scholarly journals DWI-related texture analysis for prostate cancer: differences in correlation with histological aggressiveness and data repeatability between peripheral and transition zones

2022 ◽  
Vol 6 (1) ◽  
Author(s):  
Chie Tsuruta ◽  
Kenji Hirata ◽  
Kohsuke Kudo ◽  
Naoya Masumori ◽  
Masamitsu Hatakenaka
Keyword(s):  
Prostate Cancer ◽  
Texture Features ◽  
Peripheral Zone ◽  
Gray Level ◽  
Grade Group ◽  
Eligibility Criteria ◽  
Pelvic Magnetic Resonance Imaging ◽  
Discrimination Ability ◽  
Diffusion Weighted Images ◽  
Adc Map

Abstract Background We investigated the correlation between texture features extracted from apparent diffusion coefficient (ADC) maps or diffusion-weighted images (DWIs), and grade group (GG) in the prostate peripheral zone (PZ) and transition zone (TZ), and assessed reliability in repeated examinations. Methods Patients underwent 3-T pelvic magnetic resonance imaging (MRI) before radical prostatectomy with repeated DWI using b-values of 0, 100, 1,000, and 1,500 s/mm2. Region of interest (ROI) for cancer was assigned to the first and second DWI acquisition separately. Texture features of ROIs were extracted from comma-separated values (CSV) data of ADC maps generated from several sets of two b-value combinations and DWIs, and correlation with GG, discrimination ability between GG of 1–2 versus 3–5, and data repeatability were evaluated in PZ and TZ. Results Forty-four patients with 49 prostate cancers met the eligibility criteria. In PZ, ADC 10% and 25% based on ADC map of two b-value combinations of 100 and 1,500 s/mm2 and 10% based on ADC map with b-value of 0 and 1,500 s/mm2 showed significant correlation with GG, acceptable discrimination ability, and good repeatability. In TZ, higher-order texture feature of busyness extracted from ADC map of 100 and 1,500 s/mm2, and high gray-level run emphasis, short-run high gray-level emphasis, and high gray-level zone emphasis from DWI with b-value of 100 s/mm2 demonstrated significant correlation, excellent discrimination ability, but moderate repeatability. Conclusions Some DWI-related features showed significant correlation with GG, acceptable to excellent discrimination ability, and moderate to good data repeatability in prostate cancer, and differed between PZ and TZ.

Prostate cancer: Utility of fusion of T2-weighted and high b-value diffusion-weighted images for peripheral zone tumor detection and localization

2011 ◽  
Vol 34 (1) ◽  
pp. 95-100 ◽  
Author(s):  
Andrew B. Rosenkrantz ◽  
Lorenzo Mannelli ◽  
Xiangtian Kong ◽  
Ben E. Niver ◽  
Douglas S. Berkman ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Peripheral Zone ◽  
B Value ◽  
Tumor Detection ◽  
Diffusion Weighted Images ◽  
High B Value ◽  
Diffusion Weighted ◽  
Detection And Localization

scholarly journals Utility of T2-weighted MRI texture analysis in assessment of peripheral zone prostate cancer aggressiveness: a single-arm, multicenter study

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Gabriel A. Nketiah ◽  
◽  
Mattijs Elschot ◽  
Tom W. Scheenen ◽  
Marnix C. Maas ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Texture Analysis ◽  
Multicenter Study ◽  
Peripheral Zone ◽  
Support Vector ◽  
Low Grade ◽  
Textural Features ◽  
Grade Group ◽  
Cancer Aggressiveness ◽  
Grade Groups

