Abstract TP427: Predicting Outcome of Acute Ischemic Stroke With Cancer-Related Coagulopathy by Machine-Learning Based Feature-Engineering

Stroke ◽  
2020 ◽  
Vol 51 (Suppl_1) ◽  
Author(s):  
Masaki Ito ◽  
Satoshi Kuroda ◽  
Hidetsugu Asanoi ◽  
Taku Sugiyama ◽  
Takafumi Shindo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Oral Anticoagulants ◽  
Area Under The Curve ◽  
Stroke Recurrence ◽  
Support Vector ◽  
Feature Engineering ◽  
Stroke Subtype ◽  
D Dimer

Background: Outcomes of stroke with cancer-related coagulopathy (Trousseau syndrome) is predominantly attributed to cancer managements; however, stroke management by anticoagulants can contribute to the best supportive care. We aimed to find predictors of the outcome by multivariate analysis, including machine-learning (ML) based feature-engineering. Methods: A single-center retrospective study using a prospective cohort was conducted between April 2011 and June 2019. Out of the cumulative total of 110 acute ischemic stroke patients with malignancy, 65 were treated with anticoagulants, including warfarin (n=19), non-vitamin K dependent oral anticoagulants (NOAC, n=40), or subcutaneous heparin injections (n=6). Cancer-related coagulopathy was defined by elevated blood D-dimer levels at the onset of stroke with malignancy. The incidence of stroke recurrence was analyzed using 40 variables by logistic regression (LR) and in-house ML programs. Results: Out of 65 instances of the cancer-related stroke, 12 (18.5%) stroke recurrences were observed during 455 ± 70 days (mean, SEM). The stroke subtypes were cardioembolism (n=2), stroke with undetermined etiology (n=23) or other determined etiology (cancer-related coagulopathy, n=40). Multivariate LR revealed significant predictors of stroke recurrence, including NOAC usage and stroke subtype. Whereas, combination of forward stepwise selection and Naïve-Bayes (NB) or support vector machine found the blood D-dimer level as an additional important predictor. Input the D-dimer level in addition to NOAC usage and stroke subtype yielded the best area under the curve (AUC) for either of LR or NB compared to input warfarin or heparin usage. AUC for the LR for these 3 variables was better than that for NB. Conclusion: This study suggests the incidence of stroke recurrence is high in this clinical situation. NOAC usage, stroke subtype, and blood D-dimer level at the onset of stroke have predictive value of the outcome.

Cancer-Associated Hypercoagulation Increases the Risk of Early Recurrent Stroke in Patients with Active Cancer

2018 ◽  
Vol 46 (1-2) ◽  
pp. 46-51 ◽  
Author(s):  
Jun Fujinami ◽  
Tomoyuki Ohara ◽  
Fukiko Kitani-Morii ◽  
Yasuhiro Tomii ◽  
Naoki Makita ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Recurrent Stroke ◽  
Stroke Recurrence ◽  
Multivariable Logistic Regression Analysis ◽  
Stroke Patients ◽  
Recurrent Cancer ◽  
Systemic Metastasis ◽  
D Dimer ◽  
Active Cancer

Background: This study assessed the incidence and predictors of short-term stroke recurrence in ischemic stroke patients with active cancer, and elucidated whether cancer-associated hypercoagulation is related to early recurrent stroke. Methods: We retrospectively enrolled acute ischemic stroke patients with active cancer admitted to our hospital between 2006 and 2017. Active cancer was defined as diagnosis or treatment for any cancer within 12 months before stroke onset, known recurrent cancer or metastatic disease. The primary clinical outcome was recurrent ischemic stroke within 30 days. Results: One hundred ten acute ischemic stroke patients with active cancer (73 men, age 71.3 ± 10.1 years) were enrolled. Of those, recurrent stroke occurred in 12 patients (11%). When patients with and without recurrent stroke were compared, it was found that those with recurrent stroke had a higher incidence of pancreatic cancer (33 vs. 10%), systemic metastasis (75 vs. 39%), multiple vascular territory infarctions (MVTI; 83 vs. 40%), and higher ­D-dimer levels (16.9 vs. 2.9 µg/mL). Multivariable logistic regression analysis showed that each factor mentioned above was not significantly associated with stroke recurrence independently, but high D-dimer (hDD) levels (≥10.4 µg/mL) and MVTI together were significantly associated with stroke recurrence (OR 6.20, 95% CI 1.42–30.7, p = 0.015). Conclusions: Ischemic stroke patients with active cancer faced a high risk of early recurrent stroke. The concurrence of hDD levels (≥10.4 µg/mL) and MVTI was an independent predictor of early recurrent stroke in active cancer patients. Our findings suggest that cancer-associated hypercoagulation increases the early recurrent stroke risk.

