Correlation of gleason scores with magnetic resonance diffusion tensor imaging in peripheral zone prostate cancer

2014 ◽  
Vol 42 (2) ◽  
pp. 460-467 ◽  
Author(s):  
Liang Li ◽  
Daniel J.A. Margolis ◽  
Ming Deng ◽  
Jie Cai ◽  
Ling Yuan ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Diffusion Tensor Imaging ◽  
Magnetic Resonance ◽  
Diffusion Tensor ◽  
Peripheral Zone

scholarly journals Diffusion Is Directional: Innovative Diffusion Tensor Imaging to Improve Prostate Cancer Detection

Diagnostics ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 563
Author(s):  
Chen Shenhar ◽  
Hadassa Degani ◽  
Yaara Ber ◽  
Jack Baniel ◽  
Shlomit Tamir ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Diffusion Tensor Imaging ◽  
Predictive Value ◽  
Breast Imaging ◽  
Normal Tissue ◽  
Diffusion Tensor ◽  
Peripheral Zone ◽  
Predictive Values ◽  
Directional Diffusion ◽  
Central Gland

In the prostate, water diffusion is faster when moving parallel to duct and gland walls than when moving perpendicular to them, but these data are not currently utilized in multiparametric magnetic resonance imaging (mpMRI) for prostate cancer (PCa) detection. Diffusion tensor imaging (DTI) can quantify the directional diffusion of water in tissue and is applied in brain and breast imaging. Our aim was to determine whether DTI may improve PCa detection. We scanned patients undergoing mpMRI for suspected PCa with a DTI sequence. We calculated diffusion metrics from DTI and diffusion weighted imaging (DWI) for suspected lesions and normal-appearing prostate tissue, using specialized software for DTI analysis, and compared predictive values for PCa in targeted biopsies, performed when clinically indicated. DTI scans were performed on 78 patients, 42 underwent biopsy and 16 were diagnosed with PCa. The median age was 62 (IQR 54.4–68.4), and PSA 4.8 (IQR 1.3–10.7) ng/mL. DTI metrics distinguished PCa lesions from normal tissue. The prime diffusion coefficient (λ1) was lower in both peripheral-zone (p < 0.0001) and central-gland (p < 0.0001) cancers, compared to normal tissue. DTI had higher negative and positive predictive values than mpMRI to predict PCa (positive predictive value (PPV) 77.8% (58.6–97.0%), negative predictive value (NPV) 91.7% (80.6–100%) vs. PPV 46.7% (28.8–64.5%), NPV 83.3% (62.3–100%)). We conclude from this pilot study that DTI combined with T2-weighted imaging may have the potential to improve PCa detection without requiring contrast injection.

scholarly journals Evaluation of Peripheral Zone Prostate Cancer Aggressiveness Using the Ratio of Diffusion Tensor Imaging Measures

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Aslihan Onay ◽  
Gokhan Ertas ◽  
Metin Vural ◽  
Omer Acar ◽  
Yesim Saglican ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Diffusion Tensor Imaging ◽  
Diffusion Tensor ◽  
Mean Diffusivity ◽  
Peripheral Zone ◽  
Strong Correlations ◽  
Noncancerous Tissue ◽  
Statistical Measures ◽  
Cancer Aggressiveness ◽  
Better Than

Purpose. To evaluate the aggressiveness of peripheral zone prostate cancer by correlating the Gleason score (GS) with the ratio of the diffusion tensor imaging (DTI) measures. Materials and Methods. Forty-two peripheral zone prostate tumors were imaged using DTI. Regions of interest focusing on the center of tumor foci and noncancerous tissue were used to extract statistical measures of mean diffusivity (MD) and fractional anisotroy (FA). Measure ratio was calculated by dividing tumor measure by noncancerous tissue measure. Results. Strong correlations are observable between GS and MD measures while weak correlations are present between GS and FA measures. Minimum tumor MD (MDmin) and the ratio of minimum MD (rMDmin) show the same highest correlation with GS (both ρ=-0.73). Between GS ≤ 7 (3 + 4) and GS ≥ 7 (4 + 3), differences are significant for all MD measures but for some FA measures. MD measures perform better than FA measures in discriminating GS ≥ 7 (4 + 3). Conclusion. Ratios of MD measures can be used in evaluation of peripheral zone prostate cancer aggressiveness; however tumor MD measures alone perform similarly.

scholarly journals Machine Learning for the Classification of Alzheimer’s Disease and Its Prodromal Stage Using Brain Diffusion Tensor Imaging Data: A Systematic Review

Processes ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1071
Author(s):  
Lucia Billeci ◽  
Asia Badolato ◽  
Lorenzo Bachi ◽  
Alessandro Tonacci
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Diffusion Tensor Imaging ◽  
Magnetic Resonance ◽  
Diffusion Tensor ◽  
Classification Problem ◽  
Computer Algorithms ◽  
Imaging Data

