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PubMed2026

Occlusion site and outcomes in intra-arterial tenecteplase after successful endovascular therapy: a secondary analysis of the ANGEL-TNK trial.

BACKGROUND: Endovascular thrombectomy achieves macroreperfusion in large vessel occlusion (LVO) stroke, but only one-quarter of patients had excellent functional outcome. Adjunct intra-arterial (IA) tenecteplase could further improve the treatment effect, yet its efficacy across internal carotid artery (ICA) versus middle cerebral artery (MCA) M1/M2 occlusion remains unclear. OBJECTIVE: To evaluate whether IA tenecteplase improves 90-day functional outcomes in LVO patients stratified by occlusion site (ICA, MCA M1, MCA M2). METHODS: Prespecified secondary analysis of the ANGEL-TNK trial (multicenter, randomized, open-label, blinded endpoint) in 19 Chinese stroke centers. Participants were enrolled who had anterior circulation LVO, 4.5-24 hours from symptom onset, and CT angiography (CTA)/magnetic resonance angiography (MRA)-proven ICA, M1, or M2 occlusion. INTERVENTION: Randomization (1:1) to IA tenecteplase (0.125 mg/kg) or standard medical management after expanded Thrombolysis in Cerebral Infarction (eTICI) 2b50-3 reperfusion. MAIN OUTCOME MEASURE: The primary outcome was the rate of 90-day modified Rankin Scale (mRS) 0-1. RESULTS: There were 256 patients in the trial, including 71 (27.7%) ICA, 122 (47.6%) MCA M1, and 62 (24.2%) MCA M2. ICA occlusion patients treated with IA tenecteplase had higher rates of 90-day mRS 0-1 (39.4% vs 13.2%; relative risk (RR) 2.99; 95% CI 1.51 to 5.96; p=0.002) and mRS 0-3 (54.5% vs 42.1%; RR 1.30; p=0.048) versus controls, with a significant shift toward better mRS scores (median 3 (IQR 1-4) vs 4 (2-6); odds ratio (OR) 2.26; p<0.001). Post hoc analysis showed higher eTICI progression in ICA patients (51.5%) versus MCA M1 (37.9%) and M2 (25.7%). IA tenecteplase had a lower any intracranial hemorrhage within 48 hours in ICA patients (15.2% vs 39.5%; RR 0.38; p<0.001) compared with standard medical management. No significant benefits were observed in the MCA M1/M2 subgroups, and interaction effects were significant between ICA and MCA segments for functional outcomes and safety. CONCLUSIONS AND RELEVANCE: IA tenecteplase had a better functional outcome and lower hemorrhage risk in ICA occlusion compared with standard medical management but not in MCA M1/M2. Occlusion site is a critical determinant of response to IA tenecteplase. A pooled analysis of IA tenecteplase stratified by occlusion site strata is warranted.

Humans↗

PubMed2026

Spinal meningiomas: histopathological grading using a benchmark radiomics model with notes on disease control.

OBJECTIVE: Spinal meningiomas (SMs) are common primary spinal tumors for which surgery is considered the first-line treatment when safe and feasible. The ability to extrapolate the tumor grade from preoperative imaging may significantly inform early patient expectation-setting regarding recurrence. Building on radiomics studies in cranial meningiomas, the authors aimed to construct a benchmark radiomics model to preoperatively identify the histological grade of SMs. METHODS: Institutional surgical records from May 2012 to November 2025 were queried for pathology-confirmed meningiomas below the foramen magnum, with preoperative contrast-enhanced imaging available for segmentation. SMs were classified as low-grade (WHO grade 1) and high-grade (WHO grade 2 tumors and grade 1 tumors with atypia). Tumors were manually segmented, and features were extracted using the PyRadiomics software package. An ensemble model of k-nearest neighbors, random forest, and support vector machine classifiers was trained using nested cross-validation on a subset of 10 features to differentiate tumor grades. Clinical data for the cohort were also extracted, and disease control in an adjunctive clinical series was assessed. RESULTS: Seventy-four patients were included in radiomics analysis, with an area under the receiver operating characteristic curve of 0.879 and a mean F1 score of 0.748. The model's top 5 features were all texture features that differed significantly (p < 0.05) across low- and high-grade SMs. These included measures of tumor textural and contrast-enhancement heterogeneity, with overlap with features reported in radiomics models for histological grading of intracranial meningiomas. Fifty-five patients with a median radiographic follow-up of 22.2 (range 1.9-86.4) months remained for clinical analysis after exclusion of patients with less than 1 month of follow-up and syndromic meningiomas. Four recurrences occurred at a median of 20.8 (range 1.8-41.8) months. High-grade tumor pathology did not significantly impact progression-free survival (p = 0.682, log-rank test; Cox regression high vs low grade hazard ratio [HR] 0.62, 95% CI 0.06-6.11, p = 0.685). Subtotal resection was associated with poorer progression-free survival than gross-total resection (p = 0.004, log-rank test; Cox regression subtotal vs gross-total resection HR 10.62, 95% CI 1.46-77.05, p = 0.019). These findings remain contextualized within a relatively limited follow-up window and small recurrence event count, suggesting a need to characterize the interplay between tumor grade and extent of resection as drivers of local disease control in SMs. CONCLUSIONS: A preoperative radiomics model can stratify high-grade SMs using open-source tools applied to single-institution data.

