way of living education is the best choice for the prevention of cancers. INTRODUCTION: Urogenital cancers are the life threatening disease in worldwide, among them kidney cancer, bladder cancer, prostate cancer and testicular cancer are four most life threatening urogenital cancers. The kidney cancer has the highest mortality rate of the Genito urinary cancers and the incidence of kidney cancer has risen steadily.

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“…The central zone is a layer of tissue surrounding the ejaculatory ducts from the prostatic base to verumontanum. The central zone originates from the Wolffian duct, whereas the transition and peripheral zones are derived from the urogenital sinus (60). Due to the subsequent compression of the peripheral zone, a pseudocapsule, or surgical capsule, of the prostate, is formed.…”

Section: Bphmentioning

confidence: 99%

“…Prostate cancer is the most commonly diagnosed malignancy and second-leading cause of death in men in the United States (63). Approximately 70% of prostate cancers arise in the peripheral zone, 25% in the transition zone, and 5% in the central zone (60,64). At MR imaging, it is of low signal intensity at T2-weighted imaging and of intermediate intensity at T1-weighted imaging, relative to normal regions (Fig 20) (2).…”

Section: Neoplastic Disordersmentioning

confidence: 99%

Hematospermia Evaluation at MR Imaging

Mittal¹,

Camacho²,

Sahani

et al. 2016

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Hematospermia is a challenging and anxiety-provoking condition that can manifest as a single episode or recur over the course of weeks to months. It is usually a benign self-limiting condition in younger sexually active males without a history of risk factors such as cancer, urogenital malformations, bleeding disorders, and their associated symptoms. However, patients with recurrent, refractory and painful hematospermia with associated symptoms, such as fever, pain, or weight loss, require evaluation through clinical assessment and noninvasive investigations to rule out underlying pathologic conditions such as ejaculatory obstruction, infectious and inflammatory causes, malignancy, vascular malformations, and systemic disorders that increase the risk of bleeding, especially when presenting in older men. If these investigations are negative, the patient should be reassured and treated accordingly. In the recent past, magnetic resonance (MR) imaging has assumed a major role in the investigation of hematospermia due to its excellent soft-tissue contrast and multiplanar capabilities. In this review, we will discuss the potential causes of hematospermia and its diagnostic workup, including pathophysiology, anatomic considerations, the imaging appearance of associated pathologic conditions, and management. (©)RSNA, 2016.

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Anatomic Imaging of the Prostate

Cited by 56 publications

References 20 publications

Fully automated localization of prostate peripheral zone tumors on apparent diffusion coefficient map MR images using an ensemble learning method

Fully automated localization of prostate peripheral zone tumors on apparent diffusion coefficient map MR images using an ensemble learning method

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Hematospermia Evaluation at MR Imaging

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