scholarly journals Machine Learning-Based Three-Month Outcome Prediction in Acute Ischemic Stroke: A Single Cerebrovascular-Specialty Hospital Study in South Korea

Diagnostics ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1909
Author(s):  
Dougho Park ◽  
Eunhwan Jeong ◽  
Haejong Kim ◽  
Hae Wook Pyun ◽  
Haemin Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Functional Outcome ◽  
Outcome Prediction ◽  
Prediction Models ◽  
Gradient Boosting ◽  
Support Vector ◽  
Extreme Gradient Boosting

Background: Functional outcomes after acute ischemic stroke are of great concern to patients and their families, as well as physicians and surgeons who make the clinical decisions. We developed machine learning (ML)-based functional outcome prediction models in acute ischemic stroke. Methods: This retrospective study used a prospective cohort database. A total of 1066 patients with acute ischemic stroke between January 2019 and March 2021 were included. Variables such as demographic factors, stroke-related factors, laboratory findings, and comorbidities were utilized at the time of admission. Five ML algorithms were applied to predict a favorable functional outcome (modified Rankin Scale 0 or 1) at 3 months after stroke onset. Results: Regularized logistic regression showed the best performance with an area under the receiver operating characteristic curve (AUC) of 0.86. Support vector machines represented the second-highest AUC of 0.85 with the highest F1-score of 0.86, and finally, all ML models applied achieved an AUC > 0.8. The National Institute of Health Stroke Scale at admission and age were consistently the top two important variables for generalized logistic regression, random forest, and extreme gradient boosting models. Conclusions: ML-based functional outcome prediction models for acute ischemic stroke were validated and proven to be readily applicable and useful.

scholarly journals Predicting 1-Hour Thrombolysis Effect of r-tPA in Patients With Acute Ischemic Stroke Using Machine Learning Algorithm

2022 ◽  
Vol 12 ◽  
Author(s):  
Bin Zhu ◽  
Jianlei Zhao ◽  
Mingnan Cao ◽  
Wanliang Du ◽  
Liuqing Yang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Learning Algorithm ◽  
Degradation Products ◽  
Early Stage ◽  
Area Under The Curve ◽  
Recursive Feature Elimination ◽  
Gradient Boosting ◽  
Data Sets

Background: Thrombolysis with r-tPA is recommended for patients after acute ischemic stroke (AIS) within 4.5 h of symptom onset. However, only a few patients benefit from this therapeutic regimen. Thus, we aimed to develop an interpretable machine learning (ML)–based model to predict the thrombolysis effect of r-tPA at the super-early stage.Methods: A total of 353 patients with AIS were divided into training and test data sets. We then used six ML algorithms and a recursive feature elimination (RFE) method to explore the relationship among the clinical variables along with the NIH stroke scale score 1 h after thrombolysis treatment. Shapley additive explanations and local interpretable model–agnostic explanation algorithms were applied to interpret the ML models and determine the importance of the selected features.Results: Altogether, 353 patients with an average age of 63.0 (56.0–71.0) years were enrolled in the study. Of these patients, 156 showed a favorable thrombolysis effect and 197 showed an unfavorable effect. A total of 14 variables were enrolled in the modeling, and 6 ML algorithms were used to predict the thrombolysis effect. After RFE screening, seven variables under the gradient boosting decision tree (GBDT) model (area under the curve = 0.81, specificity = 0.61, sensitivity = 0.9, and F1 score = 0.79) demonstrated the best performance. Of the seven variables, activated partial thromboplastin clotting time (time), B-type natriuretic peptide, and fibrin degradation products were the three most important clinical characteristics that might influence r-tPA efficiency.Conclusion: This study demonstrated that the GBDT model with the seven variables could better predict the early thrombolysis effect of r-tPA.

scholarly journals Anticoagulation in the early phase of non-valvular atrial fibrillation-related acute ischemic stroke: where do we stand?

