The role of preoperative diffusion tensor imaging in predicting and improving functional outcome in pediatric patients undergoing epilepsy surgery: a systematic review

Objective: Diffusion tensor imaging (DTI) is a useful neuroimaging technique for surgical planning in adult patients. However, no systematic review has been conducted to determine its utility for pre-operative analysis and planning of Pediatric Epilepsy surgery. We sought to determine the benefit of pre-operative DTI in predicting and improving neurological functional outcome after epilepsy surgery in children with intractable epilepsy. Methods: A systematic review of articles in English using PubMed, EMBASE and Scopus databases, from inception to January 10, 2020 was conducted. All studies that used DTI as either predictor or direct influencer of functional neurological outcome (motor, sensory, language and/or visual) in pediatric epilepsy surgical candidates were included. Data extraction was performed by two blinded reviewers. Risk of bias of each study was determined using the QUADAS 2 Scoring System. Results: 13 studies were included (6 case reports/series, 5 retrospective cohorts, and 2 prospective cohorts) with a total of 229 patients. Seven studies reported motor outcome; three reported motor outcome prediction with a sensitivity and specificity ranging from 80 to 85.7 and 69.6 to 100%, respectively; four studies reported visual outcome. In general, the use of DTI was associated with a high degree of favorable neurological outcomes after epilepsy surgery. Conclusion: Multiple studies show that DTI helps to create a tailored plan that results in improved functional outcome. However, more studies are required in order to fully assess its utility in pediatric patients. This is a desirable field of study because DTI offers a non-invasive technique more suitable for children. Advances in knowledge: This systematic review analyses, exclusively, studies of pediatric patients with drug-resistant epilepsy and provides an update of the evidence regarding the role of DTI, as part of the pre-operative armamentarium, in improving post-surgical neurological sequels and its potential for outcome prediction.

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Preoperative Prediction of Facial Nerve in Patients with Vestibular Schwannomas: The Role of Diffusion Tensor Imaging—A Systematic Review

World Neurosurgery ◽

10.1016/j.wneu.2019.01.099 ◽

2019 ◽

Vol 125 ◽

pp. 24-31 ◽

Cited By ~ 5

Author(s):

Valentina Baro ◽

Andrea Landi ◽

Sabrina Brigadoi ◽

Marco Castellaro ◽

Manuela Moretto ◽

...

Keyword(s):

Systematic Review ◽

Diffusion Tensor Imaging ◽

Facial Nerve ◽

Diffusion Tensor ◽

Vestibular Schwannomas ◽

Preoperative Prediction

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Diffusion tensor imaging of renal parenchyma in pediatric patients with chronic kidney disease: Correlation with serum biomarkers

10.22541/au.161235171.19347933/v1 ◽

2021 ◽

Author(s):

Ahmed Abdel Razek ◽

Ayman Hammad ◽

Manar Mansour ◽

Marwa Ramadan ◽

Khadiga Ali ◽

...

Keyword(s):

Chronic Kidney Disease ◽

Diffusion Tensor Imaging ◽

Kidney Disease ◽

Pediatric Patients ◽

Renal Cortex ◽

Diffusion Tensor ◽

Serum Biomarkers ◽

Apparent Diffusion ◽

Diffusion Tensor Imaging Dti

Purpose: to demonstrate role of diffusion tensor imaging (DTI) in diagnosis of pediatric chronic kidney disease (CKD) using fraction anisotropy (FA) and apparent diffusion coefficient (ADC). Material and methods: Prospective study done on 35 CKD patients (19 boys, 16 girls; mean age 12.2±2.7 years) and 19 age and sex-matched volunteers. Patients with sclerotic (n = 25) and non-sclerotic (n= 10) CKD that underwent DTI of kidney. Results: Mean FA of renal cortex/ medulla in CKD (0.20±0.07, and 0.18±0.08) was significantly lower (p = 0.001) from volunteers (0.27±0.08, 0.31±0.09). Cutoff renal FA of cortex/ medulla used for diagnosis of CKD was 0.23, and 0.22 with AUC of 0.828, 0.828 and accuracy of 82.9%, 80.7%. Mean ADC of renal cortex/ medulla in CKD (1.98±0.23 and 2.03±0.23 X10-3mm2/s) was significantly higher (p = 0.001) that of volunteers (1.65±0.134 and 1.68±0.16 X10-3mm2/s. Cutoff renal ADC of cortex/medulla used to diagnosis of CKD were 1.75 and 1.85X10-3mm2/s with AUC of 0.828, 0.910, 0.828 and 0.81 and accuracy of 82.9%, 84.1%, 80.7% and 79.5%. FA of renal cortex/medulla in sclerotic CKD was significantly different (p = 0.001) than non-sclerotic CKD (0.26±0.07 and 0.25±0.08). The FA of renal cortex/medulla in CKD patients correlated with serum creatinine (r = -0.468; p = 0.000, r =-0.381; p = 0.001), e GFR (r = 0.364; p = 0.002, r = 0.318; p = 0.007). Conclusion: FA and ADC of renal cortex/ medulla can help in diagnosis of CKD, FA cortex/ medulla predicts sclerotic CKD and correlated with some of serum biomarkers.

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The role of diffusion tensor imaging in characterizing injury patterns on athletes with concussion and subconcussive injury: a systematic review

Brain Injury ◽

10.1080/02699052.2021.1895313 ◽

2021 ◽

pp. 1-24

Author(s):

Maryam Tayebi ◽

Samantha J. Holdsworth ◽

Allen A. Champagne ◽

Douglas J. Cook ◽

Poul Nielsen ◽

...

Keyword(s):

Systematic Review ◽

Diffusion Tensor Imaging ◽

Diffusion Tensor ◽

Injury Patterns ◽

Subconcussive Injury

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

Processes ◽

10.3390/pr8091071 ◽

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.

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