Imaging-based prediction of histological clot composition from admission CT imaging

Deep learning prediction of stroke thrombus red blood cell content from multiparametric MRI

BACKGROUND AND PURPOSE

Thrombus red blood cell (RBC) content has been shown to be a significant factor influencing the efficacy of acute ischemic stroke treatment. In this study, our objective was to evaluate the ability of convolutional neural networks (CNNs) to predict ischemic stroke thrombus RBC content using multiparametric MR images.

MATERIALS AND METHODS

Retrieved stroke thrombi were scanned ex vivo using a three-dimensional multi-echo gradient echo sequence and histologically analyzed. 188 thrombus R2*, quantitative susceptibility mapping and late-echo GRE magnitude image slices were used to train and test a 3-layer CNN through cross-validation. Data augmentation techniques involving input equalization and random image transformation were employed to improve network performance. The network was assessed for its ability to quantitatively predict RBC content and to classify thrombi into RBC-rich and RBC-poor groups.

RESULTS

The CNN predicted thrombus RBC content with an accuracy of 62% (95% CI 48-76%) when trained on the original dataset and improved to 72% (95% CI 60-84%) on the augmented dataset. The network classified thrombi as RBC-rich or poor with an accuracy of 71% (95% CI 58-84%) and an area under the curve of 0.72 (95% CI 0.57-0.87) when trained on the original dataset and improved to 80% (95% CI 69-91%) and 0.84 (95% CI 0.73-0.95), respectively, on the augmented dataset.

CONCLUSIONS

The CNN was able to accurately predict thrombus RBC content using multiparametric MR images, and could provide a means to guide treatment strategy in acute ischemic stroke.

MicroCT and Histological Analysis of Clot Composition in Acute Ischemic Stroke : A Comparative Study of MT-Retrieved Clots and Clot Analogs

PURPOSE

Assessing clot composition on prethrombectomy computed tomography (CT) imaging may help in stroke treatment planning. In this study we seek to use microCT imaging of fabricated blood clots to understand the relationship between CT radiographic signals and the biological makeup.

METHODS

Clots (n = 10) retrieved by mechanical thrombectomy (MT) were collected, and 6 clot analogs of varying RBC composition were made. We performed paired microCT and histological image analysis of all 16 clots using a ScanCo microCT 100 (4.9 µm resolution) and standard H&E staining (imaged at 40×). From these data types, first order statistic (FOS) radiomics were computed from microCT, and percent composition of RBCs (%RBC) was computed from histology. Polynomial and linear regression (LR) were used to build statistical models based on retrieved thrombus microCT and %RBC that were evaluated for their ability to predict the %RBC of clot analogs from mean HU. Correlation analyses of microCT FOS with composition were completed for both retrieved clots and analogs.

RESULTS

The LR model fits relating MT-retrieved clot %RBC with mean (R2 = 0.625, p = 0.006) and standard deviation (R2 = 0.564, p < 0.05) in HUs on microCT were significant. Similarly, LR models relating analog histological %RBC to analog protocol %RBC (R2 = 0.915, p = 0.003) and mean HUs on microCT (R2 = 0.872, p = 0.007) were also significant. When the LR model built using MT-retrieved clots was used to predict analog %RBC from mean HUs, significant correlation was observed between predictions and actual histological %RBC (R2 = 0.852, p = 0.009). For retrieved clots, significant correlations were observed for energy and total energy with %RBC and %FP (|R| > 0.7, q < 0.01). Analogs further demonstrated significant correlation between FOS energy, total energy, variance and %WBC (|R| > 0.9, q < 0.01).

CONCLUSION

MicroCT can be used to build models that predict AIS clot composition from routine CT parameters and help us to better understand radiomic signatures associated with clot composition and first pass outcomes. In future work, such observations can be used to better infer clot composition and inform thrombectomy prognostics from pretreatment CTs.

Radiomics features on computed tomography reflect thrombus histological age prior to endovascular treatment of acute ischemic stroke

PURPOSE

To investigate the role of radiomics features in thrombus age identification and establish a CT-based radiomics model for predicting thrombus age of large vessel occlusion stroke patients.

