Automatic segmentation of hemorrhagic transformation on follow-up non-contrast CT after acute ischemic stroke

Background

Hemorrhagic transformation (HT) following reperfusion therapies is a serious complication for patients with acute ischemic stroke. Segmentation and quantification of hemorrhage provides critical insights into patients' condition and aids in prognosis. This study aims to automatically segment hemorrhagic regions on follow-up non-contrast head CT (NCCT) for stroke patients treated with endovascular thrombectomy (EVT).

Methods

Patient data were collected from 10 stroke centers across two countries. We propose a semi-automated approach with adaptive thresholding methods, eliminating the need for extensive training data and reducing computational demands. We used Dice Similarity Coefficient (DSC) and Lin's Concordance Correlation Coefficient (Lin's CCC) to evaluate the performance of the algorithm.

Results

A total of 51 patients were included, with 28 Type 2 hemorrhagic infarction (HI2) cases and 23 parenchymal hematoma (PH) cases. The algorithm achieved a mean DSC of 0.66 ± 0.17. Notably, performance was superior for PH cases (mean DSC of 0.73 ± 0.14) compared to HI2 cases (mean DSC of 0.61 ± 0.18). Lin's CCC was 0.88 (95% CI 0.79-0.93), indicating a strong agreement between the algorithm's results and the ground truth. In addition, the algorithm demonstrated excellent processing time, with an average of 2.7 s for each patient case.

Conclusion

To our knowledge, this is the first study to perform automated segmentation of post-treatment hemorrhage for acute stroke patients and evaluate the performance based on the radiological severity of HT. This rapid and effective tool has the potential to assist with predicting prognosis in stroke patients with HT after EVT.

Automated occlusion detection for the diagnosis of acute ischemic stroke: A detailed performance review

INTRODUCTION

Stroke is a leading cause of adult disability and death worldwide. Automated detection of stroke on brain imaging has promise in a time critical environment. We present a method for the automated detection of intracranial occlusions on dynamic CT Angiography (CTA) causing acute ischemic stroke.

METHODS

We derived dynamic CTA images from CT Perfusion (CTP) data and utilised advanced image processing to enhance and display major cerebral blood vessels for symmetry analysis. We reviewed the performance of the algorithm on a cohort of 207 patients from the International Stroke Perfusion Imaging Registry (INSPIRE), with Large Vessel Occlusion (LVO) and non-LVO strokes. Included in the data were images with chronic stroke, various artefacts, incomplete vessel occlusions, and images of poorer quality. All images were annotated by stroke experts. In addition, each image was graded in terms of the difficulty of the task of occlusion detection. Performance was evaluated on the overall cohort, and with respect to occlusion location, collateral grade, and task difficulty. We also evaluated the impact of including additional perfusion data.

RESULTS

Images with a rating of lower difficulty achieved a sensitivity and specificity of 96% and 90%, respectively, while images with a moderate difficulty rating achieved 88% and 50%, respectively. For cases of high difficulty, where more than two experts or additional data were required to reach consensus, sensitivity and specificity was 53% and 11%. The addition of perfusion data to the dCTA images increased the specificity by 38%.

CONCLUSION

We have provided an unbiased interpretation of algorithm performance. Further developments include generalising to conventional CTA and employing the algorithm in a clinical setting for prospective studies.

Benchmark dataset for clot detection in ischemic stroke vessel-based imaging: CODEC-IV

We present an annotated dataset for the purposes of creating a benchmark in Artificial Intelligence for automated clot detection. While there are commercial tools available for automated clot detection on computed tomographic (CT) angiographs, they have not been compared in a standardized manner whereby accuracy is reported on a publicly available benchmark dataset. Furthermore, there are known difficulties in automated clot detection - namely, cases where there is robust collateral flow, or residual flow and occlusions of the smaller vessels - and it is necessary to drive an initiative to overcome these challenges. Our dataset contains 159 multiphase CTA patient datasets, derived from CTP and annotated by expert stroke neurologists. In addition to images where the clot is marked, the expert neurologists have provided information about clot location, hemisphere and the degree of collateral flow. The data is available on request by researchers via an online form, and we will host a leaderboard where the results of clot detection algorithms on the dataset will be displayed. Participants are invited to submit an algorithm to us for evaluation using the evaluation tool, which is made available at together with the form at https://github.com/MBC-Neuroimaging/ClotDetectEval.

Risk factors of hemorrhagic transformation in acute ischaemic stroke: A systematic review and meta-analysis

Background

Hemorrhagic transformation (HT) following reperfusion therapies for acute ischaemic stroke often predicts a poor prognosis. This systematic review and meta-analysis aims to identify risk factors for HT, and how these vary with hyperacute treatment [intravenous thrombolysis (IVT) and endovascular thrombectomy (EVT)].

Methods

Electronic databases PubMed and EMBASE were used to search relevant studies. Pooled odds ratio (OR) with 95% confidence interval (CI) were estimated.