AbstractT2-weighted (T2W) MRI provides high spatial resolution and tissue-specific contrast, but it is predominantly used for qualitative evaluation of prostate anatomy and anomalies. This retrospective multicenter study evaluated the potential of T2W image-derived textural features for quantitative assessment of peripheral zone prostate cancer (PCa) aggressiveness. A standardized preoperative multiparametric MRI was performed on 87 PCa patients across 6 institutions. T2W intensity and apparent diffusion coefficient (ADC) histogram, and T2W textural features were computed from tumor volumes annotated based on whole-mount histology. Spearman correlations were used to evaluate association between textural features and PCa grade groups (i.e. 1–5). Feature utility in differentiating and classifying low-(grade group 1) vs. intermediate/high-(grade group ≥ 2) aggressive cancers was evaluated using Mann–Whitney U-tests, and a support vector machine classifier employing “hold-one-institution-out” cross-validation scheme, respectively. Textural features indicating image homogeneity and disorder/complexity correlated significantly (p < 0.05) with PCa grade groups. In the intermediate/high-aggressive cancers, textural homogeneity and disorder/complexity were significantly lower and higher, respectively, compared to the low-aggressive cancers. The mean classification accuracy across the centers was highest for the combined ADC and T2W intensity-textural features (84%) compared to ADC histogram (75%), T2W histogram (72%), T2W textural (72%) features alone or T2W histogram and texture (77%), T2W and ADC histogram (79%) combined. Texture analysis of T2W images provides quantitative information or features that are associated with peripheral zone PCa aggressiveness and can augment their classification.

scholarly journals Prediction of Clinically Significant Cancer Using Radiomics Features of Pre-Biopsy of Multiparametric MRI in Men Suspected of Prostate Cancer

Cancers ◽  
2021 ◽  
Vol 13 (24) ◽  
pp. 6199
Author(s):  
Chidozie N. Ogbonnaya ◽  
Xinyu Zhang ◽  
Basim S. O. Alsaedi ◽  
Norman Pratt ◽  
Yilong Zhang ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Quantitative Imaging ◽  
Spatial Relationship ◽  
Region Of Interest ◽  
Texture Features ◽  
Gray Level ◽  
Discriminative Ability ◽  
Significant Difference ◽  
Clinically Significant ◽  
Occurrence Matrix

Background: Texture features based on the spatial relationship of pixels, known as the gray-level co-occurrence matrix (GLCM), may play an important role in providing the accurate classification of suspected prostate cancer. The purpose of this study was to use quantitative imaging parameters of pre-biopsy multiparametric magnetic resonance imaging (mpMRI) for the prediction of clinically significant prostate cancer. Methods: This was a prospective study, recruiting 200 men suspected of having prostate cancer. Participants were imaged using a protocol-based 3T MRI in the pre-biopsy setting. Radiomics parameters were extracted from the T2WI and ADC texture features of the gray-level co-occurrence matrix were delineated from the region of interest. Radical prostatectomy histopathology was used as a reference standard. A Kruskal–Wallis test was applied first to identify the significant radiomic features between the three groups of Gleason scores (i.e., G1, G2 and G3). Subsequently, the Holm–Bonferroni method was applied to correct and control the probability of false rejections. We compared the probability of correctly predicting significant prostate cancer between the explanatory GLCM radiomic features, PIRADS and PSAD, using the area under the receiver operation characteristic curves. Results: We identified the significant difference in radiomic features between the three groups of Gleason scores. In total, 12 features out of 22 radiomics features correlated with the Gleason groups. Our model demonstrated excellent discriminative ability (C-statistic = 0.901, 95%CI 0.859–0.943). When comparing the probability of correctly predicting significant prostate cancer between explanatory GLCM radiomic features (Sum Variance T2WI, Sum Entropy T2WI, Difference Variance T2WI, Entropy ADC and Difference Variance ADC), PSAD and PIRADS via area under the ROC curve, radiomic features were 35.0% and 34.4% more successful than PIRADS and PSAD, respectively, in correctly predicting significant prostate cancer in our patients (p < 0.001). The Sum Entropy T2WI score had the greatest impact followed by the Sum Variance T2WI. Conclusion: Quantitative GLCM texture analyses of pre-biopsy MRI has the potential to be used as a non-invasive imaging technique to predict clinically significant cancer in men suspected of having prostate cancer.

scholarly journals Diagnosis and Localization of Prostate Cancer via Automated Multiparametric MRI Equipped with Artificial Intelligence

