Normal 3T MR Anatomy of the Prostate Gland and Surrounding Structures

Sklinda, Katarzyna; Frączek, Michał; Mruk, Bartosz; Walecki, Jerzy

doi:10.1155/2019/3040859

Cited by 22 publications

(16 citation statements)

References 6 publications

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“…Using deep learning classifiers on MRI-extracted LSTM features to detect prostate cancer, it is evident that we have achieved (100%) specificity and positive predictive value, where the accuracy (99.48%) was obtained using the same technique. The deep learning classifiers ResNet-101 achieved the highest AUC value of (1) and MCC (1). The results also show that the LSTM classifier and ResNet-101 has a false positive rate of (0%), which is the lowest among all classifiers.…”

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confidence: 79%

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Prostate Cancer Detection Using Deep Learning and Traditional Techniques

et al. 2021

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Prostate cancer (PCa) is a severe type of cancer and causes major deaths among men due to its poor diagnostic system. The images obtained from patients with carcinoma consist of complex and necessary features that cannot be extracted readily by traditional diagnostic techniques. This research employed deep learning long short-term memory (LST M) and Residual Net (ResN et−101), independent of hand-crafted features, and is fine-tuned. The results were compared with hand-crafted features such as texture, morphology, and gray level co-occurrence matrix (GLCM) using non-deep learning classifiers such as support vector machine (SV M) Gaussian Kernel, k-nearest neighbor-Cosine (KN N − Cosine), kernel naive Bayes, decision tree (DT) and RUSBoost tree. This study reduces the features of carcinoma images, employed machine learning and deep learning approaches. For validation of training and testing data, a jack-knife tenfold cross-validation method was used. The performance was measured using a confusion matrix such as sensitivity, specificity, positive predictive value (P P V), negative predictive value (N P V), accuracy (AC), Mathews Correlation Coefficient (M CC), and area under the curve (AU C). The most remarkable performance was obtained using non-deep learning methods with GLCM features using KNN-Cosine with sensitivity (98.00%), specificity (99.25%), PPV (98.99%), NPV (99.11%), accuracy (99.07%), and AUC (0.998). The LSTM deep learning method yields performance with sensitivity (98.33%), specificity (100%), PPV (100%), NPV (99.26%), accuracy (99.48%), MCC (0.9879) and AUC (0.9999), where using Deep learning method ResN et − 101, we obtained (100%) Accuracy and AUC (1) for Kernel Naive Bayes, SVM Gaussian and RUSBoost Tree. The results show that ResN et − 101 deep learning outperformed than non-deep learning methods and LST M. Thus, the deep learning method ResN et − 101 could be used as a better predictor for the detection of prostate cancer.

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confidence: 79%

“…This study uses publicly available data-sets 1 . This dataset on T 1 images and shows the early enhancement.…”

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confidence: 99%

Prostate Cancer Detection Using Deep Learning and Traditional Techniques

et al. 2021

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“…The typical prostate, when not affected by BPH, measures approximately 3 cm x 3 cm x 5 cm [7], with 25 cm 3 representing the upper limit of normal prostatic volume [8]. In the normal prostate, the PZ comprises approximately 70% of its total volume, the CZ approximately 25%, and the TZ approximately 5% [9]. Particularly in patients with larger total prostate volumes, the enlarging TZ may cause thinning and effacement of the PZ [10].…”

Section: Anatomy Of Bphmentioning

confidence: 99%

“…Although the prostate does not histologically contain a capsule, overlapping layers of fascia surround the PZ and appear on T2WI as a hypointense line often referred to as the prostatic "capsule". An additional T2hypointense line between the TZ and PZ is considered the "surgical capsule" and, though technically a pseudocapsule, represents an important plane for surgical interventions [9].…”

Section: Anatomy Of Bphmentioning

confidence: 99%

MRI Evaluation of Patients Before and After Interventions for Benign Prostatic Hyperplasia: An Update

Diaz

Benson

Clinkenbeard

et al. 2022

American Journal of Roentgenology

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The publication of this Accepted Manuscript is provided to give early visibility to the contents of the article, which will undergo additional copyediting, typesetting, and review before it is published in its final form. During the production process, errors may be discovered that could affect the content of the Accepted Manuscript. All legal disclaimers that apply to the journal pertain. The reader is cautioned to consult the definitive version of record before relying on the contents of this document.

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“…MRI offers excellent soft tissue contrast and different prostatic zones can be reliably distinguished and accurately measured [16]. Quantification of the exact prostate volume, exclusion of prostate cancer prior to therapy of an enlarged prostate and detection of pathologies leading to LUTS are promising use cases for mpMRI that have not yet been implemented in current guidelines.…”

Section: Definitions and Guidelines For Imagingmentioning

confidence: 99%

More Than Detection of Adenocarcinoma – Indications and Findings in Prostate MRI in Benign Prostatic Disorders

Oerther

Sigle

Franiel

et al. 2022

Rofo

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Background Multiparametric MRI of the prostate has become a fundamental tool in the diagnostic pathway for prostate cancer and is recommended before (or after negative) biopsy to guide biopsy and increase accuracy, as a staging examination (high-risk setting), and prior to inclusion into active surveillance. Despite this main field of application, prostate MRI can be utilized to obtain information in a variety of benign disorders of the prostate. Methods Systematic bibliographical research with extraction of studies, national (German) as well as international guidelines (EAU, AUA), and consensus reports on MRI of benign disorders of the prostate was performed. Indications and imaging findings of prostate MRI were identified for a) imaging the enlarged prostate, b) prostate MRI in prostatic artery embolization, c) imaging in prostatitis and d) imaging in congenital anomalies. Results and Conclusions Different phenotypes of the enlarged prostate that partly correlate with severity of symptoms are discussed. We provide an overview of the different types of prostatitis and possible imaging findings, highlighting abscesses as a severe complication. The most common congenital anomalies of the prostate are utricular cysts, whereas anomalies like aplasia, hypoplasia, and ectopia are rare disorders. Knowledge of indications for imaging and imaging appearance of these conditions may improve patient care and enhance differential diagnosis. Key Points: Citation Format

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Normal 3T MR Anatomy of the Prostate Gland and Surrounding Structures

Cited by 22 publications

References 6 publications

Prostate Cancer Detection Using Deep Learning and Traditional Techniques

Prostate Cancer Detection Using Deep Learning and Traditional Techniques

MRI Evaluation of Patients Before and After Interventions for Benign Prostatic Hyperplasia: An Update

More Than Detection of Adenocarcinoma – Indications and Findings in Prostate MRI in Benign Prostatic Disorders

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