Radiation-induced abnormal cortical thickness in patients with nasopharyngeal carcinoma after radiotherapy

Conventional MRI studies showed that radiation-induced brain necrosis in patients with nasopharyngeal carcinoma (NPC) in years after radiotherapy (RT) could involve brain gray matter (GM) and impair brain function. However, it is still unclear the radiation-induced brain morphological changes in NPC patients with normal-appearing GM in the early period after RT. In this study, we acquired high-resolution brain structural MRI data from three groups of patients, 22 before radiotherapy (pre-RT) NPC patients with newly diagnosed but not yet medically treated, 22 NPC patients in the early-delayed stage after radiotherapy (post-RT-ED), and 20 NPC patients in the late-delayed stage after radiotherapy (post-RT-LD), and then analyzed the radiation-induced cortical thickness alteration in NPC patients after RT. Using a vertex-wise surface-based morphometry (SBM) approach, we detected significantly decreased cortical thickness in the precentral gyrus (PreCG) in the post-RT-ED group compared to the pre-RT group. And the post-RT-LD group showed significantly increased cortical thickness in widespread brain regions, including the bilateral inferior parietal, left isthmus of the cingulate, left bank of the superior temporal sulcus and left lateral occipital regions, compared to the pre-RT group, and in the bilateral PreCG compared to the post-RT-ED group. Similar analysis with ROI-wise SBM method also found the consistent results. These results indicated that radiation-induced brain injury mainly occurred in the post-RT-LD group and the cortical thickness alterations after RT were dynamic in different periods. Our findings may reflect the pathogenesis of radiation-induced brain injury in NPC patients with normal-appearing GM and an early intervention is necessary for protecting GM during RT.

Computational analysis in epilepsy neuroimaging: A survey of features and methods

Epilepsy affects 65 million people worldwide, a third of whom have seizures that are resistant to anti-epileptic medications. Some of these patients may be amenable to surgical therapy or treatment with implantable devices, but this usually requires delineation of discrete structural or functional lesion(s), which is challenging in a large percentage of these patients.

Advances in neuroimaging and machine learning allow semi-automated detection of malformations of cortical development (MCDs), a common cause of drug resistant epilepsy. A frequently asked question in the field is what techniques currently exist to assist radiologists in identifying these lesions, especially subtle forms of MCDs such as focal cortical dysplasia (FCD) Type I and low grade glial tumors. Below we introduce some of the common lesions encountered in patients with epilepsy and the common imaging findings that radiologists look for in these patients. We then review and discuss the computational techniques introduced over the past 10 years for quantifying and automatically detecting these imaging findings. Due to large variations in the accuracy and implementation of these studies, specific techniques are traditionally used at individual centers, often guided by local expertise, as well as selection bias introduced by the varying prevalence of specific patient populations in different epilepsy centers. We discuss the need for a multi-institutional study that combines features from different imaging modalities as well as computational techniques to definitively assess the utility of specific automated approaches to epilepsy imaging. We conclude that sharing and comparing these different computational techniques through a common data platform provides an opportunity to rigorously test and compare the accuracy of these tools across different patient populations and geographical locations. We propose that these kinds of tools, quantitative imaging analysis methods and open data platforms for aggregating and sharing data and algorithms, can play a vital role in reducing the cost of care, the risks of invasive treatments, and improve overall outcomes for patients with epilepsy.

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We introduce common epileptogenic lesions encountered in patients with drug resistant epilepsy.

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We discuss state of the art computational techniques used to detect lesions.

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There is a need for multi-institutional studies that combine these techniques.

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Clinically validated pipelines alongside the advances in imaging and electrophysiology will improve outcomes.