Prediction of Progression to Severe Stroke in Initially Diagnosed Anterior Circulation Ischemic Cerebral Infarction

Predicting Functional Outcomes of Endovascular Thrombectomy in Acute Ischemic Stroke Using a Clinical-Radiomics Nomogram

BACKGROUND

Endovascular thrombectomy (EVT) is recommended for acute ischemic stroke due to large-vessel occlusion. However, approximately 50% of patients still experience poor outcomes after the procedure. This study aimed to assess whether a nomogram model that integrates computed tomography angiography radiomics features and clinical variables can predict EVT outcomes in patients with acute ischemic stroke.

METHODS

A total of 159 patients undergoing EVT were randomly divided into training and validation groups at a 7:3 ratio. A modified Rankin Scale score ≤ 2 at 90 days indicated a favorable outcome. We used univariate and multivariate logistic regression to identify analytic and radiomics predictors and create predictive models. Model performance was evaluated using the area under the curve, Hosmer-Lemeshow test, and decision curve analysis for discrimination, calibration, and clinical utility.

RESULTS

A 19-feature radiomics signature reached an area under the curve of 0.79. Combining it with age, baseline National Institutes of Health Stroke Scale score, diabetes, and statin use increased the area under the curve of the clinical-radiomics nomogram to 0.85. Both decision curve and calibration curve analyses showed strong performance.

CONCLUSIONS

Combining a radiomics nomogram with clinical predictors could effectively forecast EVT outcomes in patients with acute anterior circulation large vessel occlusion stroke.

Outcome prediction comparison of ischaemic areas' radiomics in acute anterior circulation non-lacunar infarction

The outcome prediction of acute anterior circulation non-lacunar infarction (AACNLI) is important for the precise clinical treatment of this disease. However, the accuracy of prognosis prediction is still limited. This study aims to develop and compare machine learning models based on MRI radiomics of multiple ischaemic-related areas for prognostic prediction in AACNLI. This retrospective multicentre study consecutively included 372 AACNLI patients receiving MRI examinations and conventional therapy between October 2020 and February 2023. These were grouped into training set, internal test set and external test set. MRI radiomics features were extracted from the mask diffusion-weighted imaging, mask apparent diffusion coefficient (ADC) and mask ADC620 by AACNLI segmentations. Grid search parameter tuning was performed on 12 feature selection and 9 machine learning algorithms, and algorithm combinations with the smallest rank-sum of area under the curve (AUC) was selected for model construction. The performances of all models were evaluated in the internal and external test sets. The AUC of radiomics model was larger than that of non-radiomics model with the same machine learning algorithm in the three mask types. The radiomics model using least absolute shrinkage and selection operator-random forest algorithm combination gained the smallest AUC rank-sum among all the algorithm combinations. The AUC of the model with ADC620 was 0.98 in the internal test set and 0.91 in the external test set, and the weighted average AUC in the three sets was 0.96, the largest among three mask types. The Shapley additive explanations values of the maximum of National Institute of Health Stroke Scale score within 7 days from onset (7-d NIHSSmax), stroke-associated pneumonia and admission Glasgow coma scale score ranked top three among the features in AACNLI outcome prediction. In conclusion, the random forest model with mask ADC620 can accurately predict the AACNLI outcome and reveal the risk factors leading to the poor prognosis.

Predicting long-term outcomes for acute ischemic stroke using multi-model MRI radiomics and clinical variables

Purpose

The objective of this study was to create and validate a novel prediction model that incorporated both multi-modal radiomics features and multi-clinical features, with the aim of accurately identifying acute ischemic stroke (AIS) patients who faced a higher risk of poor outcomes.

Methods

A cohort of 461 patients diagnosed with AIS from four centers was divided into a training cohort and a validation cohort. Radiomics features were extracted and selected from diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) images to create a radiomic signature. Prediction models were developed using multi-clinical and selected radiomics features from DWI and ADC.

Results

A total of 49 radiomics features were selected from DWI and ADC images by the least absolute shrinkage and selection operator (LASSO). Additionally, 20 variables were collected as multi-clinical features. In terms of predicting poor outcomes in validation set, the area under the curve (AUC) was 0.727 for the DWI radiomics model, 0.821 for the ADC radiomics model, 0.825 for the DWI + ADC radiomics model, and 0.808 for the multi-clinical model. Furthermore, a prediction model was built using all selected features, the AUC for predicting poor outcomes increased to 0.86.

Conclusion

Radiomics features extracted from DWI and ADC images can serve as valuable biomarkers for predicting poor clinical outcomes in patients with AIS. Furthermore, when these radiomics features were combined with multi-clinical features, the predictive performance was enhanced. The prediction model has the potential to provide guidance for tailoring rehabilitation therapies based on individual patient risks for poor outcomes.

Application of machine learning in Chinese medicine differentiation of dampness-heat pattern in patients with type 2 diabetes mellitus

Background

China has become the country with the largest number of people with type 2 diabetes mellitus (T2DM), and Chinese medicine (CM) has unique advantages in preventing and treating T2DM, while accurate pattern differentiation is the guarantee for proper treatment.

Objective

The establishment of the CM pattern differentiation model of T2DM is helpful to the pattern diagnosis of the disease. At present, there are few studies on dampness-heat pattern differentiation models of T2DM. Therefore, we establish a machine learning model, hoping to provide an efficient tool for the pattern diagnosis of CM for T2DM in the future.

Methods

A total of 1021 effective samples of T2DM patients from ten CM hospitals or clinics were collected by a questionnaire including patients' demographic and dampness-heat-related symptoms and signs. All information and the diagnosis of the dampness-heat pattern of patients were completed by experienced CM physicians at each visit. We applied six machine learning algorithms (Artificial Neural Network [ANN], K-Nearest Neighbor [KNN], Naïve Bayes [NB], Support Vector Machine [SVM], Extreme Gradient Boosting [XGBoost] and Random Forest [RF]) and compared their performance. And then we also utilized Shapley additive explanation (SHAP) method to explain the best performance model.

Results

The XGBoost model had the highest AUC (0.951, 95% CI 0.925-0.978) among the six models, with the best sensitivity, accuracy, F1 score, negative predictive value, and excellent specificity, precision, and positive predictive value. The SHAP method based on XGBoost showed that slimy yellow tongue fur was the most important sign in dampness-heat pattern diagnosis. The slippery pulse or rapid-slippery pulse, sticky stool with ungratifying defecation also performed an important role in this diagnostic model. Furthermore, the red tongue acted as an important tongue sign for the dampness-heat pattern.

Conclusion

This study constructed a dampness-heat pattern differentiation model of T2DM based on machine learning. The XGBoost model is a tool with the potential to help CM practitioners make quick diagnosis decisions and contribute to the standardization and international application of CM patterns.