METHODS

We retrospectively reviewed patients with middle cerebral artery occlusion receiving mechanical thrombectomy from July 2020 to March 2022 at our center. The retrieved clots were stained with Hematoxylin and Eosin (H&E) and determined as fresh or older thrombi based on coagulation age. Clot-derived radiomics features were selected by least absolute shrinkage and selection operator (LASSO) regression analysis, by which selected radiomics features were integrated into the Rad-score via the corresponding coefficients. The prediction performance of Rad-score in thrombus age was evaluated with the area under the curve (AUC) of receiver operating characteristic (ROC) curve analysis.

RESULTS

A total of 104 patients were included in our analysis, with 52 in training and 52 in validation cohort. Older thrombi were characterized with delayed procedure time, worse functional outcome and marginally associated with more attempts of device. We extracted 982 features from NCCT images. Following T test and LASSO analysis in training cohort, six radiomics features were selected, based on which the Rad-score was generated by the linear combination of features. The Rad-score showed satisfactory performance in distinguishing fresh with older thrombi, with the AUC of 0.873 (95 %CI: 0.777-0.956) and 0.773 (95 %CI: 0.636-0.910) in training and validation cohort, respectively.

CONCLUSION

This study established and validated a CT-based radiomics model that could accurately differentiate fresh with older thrombi for stroke patients receiving mechanical thrombectomy.

Automated Intracranial Clot Detection: A Promising Tool for Vascular Occlusion Detection in Non-Enhanced CT

(1) Background: to test the diagnostic performance of a fully convolutional neural network-based software prototype for clot detection in intracranial arteries using non-enhanced computed tomography (NECT) imaging data. (2) Methods: we retrospectively identified 85 patients with stroke imaging and one intracranial vessel occlusion. An automated clot detection prototype computed clot location, clot length, and clot volume in NECT scans. Clot detection rates were compared to the visual assessment of the hyperdense artery sign by two neuroradiologists. CT angiography (CTA) was used as the ground truth. Additionally, NIHSS, ASPECTS, type of therapy, and TOAST were registered to assess the relationship between clinical parameters, image results, and chosen therapy. (3) Results: the overall detection rate of the software was 66%, while the human readers had lower rates of 46% and 24%, respectively. Clot detection rates of the automated software were best in the proximal middle cerebral artery (MCA) and the intracranial carotid artery (ICA) with 88-92% followed by the more distal MCA and basilar artery with 67-69%. There was a high correlation between greater clot length and interventional thrombectomy and between smaller clot length and rather conservative treatment. (4) Conclusions: the automated clot detection prototype has the potential to detect intracranial arterial thromboembolism in NECT images, particularly in the ICA and MCA. Thus, it could support radiologists in emergency settings to speed up the diagnosis of acute ischemic stroke, especially in settings where CTA is not available.

CT-based thrombus radiomics nomogram for predicting secondary embolization during mechanical thrombectomy for large vessel occlusion

Background and aims

Secondary embolization (SE) during mechanical thrombectomy (MT) for cerebral large vessel occlusion (LVO) could reduce the anterior blood flow and worsen clinical outcomes. The current SE prediction tools have limited accuracy. In this study, we aimed to develop a nomogram to predict SE following MT for LVO based on clinical features and radiomics extracted from computed tomography (CT) images.

Materials and methods

A total of 61 patients with LVO stroke treated by MT at Beijing Hospital were included in this retrospective study, of whom 27 developed SE during the MT procedure. The patients were randomly divided (7:3) into training (n = 42) and testing (n = 19) cohorts. The thrombus radiomics features were extracted from the pre-interventional thin-slice CT images, and the conventional clinical and radiological indicators associated with SE were recorded. A support vector machine (SVM) learning model with 5-fold cross-verification was used to obtain the radiomics and clinical signatures. For both signatures, a prediction nomogram for SE was constructed. The signatures were then combined using the logistic regression analysis to construct a combined clinical radiomics nomogram.

Results

In the training cohort, the area under the receiver operating characteristic curve (AUC) of the nomograms was 0.963 for the combined model, 0.911 for the radiomics, and 0.891 for the clinical model. Following validation, the AUCs were 0.762 for the combined model, 0.714 for the radiomics model, and 0.637 for the clinical model. The combined clinical and radiomics nomogram had the best prediction accuracy in both the training and test cohort.

Conclusion

This nomogram could be used to optimize the surgical MT procedure for LVO based on the risk of developing SE.