2019 ◽  
Vol 13 (1) ◽  
pp. 24-31
Author(s):  
Luca Masotti ◽  
Elisa Grifoni ◽  
Alessandro Dei ◽  
Vieri Vannucchi ◽  
Federico Moroni ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Early Phase ◽  
Randomized Clinical Trials ◽  
Oral Anticoagulants ◽  
Real Life ◽  
Stroke Onset ◽  
Phase Iii ◽  
Stroke Recurrence

The balance between the risk of early stroke recurrence and hemorrhagic transformation represents the cornerstone of practical management of non-valvular atrial fibrillation (NVAF)-related acute ischemic stroke (AIS). Patients who receive antithrombotic therapy as secondary prevention in the early phase of NVAF-related AIS have a better prognosis compared with patients who do not receive antithrombotic treatment. Recently, the RAF study showed that the best efficacy/safety profile was associated with anticoagulation started between 4 and 14 days from stroke onset. Based on the RAF study, the 2018 American Heart Association/American Stroke Association (AHA/ASA) guidelines suggest starting anticoagulants between 4 and 14 days from stroke onset with a class of recommendation IIa. Strong evidence for the use of direct oral anticoagulants (DOACs) in the early phase of NVAF-related AIS is lacking, because this kind of patients were excluded from phase III randomized clinical trials (RCT) and ad hoc RCTs are ongoing. However, the real life evidence suggests that early starting time of DOACs in patients with NVAF-related AIS is safe and associated with low recurrence risk and all-cause mortality. In the present review the Authors provide an update on anticoagulation in the early phase of NVAF-related AIS with focus on DOACs.

scholarly journals Prediction of Long-Term Stroke Recurrence Using Machine Learning Models

2021 ◽  
Vol 10 (6) ◽  
pp. 1286
Author(s):  
Vida Abedi ◽  
Venkatesh Avula ◽  
Durgesh Chaudhary ◽  
Shima Shahjouei ◽  
Ayesha Khan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ischemic Stroke ◽  
Performance Metrics ◽  
Gradient Boosting ◽  
Stroke Recurrence ◽  
Support Vector ◽  
Sampling Strategies ◽  
Specificity And Sensitivity ◽  
Extreme Gradient Boosting

Background: The long-term risk of recurrent ischemic stroke, estimated to be between 17% and 30%, cannot be reliably assessed at an individual level. Our goal was to study whether machine-learning can be trained to predict stroke recurrence and identify key clinical variables and assess whether performance metrics can be optimized. Methods: We used patient-level data from electronic health records, six interpretable algorithms (Logistic Regression, Extreme Gradient Boosting, Gradient Boosting Machine, Random Forest, Support Vector Machine, Decision Tree), four feature selection strategies, five prediction windows, and two sampling strategies to develop 288 models for up to 5-year stroke recurrence prediction. We further identified important clinical features and different optimization strategies. Results: We included 2091 ischemic stroke patients. Model area under the receiver operating characteristic (AUROC) curve was stable for prediction windows of 1, 2, 3, 4, and 5 years, with the highest score for the 1-year (0.79) and the lowest score for the 5-year prediction window (0.69). A total of 21 (7%) models reached an AUROC above 0.73 while 110 (38%) models reached an AUROC greater than 0.7. Among the 53 features analyzed, age, body mass index, and laboratory-based features (such as high-density lipoprotein, hemoglobin A1c, and creatinine) had the highest overall importance scores. The balance between specificity and sensitivity improved through sampling strategies. Conclusion: All of the selected six algorithms could be trained to predict the long-term stroke recurrence and laboratory-based variables were highly associated with stroke recurrence. The latter could be targeted for personalized interventions. Model performance metrics could be optimized, and models can be implemented in the same healthcare system as intelligent decision support for targeted intervention.

