2026-04-13T01:43:14Zhttps://recercat.cat/oai/request

oai:recercat.cat:11351/135602025-10-24T10:24:42Zcom_2072_378070com_2072_378040col_2072_378092

00925njm 22002777a 4500 dc Gonzalez Riveros, Jesus David author Canals, Pere author Mayol, Jordi author Garcia-Tornel, Alvaro author Rodrigo-Gisbert, Marc author Ribo, Marc author 2025-08-21T09:03:27Z 2025-08-21T09:03:27Z 2025-09 Acute ischemic stroke; Intracranial atherosclerosis disease; Multimodal deep learning Ictus isquèmic agut; Malaltia ateroscleròtica intracranial; Aprenentatge profund multimodal Ictus isquémico agudo; Enfermedad aterosclerótica intracraneal; Aprendizaje profundo multimodal Purpose: This study explores a multi-modal deep learning approach that integrates pre-intervention neuroimaging and clinical data to predict endovascular therapy (EVT) outcomes in acute ischemic stroke patients. To this end, consecutive stroke patients undergoing EVT were included in the study, including patients with suspected Intracranial Atherosclerosis-related Large Vessel Occlusion ICAD-LVO and other refractory occlusions. Methods: A retrospective, single-center cohort of patients with anterior circulation LVO who underwent EVT between 2017-2023 was analyzed. Refractory LVO (rLVO) defined class, comprised patients who presented any of the following: final angiographic stenosis > 50 %, unsuccessful recanalization (eTICI 0-2a) or required rescue treatments (angioplasty +/- stenting). Neuroimaging data included non-contrast CT and CTA volumes, automated vascular segmentation, and CT perfusion parameters. Clinical data included demographics, comorbidities and stroke severity. Imaging features were encoded using convolutional neural networks and fused with clinical data using a DAFT module. Data were split 80 % for training (with four-fold cross-validation) and 20 % for testing. Explainability methods were used to analyze the contribution of clinical variables and regions of interest in the images. Results: The final sample comprised 599 patients; 481 for training the model (77, 16.0 % rLVO), and 118 for testing (16, 13.6 % rLVO). The best model predicting rLVO using just imaging achieved an AUC of 0.53 ± 0.02 and F1 of 0.19 ± 0.05 while the proposed multimodal model achieved an AUC of 0.70 ± 0.02 and F1 of 0.39 ± 0.02 in testing. Conclusion: Combining vascular segmentation, clinical variables, and imaging data improved prediction performance over single-source models. This approach offers an early alert to procedural complexity, potentially guiding more tailored, timely intervention strategies in the EVT workflow. http://hdl.handle.net/11351/13560 Malalties cerebrovasculars - Cirurgia Aprenentatge profund Malalties cerebrovasculars - Imatgeria PHENOMENA AND PROCESSES::Mathematical Concepts::Algorithms::Artificial Intelligence::Machine Learning::Deep Learning ANALYTICAL, DIAGNOSTIC AND THERAPEUTIC TECHNIQUES, AND EQUIPMENT::Diagnosis::Diagnostic Techniques and Procedures::Diagnostic Imaging::Multimodal Imaging ANALYTICAL, DIAGNOSTIC AND THERAPEUTIC TECHNIQUES, AND EQUIPMENT::Diagnosis::Diagnostic Techniques and Procedures::Diagnostic Imaging::Neuroimaging DISEASES::Nervous System Diseases::Central Nervous System Diseases::Brain Diseases::Cerebrovascular Disorders::Stroke Other subheadings::Other subheadings::Other subheadings::/diagnostic imaging ANALYTICAL, DIAGNOSTIC AND THERAPEUTIC TECHNIQUES, AND EQUIPMENT::Surgical Procedures, Operative::Cardiovascular Surgical Procedures::Vascular Surgical Procedures::Endovascular Procedures FENÓMENOS Y PROCESOS::conceptos matemáticos::algoritmos::inteligencia artificial::aprendizaje automático::aprendizaje profundo TÉCNICAS Y EQUIPOS ANALÍTICOS, DIAGNÓSTICOS Y TERAPÉUTICOS::diagnóstico::técnicas y procedimientos diagnósticos::diagnóstico por imagen::imagen multimodal TÉCNICAS Y EQUIPOS ANALÍTICOS, DIAGNÓSTICOS Y TERAPÉUTICOS::diagnóstico::técnicas y procedimientos diagnósticos::diagnóstico por imagen::neuroimágenes ENFERMEDADES::enfermedades del sistema nervioso::enfermedades del sistema nervioso central::enfermedades cerebrales::trastornos cerebrovasculares::accidente cerebrovascular Otros calificadores::Otros calificadores::Otros calificadores::/diagnóstico por imagen TÉCNICAS Y EQUIPOS ANALÍTICOS, DIAGNÓSTICOS Y TERAPÉUTICOS::intervenciones quirúrgicas::procedimientos quirúrgicos cardiovasculares::procedimientos quirúrgicos vasculares::procedimientos endovasculares Multimodal deep learning for predicting unsuccessful recanalization in refractory large vessel occlusion