Clot-based radiomics model for cardioembolic stroke prediction with CT imaging before recanalization: a multicenter study

OBJECTIVES

To develop a clot-based radiomics model using CT imaging radiomic features and machine learning to identify cardioembolic (CE) stroke before mechanical thrombectomy (MTB) in patients with acute ischemic stroke (AIS).

MATERIALS AND METHODS

This retrospective four-center study consecutively included 403 patients with AIS who sequentially underwent CT and MTB between April 2016 and July 2021. These were grouped into training, testing, and external validation cohorts. Thrombus-extracted radiomic features and basic information were gathered to construct a machine learning model to predict CE stroke. The radiological characteristics and basic information were used to build a routine radiological model. A combined radiomics and radiological features model was also developed. The performances of all models were evaluated and compared in the validation cohort. A histological analysis helped further assess the proposed model in all patients.

RESULTS

The radiomics model yielded an area under the curve (AUC) of 0.838 (95% confidence interval [CI], 0.771-0.891) for predicting CE stroke in the validation cohort, significantly higher than the radiological model (AUC, 0.713; 95% CI, 0.636-0.781; p = 0.007) but similar to the combined model (AUC, 0.855; 95% CI, 0.791-0.906; p = 0.14). The thrombus radiomic features achieved stronger correlations with red blood cells (|r_max|, 0.74 vs. 0.32) and fibrin and platelet (|r_max|, 0.68 vs. 0.18) than radiological characteristics.

CONCLUSION

The proposed CT-based radiomics model could reliably predict CE stroke in AIS, performing better than the routine radiological method.

KEY POINTS

• Admission CT imaging could offer valuable information to identify the acute ischemic stroke source by radiomics analysis. • The proposed CT imaging-based radiomics model yielded a higher area under the curve (0.838) than the routine radiological method (0.713; p = 0.007). • Several radiomic features showed significantly stronger correlations with two main thrombus constituents (red blood cells, |r_max|, 0.74; fibrin and platelet, |r_max|, 0.68) than routine radiological characteristics.

A nomogram for predicting thrombus composition in stroke patients with large vessel occlusion: combination of thrombus density and perviousness with clinical features

PURPOSE

To establish a nomogram incorporating pretreatment imaging parameters and clinical characteristics for predicting the thrombus composition of acute ischemic stroke (AIS) with large vessel occlusion (LVO).

METHODS

We retrospectively enrolled patients with occlusion of the Middle Cerebral Artery (MCA) who underwent Mechanical Thrombectomy (MT). Retrieved thrombi were stained with Hematoxylin and Eosin (H&E) and Martius Scarlet Blue (MSB). Thrombi are assigned to the Fibrin-rich or RBC-rich group based on the relative fractions of Red Blood Cells (RBC), fibrin, and platelet. The independent risk factors for Fibrin-rich clots were determined via univariate and multivariate logistic regression analysis and were then integrated to establish a nomogram.

RESULTS

In total, 98 patients were included in this study. Patients with fibrin-rich clots had worse functional outcome [modified Rankin scale (mRS) 0-2, 34.7% vs 63.2%, p = 0.005], longer procedure time (76.8 min vs 50.8 min, p = 0.001), and increased maneuvers of MT (1.84 vs 1.46, p = 0.703) than those with RBC-rich clots. The independent risk factors for Fibrin-rich clots were lower perviousness measured by Non-Contrast Computer Tomography (NCCT) and CT Angiography (CTA), lower thrombus relative attenuation on NCCT, elevated Platelet-WBC ratio (PWR) of admission peripheral blood, and previous antithrombotic medication. The nomogram showed good discrimination with an area under the Receiver Operating Characteristic (ROC) curve (AUC) of 0.852 (95% CI: 0.778-0.926). The calibration curve and decision curve analysis also displayed satisfactory accuracy and clinical utility.

CONCLUSION

This study has developed and internally validated an easy-to-use nomogram which can help predict clot composition and optimize therapeutic strategies for thrombectomy.

Spatial heterogeneity of occlusive thrombus in acute ischemic stroke: A systematic review

Following the advent of mechanical thrombectomy, occlusive clots in ischemic stroke have been amply characterized using conventional histopathology. Many studies have investigated the compositional variability of thrombi and the consequences of thrombus composition on treatment response. More recent evidence has emerged about the spatial heterogeneity of the clot or the preferential distribution of its components and compact nature. Here we review this emerging body of evidence, discuss its potential clinical implications, and propose the development of adequate characterization techniques.