Uro ◽  
2022 ◽  
Vol 2 (1) ◽  
pp. 21-29
Author(s):  
Yuichiro Oishi ◽  
Takeya Kitta ◽  
Takahiro Osawa ◽  
Takashige Abe ◽  
Nobuo Shinohara ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Artificial Intelligence ◽  
Radical Prostatectomy ◽  
Roc Curve ◽  
Texture Features ◽  
Peripheral Zone ◽  
Multiparametric Mri ◽  
Shape Estimation ◽  
Chi Square ◽  
Mri Scans

Prostate MRI scans for pre-biopsied patients are important. However, fewer radiologists are available for MRI diagnoses, which requires multi-sequential interpretations of multi-slice images. To reduce such a burden, artificial intelligence (AI)-based, computer-aided diagnosis is expected to be a critical technology. We present an AI-based method for pinpointing prostate cancer location and determining tumor morphology using multiparametric MRI. The study enrolled 15 patients who underwent radical prostatectomy between April 2008 and August 2017 at our institution. We labeled the cancer area on the peripheral zone on MR images, comparing MRI with histopathological mapping of radical prostatectomy specimens. Likelihood maps were drawn, and tumors were divided into morphologically distinct regions using the superpixel method. Likelihood maps consisted of pixels, which utilize the cancer likelihood value computed from the T2-weighted, apparent diffusion coefficient, and diffusion-weighted MRI-based texture features. Cancer location was determined based on the likelihood maps. We evaluated the diagnostic performance by the area under the receiver operating characteristic (ROC) curve according to the Chi-square test. The area under the ROC curve was 0.985. Sensitivity and specificity for our approach were 0.875 and 0.961 (p < 0.01), respectively. Our AI-based procedures were successfully applied to automated prostate cancer localization and shape estimation using multiparametric MRI.

scholarly journals A Younger Man With Localized Prostate Cancer Asks, “Which Type of Radiation Is Right for Me?”

2018 ◽  
Vol 36 (18) ◽  
pp. 1780-1784 ◽  
Author(s):  
Justin E. Bekelman
Keyword(s):  
Prostate Cancer ◽  
Active Surveillance ◽  
Relevant Literature ◽  
Specific Antigen ◽  
Peripheral Zone ◽  
Magnetic Resonance Imaging Scan ◽  
Grade Group ◽  
Patient Reports ◽  
Surgical History ◽  
The Right

The Oncology Grand Rounds series is designed to place original reports published in the Journal into clinical context. A case presentation is followed by a description of diagnostic and management challenges, a review of the relevant literature, and a summary of the authors’ suggested management approaches. The goal of this series is to help readers better understand how to apply the results of key studies, including those published in Journal of Clinical Oncology, to patients seen in their own clinical practice. A 61-year-old man presents with stage II prostate cancer after a period of active surveillance. Work-up reveals T1cN0M0 carcinoma, a prostate-specific antigen (PSA) level of 4.8 ng/mL, and Grade Group II (highest Gleason 3+4) in three cores of 12 taken, at the right mid-gland and right apex. The patient has been on active surveillance for the past 16 months. He was originally diagnosed after biopsy for an elevated PSA with stage I prostate cancer, T1cN0M0; PSA, 4.5 ng/mL; Grade Group 1 (Gleason 3+3) in one core of 12 taken, also at the right mid-gland. A multiparametric magnetic resonance imaging scan showed a heterogeneous peripheral zone without a dominant lesion and a calculated prostate volume of 28 mL. His medical history includes hypercholesterolemia, for which he takes atorvastatin. He is otherwise healthy and has no other significant medical or surgical history. His father had prostate cancer in his 70s and died of other causes at 89 years of age. The patient reports 2- to 3-hour urinary frequency and 0 to 1 nocturia, and has no difficulty obtaining or maintaining an erection. After meeting with his urologist, he sees a radiation oncologist.

scholarly journals Signal Intensity of High B-value Diffusion-weighted Imaging for the Detection of Prostate Cancer

2019 ◽  
Author(s):  
A Rezaeian ◽  
M J Tahmasebi Birgani ◽  
N Chegeni ◽  
M Sarkarian ◽  
M Gh Hanafi ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Cancer Detection ◽  
Signal Intensity ◽  
Peripheral Zone ◽  
B Value ◽  
Diffusion Weighted Images ◽  
High B Value ◽  
Diffusion Weighted ◽  
Central Gland ◽  
Sensitivity Specificity