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PubMed2026

Artificial intelligence (AI) uses in stereotactic radiosurgery (SRS): diagnosis with brain metastasis (BM) - A systematic review.

BACKGROUND: Brain metastases (BM) are the most common intracranial tumors in adults, and stereotactic radiosurgery (SRS) has become a mainstay of management. However, several diagnostic challenges persist in the SRS pathway, particularly the differentiation of radiation necrosis (RN) from true tumor progression, which conventional MRI and even advanced imaging techniques often cannot reliably resolve. Recent advances in artificial intelligence (AI) offer the potential to address these diagnostic limitations. This systematic review synthesizes current literature on AI applications for MRI-based diagnostic decision support in BM patients undergoing SRS, with a focus on radiomics and deep learning tools for distinguishing RN from progression, classifying molecular and histologic subtypes, and predicting treatment response. METHODS: A systematic review was performed in accordance with PRISMA guidelines. PubMed, Web of Science, and Scopus were searched using a targeted query combining terms related to AI, brain metastasis, diagnosis or imaging, and SRS. After screening 483 records and applying strict inclusion and exclusion criteria, 18 studies published between 2015 and 2025 were included. Data were extracted on study design, cohort characteristics, imaging modality, AI methodology, validation strategy, and reported diagnostic performance. RESULTS: Among the 18 included studies, AI models demonstrated strong performance across diagnostic tasks in the BM-SRS pathway. The differentiation of RN from true tumor progression was the most extensively studied application, addressed by 14 of 18 studies, with reported AUCs ranging from 0.71 to 0.94. Support vector machines, random-forest ensembles, convolutional neural networks, and transformer-based multimodal architectures were widely used. The literature evolved from single-sequence radiomic classifiers in 2018 to multimodal deep learning frameworks fusing imaging with clinical and genomic data in 2025. Contrast-enhanced T1-weighted MRI was the dominant imaging input, and texture-based radiomic features (GLCM, GLSZM, GLDM, and wavelet-derived features) were the most consistently predictive. The highest-performing models reached AUCs of 0.85-0.91 through multimodal integration of imaging with clinical and genomic features, and consistently outperformed expert neuroradiologist read on matched cases. Remaining studies addressed longitudinal segmentation-based detection of local failure and adverse radiation effects, BRAF mutation status in melanoma BM, early Gamma Knife treatment response, and primary tumor histology classification, with more variable performance. CONCLUSION: AI models, particularly those integrating MRI-derived radiomic features with clinical and genomic data, show high accuracy in supporting diagnostic decisions for BM patients treated with SRS. The post-SRS differentiation of radiation necrosis from true tumor progression has reached the greatest level of maturity and is closest to clinical translation, with potential to reduce unnecessary biopsies, personalize surveillance intervals, and rationalize treatment-pathway decisions. Other diagnostic applications, including molecular subtyping and primary tumor histology classification, remain exploratory and require further multicenter validation. Integration of AI tools into multidisciplinary tumor-board workflows, combined with prospective validation and standardized reporting, will be essential to realize the full clinical benefits of AI in SRS for brain metastases.

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