Absence of susceptibility vessel sign and hyperdense vessel sign in patients with cancer-related stroke

Background and aim

Identification of paraneoplastic hypercoagulability in stroke patients helps to guide investigations and prevent stroke recurrence. A previous study demonstrated an association between the absence of the susceptibility vessel sign (SVS) on brain MRI and active cancer in patients treated with mechanical thrombectomy. The present study aimed to confirm this finding and assess an association between the absence of the hyperdense vessel sign (HVS) on head CT and active cancer in all stroke patients.

Methods

SVS and HVS status on baseline imaging were retrospectively assessed in all consecutive stroke patients treated at a comprehensive stroke center between 2015 and 2020. Active cancer, known at the time of stroke or diagnosed within 1 year after stroke (occult cancer), was identified. Adjusted odds ratios (aOR) and their 95% confidence interval (CI) for the association between the thrombus imaging characteristics and cancer were calculated using multivariable logistic regression.

Results

Of the 2,256 patients with thrombus imaging characteristics available at baseline, 161 had an active cancer (7.1%), of which 36 were occult at the time of index stroke (1.6% of the total). The absence of SVS was associated with active cancer (aOR 3.14, 95% CI 1.45-6.80). No significance was reached for the subgroup of occult cancer (aOR 3.20, 95% CI 0.73-13.94). No association was found between the absence of HVS and active cancer (aOR 1.07, 95% CI 0.54-2.11).

Conclusion

The absence of SVS but not HVS could help to identify paraneoplastic hypercoagulability in stroke patients with active cancer and guide patient care.

Updates in mechanical thrombectomy

Stroke is a leading cause of morbidity and mortality. The advent of mechanical thrombectomy has largely improved patient outcomes. This article reviews the features and outcomes associated with aspiration, stent retrievers, and combination catheters used in current practice. There is also a discussion on clinical considerations based on anatomical features and clot composition. The reperfusion grading scale and outcome metrics commonly used following thrombectomy when a patient is still in the hospital are reviewed. Lastly, there are proposed discharge and outpatient follow-up goals in caring for patients hospitalized for a stroke.

First Experience with the Nimbus Stentretriever

Purpose

To share our first experience with the Nimbus stentretriever, a multizone device designed to assist neurointerventionalists in handling fibrin-rich clots in endovascular stroke treatment.

Methods

We retrospectively analyzed the data of patients who were treated with the Nimbus stentretriever at our high-volume stroke center between May 2021 and May 2022. We evaluated the number of passes before Nimbus was used, the number of passes with nimbus, as well as the recanalization success before and after Nimbus according to the modified treatment in cerebral ischemia (mTICI) scale. Also, patient characteristics, procedural times and clinical outcomes were documented.

Results

A total of 21 consecutive patients were included in the study. An mTICI 2b/3 could be achieved in 76.2% and mTICI 2c/3 could be achieved in 57.1%. The mean number of passes was 3.4 before the use of Nimbus, 2.2 with Nimbus, and 5.4 for all passes with and without Nimbus and 4 occlusions (19.0%) were successfully recanalized with direct aspiration after the use of Nimbus. We observed seven subarachnoid hemorrhages (33.3%) and two cases of vasospasm.

Conclusion

In our series, the use of Nimbus resulted in successful recanalization in half of the patients after otherwise unsuccessful thrombectomy maneuvers; therefore, it should be considered as a rescue option if the maneuver with conventional stent retrievers was unsuccessful.

Patient and procedure selection for mechanical thrombectomy: Toward personalized medicine and the role of artificial intelligence

Mechanical thrombectomy (MT) for ischemic stroke due to large vessel occlusion is standard of care. Evidence-based guidelines on eligibility for MT have been outlined and evidence to extend the treatment benefit to more patients, particularly those at the extreme ends of a stroke clinical severity spectrum, is currently awaited. As patient selection continues to be explored, there is growing focus on procedure selection including the tools and techniques of thrombectomy and associated outcomes. Artificial intelligence (AI) has been instrumental in the area of patient selection for MT with a role in diagnosis and delivery of acute stroke care. Machine learning algorithms have been developed to detect cerebral ischemia and early infarct core, presence of large vessel occlusion, and perfusion deficit in acute ischemic stroke. Several available deep learning AI applications provide ready visualization and interpretation of cervical and cerebral arteries. Further enhancement of AI techniques to potentially include automated vessel probe tools in suspected large vessel occlusions is proposed. Value of AI may be extended to assist in procedure selection including both the tools and technique of thrombectomy. Delivering personalized medicine is the wave of the future and tailoring the MT treatment to a stroke patient is in line with this trend.

