@article{PennigHoyerKrauskopfetal.2021,
  author    = {Pennig, Lenhard and Hoyer, Ulrike Cornelia Isabel and Krauskopf, Alexandra and Shahzad, Rahil and J{\"u}nger, Stephanie T. and Thiele, Frank and Laukamp, Kai Roman and Grunz, Jan-Peter and Perkuhn, Michael and Schlamann, Marc and Kabbasch, Christoph and Borggrefe, Jan and Goertz, Lukas},
  title     = {Deep learning assistance increases the detection sensitivity of radiologists for secondary intracranial aneurysms in subarachnoid hemorrhage},
  series = {Neuroradiology},
  volume    = {63},
  journal   = {Neuroradiology},
  number    = {12},
  issn      = {0028-3940},
  doi       = {10.1007/s00234-021-02697-9},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-308117},
  pages     = {1985-1994},
  year      = {2021},
  abstract  = {Purpose To evaluate whether a deep learning model (DLM) could increase the detection sensitivity of radiologists for intracranial aneurysms on CT angiography (CTA) in aneurysmal subarachnoid hemorrhage (aSAH). Methods Three different DLMs were trained on CTA datasets of 68 aSAH patients with 79 aneurysms with their outputs being combined applying ensemble learning (DLM-Ens). The DLM-Ens was evaluated on an independent test set of 104 aSAH patients with 126 aneuryms (mean volume 129.2 ± 185.4 mm3, 13.0\% at the posterior circulation), which were determined by two radiologists and one neurosurgeon in consensus using CTA and digital subtraction angiography scans. CTA scans of the test set were then presented to three blinded radiologists (reader 1: 13, reader 2: 4, and reader 3: 3 years of experience in diagnostic neuroradiology), who assessed them individually for aneurysms. Detection sensitivities for aneurysms of the readers with and without the assistance of the DLM were compared. Results In the test set, the detection sensitivity of the DLM-Ens (85.7\%) was comparable to the radiologists (reader 1: 91.2\%, reader 2: 86.5\%, and reader 3: 86.5\%; Fleiss κ of 0.502). DLM-assistance significantly increased the detection sensitivity (reader 1: 97.6\%, reader 2: 97.6\%,and reader 3: 96.0\%; overall P=.024; Fleiss κ of 0.878), especially for secondary aneurysms (88.2\% of the additional aneurysms provided by the DLM). Conclusion Deep learning significantly improved the detection sensitivity of radiologists for aneurysms in aSAH, especially for secondary aneurysms. It therefore represents a valuable adjunct for physicians to establish an accurate diagnosis in order to optimize patient treatment.},
  language  = {en}
}
@article{FabritiusWoelferHerzbergetal.2021,
  author    = {Fabritius, Matthias Philipp and W{\"o}lfer, Teresa A. and Herzberg, Moriz and Tiedt, Steffen and Puhr-Westerheide, Daniel and Grosu, Sergio and Maurus, Stefan and Geyer, Thomas and Curta, Adrian and Kellert, Lars and K{\"u}pper, Clemens and Liebig, Thomas and Ricke, Jens and Dimitriadis, Konstantinos and Kunz, Wolfgang G. and Zimmermann, Hanna and Reidler, Paul},
  title     = {Course of early neurologic symptom severity after endovascular treatment of anterior circulation large vessel occlusion stroke: association with baseline multiparametric CT imaging and clinical parameters},
  series = {Diagnostics},
  volume    = {11},
  journal   = {Diagnostics},
  number    = {7},
  issn      = {2075-4418},
  doi       = {10.3390/diagnostics11071272},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-242681},
  year      = {2021},
  abstract  = {Background: Neurologic symptom severity and deterioration at 24 hours (h) predict long-term outcomes in patients with acute large vessel occlusion (LVO) stroke of the anterior circulation. We aimed to examine the association of baseline multiparametric CT imaging and clinical factors with the course of neurologic symptom severity in the first 24 h after endovascular treatment (EVT). Methods: Patients with LVO stroke of the anterior circulation were selected from a prospectively acquired consecutive cohort of patients who underwent multiparametric CT, including non-contrast CT, CT angiography and CT perfusion before EVT. The symptom severity was assessed on admission and after 24 h using the 42-point National Institutes of Health Stroke Scale (NIHSS). Clinical and imaging data were compared between patients with and without early neurological deterioration (END). END was defined as an increase in ≥4 points, and a significant clinical improvement as a decrease in ≥4 points, compared to NIHSS on admission. Multivariate regression analyses were used to determine independent associations of imaging and clinical parameters with NIHSS score increase or decrease in the first 24 h. Results: A total of 211 patients were included, of whom 38 (18.0\%) had an END. END was significantly associated with occlusion of the internal carotid artery (odds ratio (OR), 4.25; 95\% CI, 1.90-9.47) and the carotid T (OR, 6.34; 95\% CI, 2.56-15.71), clot burden score (OR, 0.79; 95\% CI, 0.68-0.92) and total ischemic volume (OR, 1.01; 95\% CI, 1.00-1.01). In a comprehensive multivariate analysis model including periprocedural parameters and complications after EVT, carotid T occlusion remained independently associated with END, next to reperfusion status and intracranial hemorrhage. Favorable reperfusion status and small ischemic core volume were associated with clinical improvement after 24 h. Conclusions: The use of imaging parameters as a surrogate for early NIHSS progression in an acute LVO stroke after EVT reached limited performance with only carotid T occlusion as an independent predictor of END. Reperfusion status and early complications in terms of intracranial hemorrhage are critical factors that influence patient outcome in the acute stroke phase after EVT.},
  language  = {en}
}