Background: Diffusion-weighted imaging (DWI) is a main component of multiparametric MRI for prostate cancer detection. Recently, high b value DWI has gained more attention because of its capability for tumor characterization.Objectives: To assess based on histopathological findings of transrectal ultrasound (TRUS)-guided prostate biopsy as a reference, an increase in signal intensity of prostatic lesions in comparison with normal background tissue on high b-value diffusion-weighted images could be a sign of malignancy. Material and Methods: Fifty-three consecutive patients retrospectively included in the study. All patients underwent routine TRUS-guided prostate biopsies involving 12 cores after the magnetic resonance imaging (MRI) examinations. In seventeen patients (n =35 lesions), the prostate cancer was histologically confirmed by TRUS-guided prostate biopsy. The biopsy results of other patients were negative. Signal intensities on the high b-value (1600 s/mm2) images of the peripheral zone, the central gland, and the defined lesions were evaluated using region of interest-based measurements. Sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) for prostate cancer detection using signal intensity of high b value diffusion-weighted images were calculated.Results: In the patients with confirmed prostate cancer, fourteen had visually increased SI on the high b-value images. The SI of lesions for these patients was higher than the SI of peripheral zone (22±18%) or central gland (31±20%). In patients with a negative biopsy, eight had visually increased SI on the high b-value images. The SI of lesions for these patients was 23±21% and 35±18% higher than the SI in the peripheral zone and the central gland, respectively. The sensitivity, specificity, PPV, and NPV for prostate cancer using SI of high b value DWI were 71, 87, 62, and 87 %, respectively.Conclusions: Visually increased SI on the high b-value images can be an indication of malignancy, although some benign lesions also show this increase in signal intensity. 

Naive patients with suspicious prostate cancer and positive multiparametric magnetic resonance imaging (mp-MRI): is it time for fusion target biopsy alone?

2021 ◽  
pp. 205141582110237
Author(s):  
Enrico Checcucci ◽  
Sabrina De Cillis ◽  
Daniele Amparore ◽  
Diletta Garrou ◽  
Roberta Aimar ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Prostate Cancer ◽  
Magnetic Resonance ◽  
Detection Rate ◽  
Resonance Imaging ◽  
Fusion Biopsy ◽  
Grade Group ◽  
Final Pathology ◽  
Multiparametric Magnetic Resonance Imaging ◽  
Clinically Significant

Objectives: To determine if standard biopsy still has a role in the detection of prostate cancer or clinically significant prostate cancer in biopsy-naive patients with positive multiparametric magnetic resonance imaging. Materials and methods: We extracted, from our prospective maintained fusion biopsy database, patients from March 2014 to December 2018. The detection rate of prostate cancer and clinically significant prostate cancer and complication rate were analysed in a cohort of patients who underwent fusion biopsy alone (group A) or fusion biopsy plus standard biopsy (group B). The International Society of Urological Pathology grade group determined on prostate biopsy with the grade group determined on final pathology among patients who underwent radical prostatectomy were compared. Results: Prostate cancer was found in 249/389 (64.01%) and 215/337 (63.8%) patients in groups A and B, respectively ( P=0.98), while the clinically significant prostate cancer detection rate was 57.8% and 55.1% ( P=0.52). No significant differences in complications were found. No differences in the upgrading rate between biopsy and final pathology finding after radical prostatectomy were recorded. Conclusions: In biopsy-naive patients, with suspected prostate cancer and positive multiparametric magnetic resonance imaging the addition of standard biopsy to fusion biopsy did not increase significantly the detection rate of prostate cancer or clinically significant prostate cancer. Moreover, the rate of upgrading of the cancer grade group between biopsy and final pathology was not affected by the addition of standard biopsy. Level of evidence: Not applicable for this multicentre audit.

scholarly journals Aquaglyceroporin-3’s Expression and Cellular Localization Is Differentially Modulated by Hypoxia in Prostate Cancer Cell Lines