Alzheimer’s disease is notoriously the most common cause of dementia in the elderly, affecting an increasing number of people. Although widespread, its causes and progression modalities are complex and still not fully understood. Through neuroimaging techniques, such as diffusion Magnetic Resonance (MR), more sophisticated and specific studies of the disease can be performed, offering a valuable tool for both its diagnosis and early detection. However, processing large quantities of medical images is not an easy task, and researchers have turned their attention towards machine learning, a set of computer algorithms that automatically adapt their output towards the intended goal. In this paper, a systematic review of recent machine learning applications on diffusion tensor imaging studies of Alzheimer’s disease is presented, highlighting the fundamental aspects of each work and reporting their performance score. A few examined studies also include mild cognitive impairment in the classification problem, while others combine diffusion data with other sources, like structural magnetic resonance imaging (MRI) (multimodal analysis). The findings of the retrieved works suggest a promising role for machine learning in evaluating effective classification features, like fractional anisotropy, and in possibly performing on different image modalities with higher accuracy.

scholarly journals Diffusion Magnetic Resonance Imaging-Based Biomarkers for Neurodegenerative Diseases

2021 ◽  
Vol 22 (10) ◽  
pp. 5216
Author(s):  
Koji Kamagata ◽  
Christina Andica ◽  
Ayumi Kato ◽  
Yuya Saito ◽  
Wataru Uchida ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Diffusion Tensor Imaging ◽  
Magnetic Resonance ◽  
Neurodegenerative Diseases ◽  
Diffusion Magnetic Resonance Imaging ◽  
New Technologies ◽  
Diffusion Tensor ◽  
Free Water ◽  
Diffusion Kurtosis Imaging ◽  
Resonance Imaging

There has been an increasing prevalence of neurodegenerative diseases with the rapid increase in aging societies worldwide. Biomarkers that can be used to detect pathological changes before the development of severe neuronal loss and consequently facilitate early intervention with disease-modifying therapeutic modalities are therefore urgently needed. Diffusion magnetic resonance imaging (MRI) is a promising tool that can be used to infer microstructural characteristics of the brain, such as microstructural integrity and complexity, as well as axonal density, order, and myelination, through the utilization of water molecules that are diffused within the tissue, with displacement at the micron scale. Diffusion tensor imaging is the most commonly used diffusion MRI technique to assess the pathophysiology of neurodegenerative diseases. However, diffusion tensor imaging has several limitations, and new technologies, including neurite orientation dispersion and density imaging, diffusion kurtosis imaging, and free-water imaging, have been recently developed as approaches to overcome these constraints. This review provides an overview of these technologies and their potential as biomarkers for the early diagnosis and disease progression of major neurodegenerative diseases.

Changes in the corticospinal tract after wearing prosthesis in bilateral transtibial amputation

2017 ◽  
Vol 41 (5) ◽  
pp. 507-511
Author(s):  
Sang Yoon Lee ◽  
Si Hyun Kang ◽  
Don-Kyu Kim ◽  
Kyung Mook Seo ◽  
Hee Joon Ro ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Diffusion Tensor Imaging ◽  
Magnetic Resonance ◽  
Primary Motor Cortex ◽  
Corticospinal Tract ◽  
Diffusion Tensor ◽  
Brain Magnetic Resonance Imaging ◽  
Resonance Imaging ◽  
Transtibial Amputation

Background:After amputation, the brain is known to be reorganized especially in the primary motor cortex. We report a case to show changes in the corticospinal tract in a patient with serial bilateral transtibial amputations using diffusion tensor imaging.Case Description and Methods:A 78-year-old man had a transtibial amputation on his left side in 2008 and he underwent a right transtibial amputation in 2011. An initial brain magnetic resonance imaging with a diffusion tensor imaging was performed before starting rehabilitation on his right transtibial prosthesis, and a follow-up magnetic resonance imaging with diffusion tensor imaging was performed 2 years after this.Findings and Outcomes:In the initial diffusion tensor imaging, the number of fiber lines in his right corticospinal tract was larger than that in his left corticospinal tract. At follow-up diffusion tensor imaging, there was no definite difference in the number of fiber lines between both corticospinal tracts.Conclusion:We found that side-to-side corticospinal tract differences were equalized after using bilateral prostheses.Clinical relevanceThis case report suggests that diffusion tensor imaging tractography could be a useful method to understand corticomotor reorganization after using prosthesis in transtibial amputation.

Magnetic resonance changes with spectroscopy and diffusion tensor imaging after acute cyanide poisoning in a child

2010 ◽  
Vol 73 (1) ◽  
pp. e5-e8
Author(s):  
Darshana Dattatray Rasalkar ◽  
Winnie Chiu-wing Chu ◽  
Kam-lau Cheung ◽  
David Ka-wai Yeung ◽  
Lin Shi ◽  
...  
Keyword(s):  
Diffusion Tensor Imaging ◽  
Magnetic Resonance ◽  
Diffusion Tensor ◽  
Cyanide Poisoning ◽  
And Diffusion
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