Results

A total of 120 studies were included. Atrial fibrillation and NIHSS score were common predictors for any intracerebral hemorrhage (ICH) after reperfusion therapies (both IVT and EVT), while a hyperdense artery sign (OR = 2.605, 95% CI 1.212-5.599, I ² = 0.0%) and number of thrombectomy passes (OR = 1.151, 95% CI 1.041-1.272, I ² = 54.3%) were predictors of any ICH after IVT and EVT, respectively. Common predictors for symptomatic ICH (sICH) after reperfusion therapies were age and serum glucose level. Atrial fibrillation (OR = 3.867, 95% CI 1.970-7.591, I ² = 29.1%), NIHSS score (OR = 1.082, 95% CI 1.060-1.105, I ² = 54.5%) and onset-to-treatment time (OR = 1.003, 95% CI 1.001-1.005, I ² = 0.0%) were predictors of sICH after IVT. Alberta Stroke Program Early CT score (ASPECTS) (OR = 0.686, 95% CI 0.565-0.833, I ² =77.6%) and number of thrombectomy passes (OR = 1.374, 95% CI 1.012-1.866, I ² = 86.4%) were predictors of sICH after EVT.

Conclusion

Several predictors of ICH were identified, which varied by treatment type. Studies based on larger and multi-center data sets should be prioritized to confirm the results.

Systematic review registration

https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=268927, identifier: CRD42021268927.

Machine learning segmentation of core and penumbra from acute stroke CT perfusion data

Introduction

Computed tomography perfusion (CTP) imaging is widely used in cases of suspected acute ischemic stroke to positively identify ischemia and assess suitability for treatment through identification of reversible and irreversible tissue injury. Traditionally, this has been done via setting single perfusion thresholds on two or four CTP parameter maps. We present an alternative model for the estimation of tissue fate using multiple perfusion measures simultaneously.

Methods

We used machine learning (ML) models based on four different algorithms, combining four CTP measures (cerebral blood flow, cerebral blood volume, mean transit time and delay time) plus 3D-neighborhood (patch) analysis to predict the acute ischemic core and perfusion lesion volumes. The model was developed using 86 patient images, and then tested further on 22 images.

Results

XGBoost was the highest-performing algorithm. With standard threshold-based core and penumbra measures as the reference, the model demonstrated moderate agreement in segmenting core and penumbra on test images. Dice similarity coefficients for core and penumbra were 0.38 ± 0.26 and 0.50 ± 0.21, respectively, demonstrating moderate agreement. Skull-related image artefacts contributed to lower accuracy.

Discussion

Further development may enable us to move beyond the current overly simplistic core and penumbra definitions using single thresholds where a single error or artefact may lead to substantial error.

DNA-PKcs modulates progenitor cell proliferation and fibroblast senescence in idiopathic pulmonary fibrosis

BACKGROUND

Recent studies have highlighted the contribution of senescent mesenchymal and epithelial cells in Idiopathic Pulmonary Fibrosis (IPF), but little is known regarding the molecular mechanisms that regulate the accumulation of senescent cells in this disease. Therefore, we addressed the hypothesis that the loss of DNA repair mechanisms mediated by DNA protein kinase catalytic subunit (DNA-PKcs) in IPF, promoted the accumulation of mesenchymal progenitors and progeny, and the expression of senescent markers by these cell types.

METHODS

Surgical lung biopsy samples and lung fibroblasts were obtained from patients exhibiting slowly, rapidly or unknown progressing IPF and lung samples lacking any evidence of fibrotic disease (i.e. normal; NL). The expression of DNA-Pkcs in lung tissue was assessed by quantitative immunohistochemical analysis. Chronic inhibition of DNA-PKcs kinase activity was mimicked using a highly specific small molecule inhibitor, Nu7441. Proteins involved in DNA repair (stage-specific embryonic antigen (SSEA)-4⁺ cells) were determined by quantitative Ingenuity Pathway Analysis of transcriptomic datasets (GSE103488). Lastly, the loss of DNA-PKc was modeled in a humanized model of pulmonary fibrosis in NSG SCID mice genetically deficient in PRKDC (the transcript for DNA-PKcs) and treated with Nu7441.

RESULTS

DNA-PKcs expression was significantly reduced in IPF lung tissues. Chronic inhibition of DNA-PKcs by Nu7441 promoted the proliferation of SSEA4⁺ mesenchymal progenitor cells and a significant increase in the expression of senescence-associated markers in cultured lung fibroblasts. Importantly, mesenchymal progenitor cells and their fibroblast progeny derived from IPF patients showed a loss of transcripts encoding for DNA damage response and DNA repair components. Further, there was a significant reduction in transcripts encoding for PRKDC (the transcript for DNA-PKcs) in SSEA4⁺ mesenchymal progenitor cells from IPF patients compared with normal lung donors. In SCID mice lacking DNA-PKcs activity receiving IPF lung explant cells, treatment with Nu7441 promoted the expansion of progenitor cells, which was observed as a mass of SSEA4⁺ CgA⁺ expressing cells.

CONCLUSIONS

Together, our results show that the loss of DNA-PKcs promotes the expansion of SSEA4⁺ mesenchymal progenitors, and the senescence of their mesenchymal progeny.

Generalised Cornu spirals: an experimental study using hard x-rays

The Cornu spiral is a graphical aid that has been used historically to evaluate Fresnel integrals. It is also the Argand-plane mapping of a monochromatic complex scalar plane wave diffracted by a hard edge. We have successfully reconstructed a Cornu spiral due to diffraction of hard x-rays from a piece of Kapton tape. Additionally, we have explored the generalisation of the Cornu spiral by observing the Argand-plane mapping of complex scalar electromagnetic fields diffracted by a cylinder and a sphere embedded within a cylinder.