scholarly journals Machine Learning-Based Model for Predicting Incidence and Severity of Acute Ischemic Stroke in Anterior Circulation Large Vessel Occlusion

2021 ◽  
Vol 12 ◽  
Author(s):  
Junzhao Cui ◽  
Jingyi Yang ◽  
Kun Zhang ◽  
Guodong Xu ◽  
Ruijie Zhao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
External Validation ◽  
Neurological Impairment ◽  
Large Vessel ◽  
Support Vector ◽  
Large Vessel Occlusion ◽  
Anterior Circulation ◽  
Vessel Occlusion

Objectives: Patients with anterior circulation large vessel occlusion are at high risk of acute ischemic stroke, which could be disabling or fatal. In this study, we applied machine learning to develop and validate two prediction models for acute ischemic stroke (Model 1) and severity of neurological impairment (Model 2), both caused by anterior circulation large vessel occlusion (AC-LVO), based on medical history and neuroimaging data of patients on admission.Methods: A total of 1,100 patients with AC- LVO from the Second Hospital of Hebei Medical University in North China were enrolled, of which 713 patients presented with acute ischemic stroke (AIS) related to AC- LVO and 387 presented with the non-acute ischemic cerebrovascular event. Among patients with the non-acute ischemic cerebrovascular events, 173 with prior stroke or TIA were excluded. Finally, 927 patients with AC-LVO were entered into the derivation cohort. In the external validation cohort, 150 patients with AC-LVO from the Hebei Province People's Hospital, including 99 patients with AIS related to AC- LVO and 51 asymptomatic AC-LVO patients, were retrospectively reviewed. We developed four machine learning models [logistic regression (LR), regularized LR (RLR), support vector machine (SVM), and random forest (RF)], whose performance was internally validated using 5-fold cross-validation. The performance of each machine learning model for the area under the receiver operating characteristic curve (ROC-AUC) was compared and the variables of each algorithm were ranked.Results: In model 1, among the included patients with AC-LVO, 713 (76.9%) and 99 (66%) suffered an acute ischemic stroke in the derivation and external validation cohorts, respectively. The ROC-AUC of LR, RLR and SVM were significantly higher than that of the RF in the external validation cohorts [0.66 (95% CI 0.57–0.74) for LR, 0.66 (95% CI 0.57–0.74) for RLR, 0.55 (95% CI 0.45–0.64) for RF and 0.67 (95% CI 0.58–0.76) for SVM]. In model 2, 254 (53.9%) and 31 (37.8%) patients suffered disabling ischemic stroke in the derivation and external validation cohorts, respectively. There was no difference in AUC among the four machine learning algorithms in the external validation cohorts.Conclusions: Machine learning methods with multiple clinical variables have the ability to predict acute ischemic stroke and the severity of neurological impairment in patients with AC-LVO.

Abstract WP368: Evaluation of Brain Regional Characteristics Related With Post-Stroke Delirium Using Machine Learning and Statistical Analysis

Stroke ◽  
2020 ◽  
Vol 51 (Suppl_1) ◽  
Author(s):  
Seo Hyun Lee ◽  
Yun Kyong Hyon ◽  
Hee Won Yang ◽  
Ki Wan Jeon ◽  
Ki Wan Jeon ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ischemic Stroke ◽  
Statistical Analysis ◽  
Acute Ischemic Stroke ◽  
Cerebral Hemisphere ◽  
Support Vector ◽  
Stroke Patients ◽  
Regional Characteristics ◽  
Post Stroke ◽  
The Brain

