Puig, Josep

Abstract Background Precise delineation of non-contrast-enhancing tumor (nCET) in glioblastoma (GB) is critical for maximal safe resection, yet routine imaging cannot reliably separate infiltrative tumor from vasogenic edema. The aim of this study was to develop and validate an automated method to identify peritumoral subregions compatible with nCET and assess its prognostic value. Methods Pre-operative T2-weighted and FLAIR MRI from 940 patients with newly diagnosed GB in four multicenter cohorts were analyzed. A deep-learning model segmented enhancing tumor, edema and necrosis; a non-local, spatially varying finite mixture model was applied to identify edema subregions characterized by relatively lower FLAIR hyperintensity, hypothesized to reflect nCET-related tissue. The ratio of these subregions to total edema volume defined the T2/FLAIR Heterogeneity Index (TFHI). Associations between TFHI and overall survival (OS) were examined with Kaplan–Meier curves and multivariable Cox regression. Results Higher TFHI values stratified patients with shorter OS. In the NCT03439332, TFHI above the optimal threshold was associated with a twofold increased hazard of death (hazard ratio [HR] 2.07, 95 % confidence interval 1.33-3.21; P = .0013) and a reduction in median survival of 98 days. Significant, though smaller, prognostic effects were confirmed in GLIOCAT & BraTS (HR= 1.37; P = .047), OUS (HR = 1.37; P = .0032) and pooled analysis (HR= 1.26; P = .0008). TFHI remained an independent predictor after adjustment for age, extent of resection and MGMT methylation. Conclusions We present a reproducible, server-hosted tool for automated identification of imaging-defined, putative nCET-related peritumoral subregions and TFHI biomarker extraction that enables independent prognostic stratification. This approach provides a quantitative framework for studying peritumoral heterogeneity in GB.

ABSTRACTBackground and PurposeLarge‐bore aspiration catheters (LBACs) are used for thrombectomy in large vessel occlusion (LVO), either as a standalone direct aspiration first‐pass technique or combined with a stent retriever (ASR). LBAC design may influence ASR thrombectomy efficacy. We compared the safety and performance of the novel MIVI Q segmental catheter with the well‐established SOFIA aspiration device in ASR thrombectomy.MethodsWe analyzed data from the Registry cOmbined vS SinglE Thrombectomy TechnIques registry of consecutive patients with anterior circulation LVO and compared the outcomes of those treated with first‐line ASR thrombectomy using Q (Q5 or Q6) or SOFIA (5F or 6F Plus) catheters. Demographic, clinical, angiographic, and clinical outcome data (24‐h National Institute of Health Stroke Scale [NIHSS] and modified Rankin Scale score at 3 months) were compared.ResultsOf the 853 patients, 155 (18.2%) were treated with MIVI Q and 698 (81.8%) with SOFIA catheters. After adjusting for age, sex, NIHSS score at baseline, tPA use, site occlusion, anesthesia type, and diameter and length of SR, the MIVI Q group was comparable to the SOFIA group in terms of first‐pass effect or successful final recanalization and safety. However, the MIVI Q group had a shorter mechanical thrombectomy time (20 [10–45] min vs. 33 [20–51] min; odds ratio [OR] = 7.4, 95% confidence interval [CI]: 1.1–14; p = 0.021) and a lower rate of symptomatic intracerebral hemorrhage (3.3% vs. 8.8%; OR = 3.59, 95% CI: 1.45–10.9; p = 0.011).ConclusionsIn ASR neurothrombectomy, SOFIA aspiration catheters were not superior to MIVI Q in achieving successful and complete first‐passage recanalization; however, MIVI Q had shorter procedural times and a lower rate of symptomatic intracranial hemorrhage.

BACKGROUND: This study aimed to develop and validate a computed tomography angiography based machine learning model that uses plaque composition data and degree of carotid stenosis to detect symptomatic carotid plaques in patients with carotid atherosclerosis. METHODS: The machine learning based model was trained using degree of stenosis and the volumes of 13 computed tomography angiography derived intracarotid plaque subcomponents (eg, lipid, intraplaque hemorrhage, calcium) to identify plaques associated with cerebrovascular events. The model was internally validated through repeated 10-fold cross-validation and tested on a dedicated testing cohort according to discrimination and calibration. RESULTS: This retrospective, single-center study evaluated computed tomography angiography scans of 268 patients with both symptomatic and asymptomatic carotid atherosclerosis (163 for the derivation set and 106 for the testing set) performed between March 2013 and October 2019. The area-under-receiver-operating characteristics curve by machine learning on the testing cohort (0.89) was significantly higher than the areas under the curve of traditional logit analysis based on the degree of stenosis (0.51, P <0.001), presence of intraplaque hemorrhage (0.69, P <0.001), and plaque composition (0.78, P <0.001), respectively. Comparable performance was obtained on internal validation. The identified plaque components and associated cutoff values that were significantly associated with a higher likelihood of symptomatic status after adjustment were the ratio of intraplaque hemorrhage to lipid volume (≥50%, 38.5 [10.1–205.1]; odds ratio, 95% CI) and percentage of intraplaque hemorrhage volume (≥10%, 18.5 [5.7–69.4]; odds ratio, 95% CI). CONCLUSIONS: This study presented an interpretable machine learning model that accurately identifies symptomatic carotid plaques using computed tomography angiography derived plaque composition features, aiding clinical decision-making.