MRI in Prostate Cancer

Ghafoori, Mahyar; Alavi, Manijeh; Ghasabeh, Mounes Aliyari

doi:10.5812/ircmj.16620

Cited by 7 publications

(3 citation statements)

References 75 publications

(70 reference statements)

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“…Prostate cancer (PCa) is the most common solid noncutaneous cancer in American men and the second most common causing cancer-related death [1]. Multiparametric MRI (mpMRI) has shown promising results for the detection and staging for clinically significant PCa [2], [3].…”

Section: Introductionmentioning

confidence: 99%

Automatic Prostate Zonal Segmentation Using Fully Convolutional Network With Feature Pyramid Attention

et al. 2019

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Our main objective is to develop a novel deep learning-based algorithm for automatic segmentation of prostate zone and to evaluate the proposed algorithm on an additional independent testing data in comparison with inter-reader consistency between two experts. With IRB approval and HIPAA compliance, we designed a novel convolutional neural network (CNN) for automatic segmentation of the prostatic transition zone (TZ) and peripheral zone (PZ) on T2-weighted (T2w) MRI. The total study cohort included 359 patients from two sources; 313 from a deidentified publicly available dataset (SPIE-AAPM-NCI PROSTATEX challenge) and 46 from a large U.S. tertiary referral center with 3T MRI (external testing dataset (ETD)). The TZ and PZ contours were manually annotated by research fellows, supervised by genitourinary (GU) radiologists. The model was developed using 250 patients and tested internally using the remaining 63 patients from the PROSTATEX (internal testing dataset (ITD)) and tested again (n=46) externally using the ETD. The Dice Similarity Coefficient (DSC) was used to evaluate the segmentation performance. DSCs for PZ and TZ were 0.74±0.08 and 0.86±0.07 in the ITD respectively. In the ETD, DSCs for PZ and TZ were 0.74±0.07 and 0.79±0.12, respectively. The inter-reader consistency (Expert 2 vs. Expert 1) were 0.71±0.13 (PZ) and 0.75±0.14 (TZ). This novel DL algorithm enabled automatic segmentation of PZ and TZ with high accuracy on both ITD and ETD without a performance difference for PZ and less than 10% TZ difference. In the ETD, the proposed method can be comparable to experts in the segmentation of prostate zones.

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Section: Introductionmentioning

confidence: 99%

Automatic Prostate Zonal Segmentation Using Fully Convolutional Network With Feature Pyramid Attention

et al. 2019

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“…Therefore, vigilant surveillance for prostate cancer is indicated in men over 40 years of age (62). 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).…”

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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“…Prostate cancer (PCa) is the most common solid organ malignancy and is among the most common causes of cancer-related death among men in the United States [1]. Multi-parametric MRI (mpMRI) is the most widely available non-invasive and sensitive tool for the detection of clinically significant PCa (csPCa), 70% and 30% of which are located in the peripheral zone (PZ) and transition zone (TZ) respectively [2], [3].…”

Section: Introductionmentioning

confidence: 99%

Exploring Uncertainty Measures in Bayesian Deep Attentive Neural Networks for Prostate Zonal Segmentation

et al. 2020

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Automatic segmentation of prostatic zones on multi-parametric MRI (mpMRI) can improve the diagnostic workflow of prostate cancer. We designed a spatial attentive Bayesian deep learning network for the automatic segmentation of the peripheral zone (PZ) and transition zone (TZ) of the prostate with uncertainty estimation. The proposed method was evaluated by using internal and external independent testing datasets, and overall uncertainties of the proposed model were calculated at different prostate locations (apex, middle, and base). The study cohort included 351 MRI scans, of which 304 scans were retrieved from a de-identified publicly available datasets (PROSTATEX) and 47 scans were extracted from a large U.S. tertiary referral center (external testing dataset; ETD)). All the PZ and TZ contours were drawn by research fellows under the supervision of expert genitourinary radiologists. Within the PROSTATEX dataset, 259 and 45 patients (internal testing dataset; ITD) were used to develop and validate the model. Then, the model was tested independently using the ETD only. The segmentation performance was evaluated using the Dice Similarity Coefficient (DSC). For PZ and TZ segmentation, the proposed method achieved mean DSCs of 0.80±0.05 and 0.89±0.04 on ITD, as well as 0.79±0.06 and 0.87±0.07 on ETD. For both PZ and TZ, there was no significant difference between ITD and ETD for the proposed method. This DL-based method enabled the accuracy of the PZ and TZ segmentation, which outperformed the state-of-art methods (Deeplab V3+, Attention U-Net, R2U-Net, USE-Net and U-Net). We observed that segmentation uncertainty peaked at the junction between PZ, TZ and AFS. Also, the overall uncertainties were highly consistent with the actual model performance between PZ and TZ at three clinically relevant locations of the prostate.

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MRI in Prostate Cancer

Cited by 7 publications

References 75 publications

Automatic Prostate Zonal Segmentation Using Fully Convolutional Network With Feature Pyramid Attention

Automatic Prostate Zonal Segmentation Using Fully Convolutional Network With Feature Pyramid Attention

Hematospermia Evaluation at MR Imaging

Exploring Uncertainty Measures in Bayesian Deep Attentive Neural Networks for Prostate Zonal Segmentation

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