Introduction: We aimed to verify the application value of the Alberta Stroke Program Early CT Score (ASPECTS) based on multiple post-labeling delay (multi-PLD) arterial spin labeling (ASL) for outcome assessment in acute ischemic stroke (AIS) patients.Method: The endpoint was modified Rankin scale score at 90 days (90-day mRS). Patients were divided into the good outcome (0–2) and poor outcome (3–6) groups. The independent samples t-test, Mann-Whitney U-test, and χ²-test were used to compare clinical and imaging parameters between groups. We used partial correlation analysis to evaluate the relationships between ASPECTS and outcomes. Multivariate logistic regression analysis was used to examine potential independent prognostic indicators. The receiver operating characteristic (ROC) curve analysis was used to evaluate the performance of the independent prognostic indicators in predicting outcomes.Results: Fifty-five AIS patients were included. The good outcome group had a lower baseline National Institutes of Health Stroke Scale (NIHSS; Z = −3.413, P < 0.001) and infarct core volume (ICV; Z = −3.114, P = 0.002) as well as higher cerebral blood flow (CBF)-ASPECTS (Z = −3.835, P < 0.001) and cerebral blood volume (CBV)-ASPECTS (Z = −4.099, P < 0.001). Higher CBF-ASPECTS (r = −0.459, P = 0.001), and CBV-ASPECTS (r = −0.502, P < 0.001) were associated with a lower 90-day mRS. The baseline NIHSS, CBF-ASPECTS, and CBV-ASPECTS were identified as independent prognostic indicators. The AUCs of the baseline NIHSS, CBF-ASPECTS, and CBV-ASPECTS were 83.3, 87.4, and 89.9%, respectively. Combining NIHSS with CBF-ASPECTS and CBV-ASPECTS, the AUC significantly improved to 96.3%. The combined three factors showed a significant difference compared to the baseline NIHSS (Z = 2.039, P = 0.041) and CBF-ASPECTS (Z = 2.099, P = 0.036), but no difference with CBV-ASPECTS (Z = 1.176, P = 0.239).Conclusions: The ASPECTS based on multi-PLD ASL is a valuable tool for identifying independent prognostic indicators and assessing clinical outcomes in AIS patients. The baseline NIHSS, combined with CBF-ASPECTS and CBV-ASPECTS, enhances the predictive efficacy of clinical outcomes in AIS patients. The CBV-ASPECTS alone can offer comparable predictive efficacy to the combination.

Accurate early prognostic prediction for acute ischemic stroke (AIS) is essential for guiding personalized treatment. This study aimed to assess the predictive value of radiomics features from whole-brain and infarct cerebral blood flow (CBF) images using multiple post-labeling delay arterial spin labeling (multi-PLD ASL), and to develop a prediction model incorporating clinical risk factors. Radiomics features were extracted from the whole-brain and infarct regions based on multi-PLD ASL CBF images of 110 AIS patients. Five machine learning algorithms were used to construct radiomics models (whole-brain, infarct, and combined), clinical models, and comprehensive models integrating radiomics and clinical data. Model performance and clinical utility were assessed using receiver operating characteristic and decision curve analyses. Model stability was evaluated via 5000 permutation tests, and differences in the area under the curve (AUC) were compared using the DeLong test. Shapley Additive exPlanation was used to interpret feature contributions. The whole-brain and infarct radiomics models showed similar predictive performance. The combined radiomics models generally outperformed the infarct-only models. Additionally, no significant differences were observed between the combined radiomics models and the clinical models across the five algorithms. The comprehensive models, which integrated both radiomics and clinical features, demonstrated superior performance compared to both the clinical and combined radiomics models. Among all models, the comprehensive model based on a support vector machine achieved the highest predictive performance (AUC = 0.904). Its predictive capability was primarily driven by the baseline National Institutes of Health Stroke Scale score, age, infarct shape features, and higher-order statistical and texture features derived from both infarct and whole-brain CBF images. The whole-brain CBF radiomics features of multi-PLD ASL can replace the infarct radiomics features that need to be delineated. Combined radiomics models outperformed infarct-only models and performed similarly to clinical models, which is applicable to AIS patients with incomplete clinical data. The comprehensive model integrates multi-PLD ASL CBF radiomics and clinical data, providing a safe and accurate tool for early prognosis prediction in AIS patients.

Background: Neuroimaging studies have suggested a pivotal role for the amygdala involvement in chronic low back pain (CLBP). However, the relationship between the amygdala subregions and CLBP has not yet been delineated. This study aimed to analyze whether the amygdala subregions were linked to the development of CLBP.Methods: A total of 45 patients with CLBP and 45 healthy controls (HCs) were included in this study. All subjects were asked to complete a three-dimensional T1-weighted magnetic resonance imaging (3D-T1 MRI) scan. FreeSurfer 7.3.2 was applied to preprocess the structural MRI images and segment the amygdala into nine subregions. Afterwards, comparisons were made between the two groups in terms of the volumes of the amygdala subregions. Correlation analysis is utilized to examine the relationship between the amygdala subregion and the scale scores, as well as the pain duration in patients with CLBP. Additionally, logistic regression was used to explore the risk of the amygdala and its subregions for CLBP.Results: In comparison to HCs, patients with CLBP exhibited a significant enlargement of the left central nucleus (Ce) and left cortical nucleus (Co). Furthermore, the increased volume of the left Ce was associated with a higher risk of CLBP.Conclusion: Our study suggests that the left Ce and left Co may be involved in the pathophysiological processes of CLBP. Moreover, the volume of the left Ce may be a biomarker for detecting the risk of CLBP.

Introduction: Early prognosis prediction of acute ischemic stroke (AIS) can support clinicians in choosing personalized treatment plans. The aim of this study is to develop a machine learning (ML) model that uses multiple post-labeling delay times (multi-PLD) arterial spin labeling (ASL) radiomics features to achieve early and precise prediction of AIS prognosis.Methods: This study enrolled 102 AIS patients admitted between December 2020 and September 2024. Clinical data, such as age and baseline National Institutes of Health Stroke Scale (NIHSS) score, were collected. Radiomics features were extracted from cerebral blood flow (CBF) images acquired through multi-PLD ASL. Features were selected using least absolute shrinkage and selection operator regression, and three models were developed: a clinical model, a CBF radiomics model, and a combined model, employing eight ML algorithms. Model performance was assessed using receiver operating characteristic curves and decision curve analysis (DCA). Shapley Additive exPlanations was applied to interpret feature contributions.Results: The combined model of extreme gradient boosting demonstrated superior predictive performance, achieving an area under the curve (AUC) of 0.876. Statistical analysis using the DeLong test revealed its significant outperformance compared to both the clinical model (AUC = 0.658, p < 0.001) and the CBF radiomics model (AUC = 0.755, p = 0.002). The robustness of all models was confirmed through permutation testing. Furthermore, DCA underscored the clinical utility of the combined model. The prognostic prediction of AIS was notably influenced by the baseline NIHSS score, age, as well as texture and shape features of CBF.Conclusion: The integration of clinical data and multi-PLD ASL radiomics features in a model offers a secure and dependable approach for predicting the prognosis of AIS, particularly beneficial for patients with contraindications to contrast agents. This model aids clinicians in devising individualized treatment plans, ultimately enhancing patient prognosis.