Background: The present study was performed to evaluate the brain regional characteristics related with development of post-stroke delirium using the machine learning and statistical analysis. Method: We used clinical and radiological data prospectively collected from 675 acute ischemic stroke patients, who were admitted in stroke unit from August 2017 to July 2018. Delirium occurrence in the patients was screened with Confusion Assessment Method and finally diagnosed using the criteria of the Diagnostic and Statistical Manual of Mental Disorders (fifth edition). Three machine learning models, Support Vector Machine (SVM), Random Forest (RF) and Tree-based Gradient Boosting (XGBoost), were applied for the prediction of post-stroke delirium with the clinical and radiologic data. And logistic regression analysis was performed to evaluate the significance of the brain regional parameters included in the importance features which were obtained from the XGBoost result. Results: Post-stroke delirium occurred in 66 (9.8%) of the total patients. On the comparison of the test accuracy to predict delirium occurrence, RF (94%), XGBoost test (93%), and SVM (89%) showed similar prediction rates. Of the brain regional parameters included in the top 30 feature importance, right side cerebral hemisphere, non-lacunar infarction, severity of periventricular white matter changes, acute temporal lobe lesion, cerebellum, brain stem, and previous lesions developed on right side cerebral hemisphere, and in temporal or frontal lobe. Conclusion: The present study shows that the brain regional characteristics related with the post-stroke delirium are shown to be significant when controlling the other features using statistical analysis with machine learning. Even though we need more studies to validate the relationships between post-stroke delirium and brain regional characteristics, the present brain regional characteristics could provide significant evidences to predict post-stroke delirium for the acute ischemic stroke patients.

Abstract TP23: Prestroke Glucose Control Can be a Predictor for Outcome in Endovascular Therapy of Acute Ischemic Stroke

Stroke ◽  
2017 ◽  
Vol 48 (suppl_1) ◽  
Author(s):  
Jun-Young Chang ◽  
Moon Ku Han ◽  
Kyu Sun Yum ◽  
Sangkil Lee ◽  
Tai Hwan Park ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Glycemic Control ◽  
Endovascular Treatment ◽  
Acute Ischemic Stroke ◽  
Functional Outcome ◽  
Cumulative Risk ◽  
Hemorrhagic Transformation ◽  
Stroke Recurrence ◽  
Stroke Subtype ◽  
Endovascular Recanalization

Background: The purpose of the study is to evaluate whether prestroke glycemic control is associated with functional outcome in patients with acute ischemic stroke after endovascular treatment. Methods: We reviewed the acute ischemic stroke patients who underwent endovascular recanalization in the participating centers between April 2008 and March 2015 from the Clinical Research Center for Stroke-5th division (CRCS-5) registry. The relationship between the level of HbA1c on admission and functional outcome at 3 month, stroke recurrence and composite outcome (stroke, myocardial infarction, or vascular death) occurrence were assessed. Results: All 829 study subjects were classified as 4 groups according to quartiles of HbA1c on admission: 1 st quartile (HbA1c ≤5.6%), 2 nd quartile (5.6% < HbA1c ≤5.9%), 3 rd quartile (5.9% < HbA1c ≤6.5%), 4 th quartile (HbA1c >6.5%). END occurred more frequently in the highest quartile of HbA1c (P=0.02). Among the components of END, the frequency of symptomatic hemorrhagic transformation occurred more often in the group with higher quartiles (P=0.03), while stroke recurrence or recurrence was not significantly different according to the quartiles of HbA1c (P=0.27). After adjusting for significant variables (age, sex, initial NIHSS, diabetes, complete recanalization, procedure time, occurrence of END, P<0.05), HbA1c on admission >6.5% was still inversely associated with favorable functional outcome at 3 month (adjusted OR 0.48, 95% CI 0.25-0.93 as quartiles, adjusted OR 0.40, 95% CI 0.22-0.73 as a dichotomized variable). No significant heterogeneities were observed according to the age, diagnosis of diabetes on admission, stroke subtype, recanalization degree, and reperfusion time. The cumulative risk of both stroke recurrence and composite even rates were not significantly different according to the quartiles of HbA1c on admission (P=0.64, P=0.19, respectively). Conclusion: Prestroke glycemic control is associated with occurrence of symptomatic hemorrhagic ransformaion and functional outcome in patients with acute ischemic stroke after endovascular treatment. More stringent glycemic control of HbA1c below or equal to 6.5 % may have beneficial effect on neurological recovery after stroke.

Evaluation of the clot burden score (cbs) for acute ischemic stroke (ais) in intent-to-treat patients.

