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A clinically applicable deep-learning model for detecting intracranial aneurysm in computed tomography angiography images.

ABSTRACT: Intracranial aneurysm is a common life-threatening disease. Computed tomography angiography is recommended as the standard diagnosis tool; yet, interpretation can be time-consuming and challenging. We present a specific deep-learning-based model trained on 1,177 digital subtraction angiography verified bone-removal computed tomography angiography cases. The model has good tolerance to image quality and is tested with different manufacturers. Simulated real-world studies are conducted in consecutive internal and external cohorts, in which it achieves an improved patient-level sensitivity and lesion-level sensitivity compared to that of radiologists and expert neurosurgeons. A specific cohort of suspected acute ischemic stroke is employed and it is found that 99.0% predicted-negative cases can be trusted with high confidence, leading to a potential reduction in human workload. A prospective study is warranted to determine whether the algorithm could improve patients' care in comparison to clinicians' assessment.

SUBMITTER: Shi Z

PROVIDER: S-EPMC7705757 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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A clinically applicable deep-learning model for detecting intracranial aneurysm in computed tomography angiography images.

Shi Zhao Z Miao Chongchang C Schoepf U Joseph UJ Savage Rock H RH Dargis Danielle M DM Pan Chengwei C Chai Xue X Li Xiu Li XL Xia Shuang S Zhang Xin X Gu Yan Y Zhang Yonggang Y Hu Bin B Xu Wenda W Zhou Changsheng C Luo Song S Wang Hao H Mao Li L Liang Kongming K Wen Lili L Zhou Longjiang L Yu Yizhou Y Lu Guang Ming GM Zhang Long Jiang LJ

Nature communications 20201130 1

Intracranial aneurysm is a common life-threatening disease. Computed tomography angiography is recommended as the standard diagnosis tool; yet, interpretation can be time-consuming and challenging. We present a specific deep-learning-based model trained on 1,177 digital subtraction angiography verified bone-removal computed tomography angiography cases. The model has good tolerance to image quality and is tested with different manufacturers. Simulated real-world studies are conducted in consecut ...[more]

PMID: 33257700

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Project description:OBJECTIVE:To compare assessment of collaterals by single-phase computed tomography (CT) angiography (CTA) and CT perfusion-derived 3-phase CTA, multiphase CTA and temporal maximum-intensity projection (tMIP) images to digital subtraction angiography (DSA), and relate collateral assessments to clinical outcome in patients with acute ischemic stroke. METHODS:Consecutive acute ischemic stroke patients who underwent CT perfusion, CTA, and DSA before thrombectomy with occlusion of the internal carotid artery, the M1 or the M2 segments were included. Two observers assessed all CT images and one separate observer assessed DSA (reference standard) with static and dynamic (modified American Society of Interventional and Therapeutic Neuroradiology) collateral grading methods. Interobserver agreement and concordance were quantified with Cohen-weighted ? and concordance correlation coefficient, respectively. Imaging assessments were related to clinical outcome (modified Rankin Scale, ? 2). RESULTS:Interobserver agreement (n = 101) was 0.46 (tMIP), 0.58 (3-phase CTA), 0.67 (multiphase CTA), and 0.69 (single-phase CTA) for static assessments and 0.52 (3-phase CTA) and 0.54 (multiphase CTA) for dynamic assessments. Concordance correlation coefficient (n = 80) was 0.08 (3-phase CTA), 0.09 (single-phase CTA), and 0.23 (multiphase CTA) for static assessments and 0.10 (3-phase CTA) and 0.27 (multiphase CTA) for dynamic assessments. Higher static collateral scores on multiphase CTA (odds ratio [OR], 1.7; 95% confidence interval [CI], 1.1-2.7) and tMIP images (OR, 2.0; 95% CI, 1.1-3.4) were associated with modified Rankin Scale of 2 or less as were higher modified American Society of Interventional and Therapeutic Neuroradiology scores on 3-phase CTA (OR, 1.5; 95% CI, 1.1-2.2) and multiphase CTA (OR, 1.7; 95% CI, 1.1-2.6). CONCLUSIONS:Concordance between assessments on CT and DSA was poor. Collateral status evaluated on 3-phase CTA and multiphase CTA, but not on DSA, was associated with clinical outcome.

