Cerebral Microbleed Detection via Convolutional Neural Network and Extreme Learning Machine

Vascular cognitive impairment - Past, present, and future challenges

Vascular cognitive impairment (VCI) is a lifelong process encompassing a broad spectrum of cognitive disorders, ranging from subtle or mild deficits to prodromal and fully developed dementia, originating from cerebrovascular lesions such as large and small vessel disease. Genetic predisposition and environmental exposure to risk factors such as unhealthy lifestyles, hypertension, cardiovascular disease, and metabolic disorders will synergistically interact, yielding biochemical and structural brain changes, ultimately culminating in VCI. However, little is known about the pathological processes underlying VCI and the temporal dynamics between risk factors and disease mechanisms (biochemical and structural brain changes). This narrative review aims to provide an evidence-based summary of the link between individual vascular risk/disorders and cognitive dysfunction and the potential structural and biochemical pathophysiological processes. We also discuss some key challenges for future research on VCI. There is a need to shift from individual risk factors/disorders to comorbid vascular burden, identifying and integrating imaging and fluid biomarkers, implementing a life-course approach, considering possible neuroprotective influences of positive life exposures, and addressing biological sex at birth and gender differences. Finally, this review highlights the need for future researchers to leverage and integrate multidimensional data to advance our understanding of the mechanisms and pathophysiology of VCI.

Study of high-altitude cerebral edema using multimodal imaging

Objective

To analyze the brain imaging features of high-altitude cerebral edema (HACE) using computed tomography (CT) and multi-sequence magnetic resonance imaging (MRI) and to explore its injury characteristics.

Materials and methods

We selected 30 patients with HACE diagnosed between January 2012 to August 2022 as the experimental group and 60 patients with dizziness on traveling from the plain to the plateau or from lower altitude to higher altitude in a short period of time as the control group. We collected general clinical data from the experimental group and classified it according to clinical symptoms. In both groups, we then performed a head CT and multi-sequence MRI (T1WI, T2WI, FLAIR, and DWI). Among them, nine patients with HACE were also scanned using susceptibility-weighted imaging (SWI). Finally, we analyzed the images.

Results

According to clinical symptoms, we divided the 30 cases of HACE into 12 mild cases and 18 severe cases. There was no significant difference in sex, age, leukocyte, neutrophil, or glucose content between mild and severe HACE. The sensitivity and specificity of the MRI diagnosis were 100 and 100%, respectively, while the sensitivity and specificity of the CT diagnosis were 23.3 and 100%, respectively. The distribution range of deep and juxtacortical white matter edema was significantly larger in severe HACE than in mild HACE (p < 0.001). The corpus callosum edema distribution range in severe HACE was significantly larger than that in mild HACE (p = 0.001). The ADC value of the splenium of the corpus callosum was significantly lower in severe HACE than in mild HACE (p = 0.049). In mild and severe HACE, the signal intensity of the DWI sequence was significantly higher than that of conventional MRI sequences (T1WI, T2WI, FLAIR) (p = 0.008, p = 0.025, respectively). In severe HACE, seven cases showed bilateral corticospinal tract edema at the thalamic level, and SWI showed cerebral microbleeds (CMBs) in five cases, especially in the corpus callosum.

Conclusions

MRI has more advantages than CT in the evaluation of HACE, especially in the DWI sequence. The white matter injury of severe HACE is more severe and extensive, especially in the corpus callosum, and some CMBs and corticospinal tract edema may also appear.

Machine-learning based investigation of prognostic indicators for oncological outcome of pancreatic ductal adenocarcinoma

Introduction

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive malignancy with a poor prognosis. Surgical resection remains the only potential curative treatment option for early-stage resectable PDAC. Patients with locally advanced or micrometastatic disease should ideally undergo neoadjuvant therapy prior to surgical resection for an optimal treatment outcome. Computerized tomography (CT) scan is the most common imaging modality obtained prior to surgery. However, the ability of CT scans to assess the nodal status and resectability remains suboptimal and depends heavily on physician experience. Improved preoperative radiographic tumor staging with the prediction of postoperative margin and the lymph node status could have important implications in treatment sequencing. This paper proposes a novel machine learning predictive model, utilizing a three-dimensional convoluted neural network (3D-CNN), to reliably predict the presence of lymph node metastasis and the postoperative positive margin status based on preoperative CT scans.

Methods

A total of 881 CT scans were obtained from 110 patients with PDAC. Patients and images were separated into training and validation groups for both lymph node and margin prediction studies. Per-scan analysis and per-patient analysis (utilizing majority voting method) were performed.

Results

For a lymph node prediction 3D-CNN model, accuracy was 90% for per-patient analysis and 75% for per-scan analysis. For a postoperative margin prediction 3D-CNN model, accuracy was 81% for per-patient analysis and 76% for per-scan analysis.

Discussion

This paper provides a proof of concept that utilizing radiomics and the 3D-CNN deep learning framework may be used preoperatively to improve the prediction of positive resection margins as well as the presence of lymph node metastatic disease. Further investigations should be performed with larger cohorts to increase the generalizability of this model; however, there is a great promise in the use of convoluted neural networks to assist clinicians with treatment selection for patients with PDAC.

Computer-Aided Extraction of Select MRI Markers of Cerebral Small Vessel Disease: A Systematic Review

Cerebral small vessel disease (CSVD) is a major vascular contributor to cognitive impairment in ageing, including dementias. Imaging remains the most promising method for in vivo studies of CSVD. To replace the subjective and laborious visual rating approaches, emerging studies have applied state-of-the-art artificial intelligence to extract imaging biomarkers of CSVD from MRI scans. We aimed to summarise published computer-aided methods for the examination of three imaging biomarkers of CSVD, namely cerebral microbleeds (CMB), dilated perivascular spaces (PVS), and lacunes of presumed vascular origin. Seventy classical image processing, classical machine learning, and deep learning studies were identified. Transfer learning and weak supervision techniques have been applied to accommodate the limitations in the training data. While good performance metrics were achieved in local datasets, there have not been generalisable pipelines validated in different research and/or clinical cohorts. Future studies could consider pooling data from multiple sources to increase data size and diversity, and evaluating performance using both image processing metrics and associations with clinical measures.