2019 ◽  
Vol 1 (1) ◽  
pp. 11-15 ◽  
Author(s):  
Sarah Yaziz ◽  
Ahmad Sobri Muda ◽  
Wan Asyraf Wan Zaidi ◽  
Nik Azuan Nik Ismail
Keyword(s):  
Ischemic Stroke ◽  
Clinical Outcome ◽  
Acute Ischemic Stroke ◽  
Predictive Value ◽  
Area Under The Curve ◽  
Rank Correlation ◽  
Treatment Decision ◽  
Roc Curves ◽  
Significant Negative Correlation ◽  
Clot Burden

Background : The clot burden score (CBS) is a scoring system used in acute ischemic stroke (AIS) to predict patient outcome and guide treatment decision. However, CBS is not routinely practiced in many institutions. This study aimed to investigate the feasibility of CBS as a relevant predictor of good clinical outcome in AIS cases. Methods:  A retrospective data collection and review of AIS patients in a teaching hospital was done from June 2010 until June 2015. Patients were selected following the inclusion and exclusion criteria. These patients were followed up after 90 days of discharge. The Modified Rankin scale (mRS) was used to assess their outcome (functional status). Linear regression Spearman Rank correlation was performed between the CBS and mRS. The quality performance of the correlations was evaluated using Receiver operating characteristic (ROC) curves. Results: A total of 89 patients with AIS were analysed, 67.4% (n=60) male and 32.6% (n=29) female. Twenty-nine (29) patients (33.7%) had a CBS ?6, 6 patients (6.7%) had CBS <6, while 53 patients (59.6%) were deemed clot free. Ninety (90) days post insult, clinical assessment showed that 57 (67.6%) patients were functionally independent, 27 (30.3%) patients functionally dependent, and 5 (5.6%) patients were deceased. Data analysis reported a significant negative correlation (r= -0.611, p<0.001). ROC curves analysis showed an area under the curve of 0.81 at the cut-off point of 6.5. This showed that a CBS of more than 6 predicted a good mRS clinical outcome in AIS patients; with sensitivity of 98.2%, specificity of 53.1%, positive predictive value (PPV) of 76%, and negative predictive value (NPV) of 21%. Conclusion: CBS is a useful additional variable for the management of AIS cases, and should be incorporated into the routine radiological reporting for acute ischemic stroke (AIS) cases.

scholarly journals EGFR-mutant NSCLC presenting with stroke and massive systemic embolization as the first manifestation: case report

BMC Neurology ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Zheng Wang ◽  
Jiangyong Miao ◽  
Lina Wang ◽  
Ying Liu ◽  
Hui Ji ◽  
...  
Keyword(s):  
Risk Factors ◽  
Ischemic Stroke ◽  
Cancer Therapy ◽  
Acute Ischemic Stroke ◽  
Oral Anticoagulants ◽  
Initial Manifestation ◽  
Arterial Thromboembolism ◽  
Systemic Embolization ◽  
Effective Cancer Therapy

Abstract Background Presentation with massive systemic embolization as the initial manifestation of occult malignancy is infrequent. The standard management of cancer-related arterial thromboembolism has not yet been established. Case presentation We described a case of Trousseau’s syndrome resulting in acute ischemic stroke concomitant with multiple embolizations in the spleen and kidney during oral administration of dabigatran for pulmonary embolism preceding the diagnosis of a malignant tumor. A cancer-related hypercoagulable state was suspected because the patient was admitted to the neurology department due to acute ischemic stroke with three territory infarcts on diffusion-weighted imaging (DWI) in the absence of identifiable conventional risk factors and brain vessel narrowing. The patient was subsequently diagnosed with epidermal growth factor receptor (EGFR) mutation–positive non-small-cell lung cancer (NSCLC) (stage IV) with pleural metastasis. Administration of low-molecular-weight heparin followed by long-term dabigatran under effective cancer therapy comprising gefitinib and subsequent chemotherapy did not cause stroke relapse during the 1-year follow-up. Conclusions This case suggests that cancer-related hypercoagulability should be considered an important etiology for stroke patients who develop unexplained disseminated acute cerebral infarction without conventional stroke risk factors, especially concomitant with multiple organ embolization. Novel oral anticoagulants may be an alternative therapy for the long-term management of cancer-related arterial thromboembolism under effective cancer therapy.

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