Cells ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 838
Author(s):  
Andreia de Almeida ◽  
Dimitris Parthimos ◽  
Holly Dew ◽  
Oliver Smart ◽  
Marie Wiltshire ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Cell Lines ◽  
Crucial Role ◽  
Cellular Localization ◽  
Texture Features ◽  
Stress Factors ◽  
Adaptation To Hypoxia ◽  
Machine Learning Classification ◽  
Hypoxia Exposure ◽  
Pc3 Cells

Aquaporins are required by cells to enable fast adaptation to volume and osmotic changes, as well as microenvironmental metabolic stimuli. Aquaglyceroporins play a crucial role in supplying cancer cells with glycerol for metabolic needs. Here, we show that AQP3 is differentially expressed in cells of a prostate cancer panel. AQP3 is located at the cell membrane and cytoplasm of LNCaP cell while being exclusively expressed in the cytoplasm of Du145 and PC3 cells. LNCaP cells show enhanced hypoxia growth; Du145 and PC3 cells display stress factors, indicating a crucial role for AQP3 at the plasma membrane in adaptation to hypoxia. Hypoxia, both acute and chronic affected AQP3′s cellular localization. These outcomes were validated using a machine learning classification approach of the three cell lines and of the six normoxic or hypoxic conditions. Classifiers trained on morphological features derived from cytoskeletal and nuclear labeling alongside corresponding texture features could uniquely identify each individual cell line and the corresponding hypoxia exposure. Cytoskeletal features were 70–90% accurate, while nuclear features allowed for 55–70% accuracy. Cellular texture features (73.9% accuracy) were a stronger predictor of the hypoxic load than the AQP3 distribution (60.3%).

scholarly journals Hypoxia-Inducible Factor 2a Expression Is Positively Correlated With Gleason Score in Prostate Cancer

2021 ◽  
Vol 20 ◽  
pp. 153303382199001
Author(s):  
Dimitrios Pavlakis ◽  
Spyridon Kampantais ◽  
Konstantinos Gkagkalidis ◽  
Victoras Gourvas ◽  
Dimitrios Memmos ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Gleason Score ◽  
Locally Advanced ◽  
Hypoxia Inducible Factor ◽  
Immunohistochemical Expression ◽  
Locally Advanced Prostate Cancer ◽  
Grade Group ◽  
Main Factors ◽  
Hif Pathway ◽  
Lower Expression

Background: One of the main factors in response to hypoxia in the tumor microenvironment is the hypoxia-inducible factor (HIF) pathway. Although its role in other solid tumors, particularly renal cell carcinoma, has been sufficiently elucidated, it remains elusive in prostate cancer. The aim of the present study was to investigate the expression of main proteins involved in this pathway and determine the correlation of the results with clinicopathological outcomes of patients with prostate cancer. Methods: The immunohistochemical expression of HIF-1a, HIF-2a and their regulators, prolyl hydroxylase domain (PHD)1, PHD2 and PHD3 and factor inhibiting HIF (FIH), was assessed on a tissue microarray. This was constructed from radical prostatectomy specimens, involving both tumor and corresponding adjacent non-tumoral prostate tissues from 50 patients with localized or locally advanced prostate cancer. Results: In comparison with non-tumoral adjacent tissue, HIF-1a exhibited an equal or lower expression in 86% of the specimens (P = 0.017), while HIF-2a was overexpressed in 52% (P = 0.032) of the cases. HIF-1a protein expression was correlated with HIF-2a (P < 0.001), FIH (P = 0.004), PHD1 (P < 0.001), PHD2 (P < 0.001) and PHD3 (P = 0.035). HIF-2a expression was positively correlated with Gleason score (P = 0.017) and International Society of Urological Pathologists (ISUP) grade group (P = 0.022). Conclusions: The findings of the present study suggest a key role for HIF-2a in prostate cancer, as HIF-2a expression was found to be correlated with Gleason score and ISUP grade of the patients. However, further studies are required to validate these results and investigate the potential value of HIF-2a as a therapeutic target in prostate cancer.

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