Deep Learning–Derived High-Level Neuroimaging Features Predict Clinical Outcomes for Large Vessel Occlusion. Issue 5 (May 2020)

• Record Type: Journal Article
• Authors: Nishi, Hidehisa; Oishi, Naoya; Ishii, Akira; Ono, Isao; Ogura, Takenori; Sunohara, Tadashi; Chihara, Hideo; Fukumitsu, Ryu; Okawa, Masakazu; Yamana, Norikazu; Imamura, Hirotoshi; Sadamasa, Nobutake; Hatano, Taketo; Nakahara, Ichiro; Sakai, Nobuyuki; Miyamoto, Susumu
• Is Part Of: Stroke. Volume 51:Issue 5(2020)
• Publication Date: 2020-05
• Subjects: biomarker -- deep learning -- neuroimaging -- prognosis -- thrombectomy; Cerebrovascular disease -- Periodicals; Cerebral circulation -- Periodicals; 616.81
• DOI: 10.1161/STROKEAHA.119.028101
• Languages: English
• ISSNs: 0039-2499

Abstract

Abstract : Background and Purpose—: For patients with large vessel occlusion, neuroimaging biomarkers that evaluate the changes in brain tissue are important for determining the indications for mechanical thrombectomy. In this study, we applied deep learning to derive imaging features from pretreatment diffusion-weighted image data and evaluated the ability of these features in predicting clinical outcomes for patients with large vessel occlusion. Methods—: This multicenter retrospective study included patients with anterior circulation large vessel occlusion treated with mechanical thrombectomy between 2013 and 2018. We designed a 2-output deep learning model based on convolutional neural networks (the convolutional neural network model). This model employed encoder-decoder architecture for the ischemic lesion segmentation, which automatically extracted high-level feature maps in its middle layers, and used its information to predict the clinical outcome. Its performance was internally validated with 5-fold cross-validation, externally validated, and the results compared with those from the standard neuroimaging biomarkers Alberta Stroke Program Early CT Score and ischemic core volume. The prediction target was a good clinical outcome, defined as a modified Rankin Scale score at 90-day follow-up of 0 to 2. Results—: The derivation cohort included 250 patients, and the validation cohort included 74 patients. The convolutional neural network model showed the highest area under the receiver operating characteristic curve: 0.81±0.06 compared with 0.63±0.05 and 0.64±0.05 for the Alberta Stroke Program Early CT Score and ischemic core volume models, respectively. In the external validation, the area under the curve for the convolutional neural network model was significantly superior to those for the other 2 models. Conclusions—: Compared with the standard neuroimaging biomarkers, our deep learning model derived a greater amount of prognostic information from pretreatment neuroimaging data. Although a confirmatory prospective evaluation is needed, the high-level imaging features derived by deep learning may offer an effective prognostic imaging biomarker. Abstract : Supplemental Digital Content is available in the text.