Hyperdense Artery Sign in Patients With Acute Ischemic Stroke-Automated Detection With Artificial Intelligence-Driven Software

Background

Hyperdense artery sign (HAS) on non-contrast CT (NCCT) can indicate a large vessel occlusion (LVO) in patients with acute ischemic stroke. HAS detection belongs to routine reporting in patients with acute stroke and can help to identify patients in whom LVO is not initially suspected. We sought to evaluate automated HAS detection by commercial software and compared its performance to that of trained physicians against a reference standard.

Methods

Non-contrast CT scans from 154 patients with and without LVO proven by CT angiography (CTA) were independently rated for HAS by two blinded neuroradiologists and an AI-driven algorithm (Brainomix®). Sensitivity and specificity were analyzed for the clinicians and the software. As a secondary analysis, the clot length was automatically calculated by the software and compared with the length manually outlined on CTA images as the reference standard.

Results

Among 154 patients, 84 (54.5%) had CTA-proven LVO. HAS on the correct side was detected with a sensitivity and specificity of 0.77 (CI:0.66–0.85) and 0.87 (0.77–0.94), 0.8 (0.69–0.88) and 0.97 (0.89–0.99), and 0.93 (0.84–0.97) and 0.71 (0.59–0.81) by the software and readers 1 and 2, respectively. The automated estimation of the thrombus length was in moderate agreement with the CTA-based reference standard [intraclass correlation coefficient (ICC) 0.73].

Conclusion

Automated detection of HAS and estimation of thrombus length on NCCT by the tested software is feasible with a sensitivity and specificity comparable to that of trained neuroradiologists.

Endovascular thrombectomy for acute ischemic stroke

This review describes the evolution of endovascular treatment for acute ischemic stroke, current state of the art, and the challenges for the next decade. The rapid development of endovascular thrombectomy (EVT), from the first attempts into standard of care on a global scale, is one of the major achievements in modern medicine. It was possible thanks to the establishment of a scientific framework for patient selection, assessment of stroke severity and outcome, technical development by dedicated physicians and the MedTech industry, including noninvasive imaging for patient selection, and radiological outcome evaluation. A series of randomized controlled trials on EVT in addition to intravenous thrombolytics, with overwhelmingly positive results for anterior circulation stroke within 6 h of onset regardless of patient characteristics with a number needed to treat of less than 3 for any positive shift in outcome, paved the way for a rapid introduction of EVT into clinical practice. Within the "extended" time window of 6-24 h, the effect has been even greater for patients with salvageable brain tissue according to perfusion imaging with a number needed to treat below 2. Even so, EVT is only available for a small portion of stroke patients, and successfully recanalized EVT patients do not always achieve excellent functional outcome. The major challenges in the years to come include rapid prehospital detection of stroke symptoms, adequate clinical and radiological diagnosis of severe ischemic stroke cases, enabling effective recanalization by EVT in dedicated angiosuites, followed by personalized post-EVT stroke care.

Non contrast enhanced volumetric histology of blood clots through high resolution propagation-based X-ray microtomography

We have demonstrated the capability of laboratory propagation-based microtomography (miroCT) in non-destructive 3D virtual histopathology of human blood clots without any contrast agent. The volumetric information are valuable to understand the mechanical properties of clots which are crucial in selecting the most efficient mechanical thrombectomy method for clot extraction. Different clot types retrieved by mechanical thrombectomy from patient victims of acute ischemic stroke were evaluated through propagation-based microCT. The results were correlated with high-resolution scanning electron microscopy (SEM) images, confirming detected cellular and fibrillary structures. Calcifications appeared as glassy opacity areas with relatively intense signal on microCT images, also proved by energy-dispersive spectroscopy and X-ray diffraction. Hyperintense regions on the microCT corresponded to individual or compact aggregates of red blood cells, whereas fibrin dominated volumes appeared at consistently moderate to low normalized microCT values. Red blood cell shapes and sizes are consistent with the SEM observations. Together with other potential parameters, 3D porosity distribution and volume fraction of structures can be easily measured by microCT data. Further development of automated post-processing techniques for X-ray propagation-based micro/nanoCT, also based on machine learning algorithms, can enable high throughput analysis of blood clot composition and their 3D histological features on large sample cohorts.

Multimodality Characterization of the Clot in Acute Stroke

Aim: Current treatment of occluded cerebral vessels can be done by a variety of endovascular techniques. Sometimes, the clot responds in varying degrees to the treatment chosen. The Ex vivo characterization of the clot occluding the arteries in acute ischemic stroke can help in understanding the underlying imaging features obtained from pre-treatment brain scans. For this reason, we explored the potential of microCT when combined with electron microscopy for clot characterization. Results were compared to the clinical CT findings.

Methods: 16 patients (9 males, 8 females, age range 54–93 years) who were referred to our institution for acute stroke underwent dual-source CT.

Results: Clinical CT clots were seen as either iso or hyperdense. This was corroborated with micro-CT, and electron microscopy can show the detailed composition.

Conclusion: MicroCT values can be used as an indicator for red blood cells-rich composition of clots. Meaningful information regarding the clot composition and modalities of embedding along the stent retrievers can be obtained through a combination of microCT and electron microscopy.

Clot Composition Analysis as a Diagnostic Tool to Gain Insight into Ischemic Stroke Etiology: A Systematic Review

Mechanical thrombectomy renders the occluding clot available for analysis. Insights into thrombus composition could help establish the stroke cause. We aimed to investigate the value of clot composition analysis as a complementary diagnostic tool in determining the etiology of large vessel occlusion (LVO) ischemic strokes (International Prospective Register of Systematic Reviews [PROSPERO] registration # CRD42020199436). Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we ran searches on Medline (using the PubMed interface) and Web of Science for studies reporting analyses of thrombi retrieved from LVO stroke patients subjected to mechanical thrombectomy (January 1, 2006 to September 21, 2020). The PubMed search was updated weekly up to February 22, 2021. Reference lists of included studies and relevant reviews were hand-searched. From 1,714 identified studies, 134 eligible studies (97 cohort studies, 31 case reports, and six case series) were included in the qualitative synthesis. Physical, histopathological, biological, and microbiological analyses provided information about the gross appearance, mechanical properties, structure, and composition of the thrombi. There were non-unanimous associations of thrombus size, structure, and composition (mainly proportions of fibrin and blood formed elements) with the Trial of Org 10172 in Acute Stroke Treatment (TOAST) etiology and underlying pathologies, and similarities between cryptogenic thrombi and those of known TOAST etiology. Individual thrombus analysis contributed to the diagnosis, mainly in atypical cases. Although cohort studies report an abundance of quantitative rates of main thrombus components, a definite clot signature for accurate diagnosis of stroke etiology is still lacking. Nevertheless, the qualitative examination of the embolus remains an invaluable tool for diagnosing individual cases, particularly regarding atypical stroke causes.

How to Improve the Management of Acute Ischemic Stroke by Modern Technologies, Artificial Intelligence, and New Treatment Methods

Stroke remains one of the leading causes of death and disability in Europe. The European Stroke Action Plan (ESAP) defines four main targets for the years 2018 to 2030. The COVID-19 pandemic forced the use of innovative technologies and created pressure to improve internet networks. Moreover, 5G internet network will be helpful for the transfer and collecting of extremely big databases. Nowadays, the speed of internet connection is a limiting factor for robotic systems, which can be controlled and commanded potentially from various places in the world. Innovative technologies can be implemented for acute stroke patient management soon. Artificial intelligence (AI) and robotics are used increasingly often without the exception of medicine. Their implementation can be achieved in every level of stroke care. In this article, all steps of stroke health care processes are discussed in terms of how to improve them (including prehospital diagnosis, consultation, transfer of the patient, diagnosis, techniques of the treatment as well as rehabilitation and usage of AI). New ethical problems have also been discovered. Everything must be aligned to the concept of “time is brain”.