Project description:BackgroundMuscle wasting (Sarcopenia) is associated with poor outcomes in cancer patients. Early identification of sarcopenia can facilitate nutritional and exercise intervention. Cross-sectional skeletal muscle (SM) area at the third lumbar vertebra (L3) slice of a computed tomography (CT) image is increasingly used to assess body composition and calculate SM index (SMI), a validated surrogate marker for sarcopenia in cancer. Manual segmentation of SM requires multiple steps, which limits use in routine clinical practice. This project aims to develop an automatic method to segment L3 muscle in CT scans.MethodsAttenuation correction CTs from full body PET-CT scans from patients enrolled in two prospective trials were used. The training set consisted of 66 non-small cell lung cancer (NSCLC) patients who underwent curative intent radiotherapy. An additional 42 NSCLC patients prescribed curative intent chemo-radiotherapy from a second trial were used for testing. Each patient had multiple CT scans taken at different time points prior to and post- treatment (147 CTs in the training and validation set and 116 CTs in the independent testing set). Skeletal muscle at L3 vertebra was manually segmented by two observers, according to the Alberta protocol to serve as ground truth labels. This included 40 images segmented by both observers to measure inter-observer variation. An ensemble of 2.5D fully convolutional neural networks (U-Nets) was used to perform the segmentation. The final layer of U-Net produced the binary classification of the pixels into muscle and non-muscle area. The model performance was calculated using Dice score and absolute percentage error (APE) in skeletal muscle area between manual and automated contours.ResultsWe trained five 2.5D U-Nets using 5-fold cross validation and used them to predict the contours in the testing set. The model achieved a mean Dice score of 0.92 and an APE of 3.1% on the independent testing set. This was similar to inter-observer variation of 0.96 and 2.9% for mean Dice and APE respectively. We further quantified the performance of sarcopenia classification using computer generated skeletal muscle area. To meet a clinical diagnosis of sarcopenia based on Alberta protocol the model achieved a sensitivity of 84% and a specificity of 95%.ConclusionsThis work demonstrates an automated method for accurate and reproducible segmentation of skeletal muscle area at L3. This is an efficient tool for large scale or routine computation of skeletal muscle area in cancer patients which may have applications on low quality CTs acquired as part of PET/CT studies for staging and surveillance of patients with cancer.

Project description:The purpose of this study was to introduce a new deep learning (DL) model for segmentation of the fovea avascular zone (FAZ) in en face optical coherence tomography angiography (OCTA) and compare the results with those of the device's built-in software and manual measurements in healthy subjects and diabetic patients. In this retrospective study, FAZ borders were delineated in the inner retinal slab of 3 × 3 enface OCTA images of 131 eyes of 88 diabetic patients and 32 eyes of 18 healthy subjects. To train a deep convolutional neural network (CNN) model, 126 enface OCTA images (104 eyes with diabetic retinopathy and 22 normal eyes) were used as training/validation dataset. Then, the accuracy of the model was evaluated using a dataset consisting of OCTA images of 10 normal eyes and 27 eyes with diabetic retinopathy. The CNN model was based on Detectron2, an open-source modular object detection library. In addition, automated FAZ measurements were conducted using the device's built-in commercial software, and manual FAZ delineation was performed using ImageJ software. Bland-Altman analysis was used to show 95% limit of agreement (95% LoA) between different methods. The mean dice similarity coefficient of the DL model was 0.94 ± 0.04 in the testing dataset. There was excellent agreement between automated, DL model and manual measurements of FAZ in healthy subjects (95% LoA of - 0.005 to 0.026 mm2 between automated and manual measurement and 0.000 to 0.009 mm2 between DL and manual FAZ area). In diabetic eyes, the agreement between DL and manual measurements was excellent (95% LoA of - 0.063 to 0.095), however, there was a poor agreement between the automated and manual method (95% LoA of - 0.186 to 0.331). The presence of diabetic macular edema and intraretinal cysts at the fovea were associated with erroneous FAZ measurements by the device's built-in software. In conclusion, the DL model showed an excellent accuracy in detection of FAZ border in enfaces OCTA images of both diabetic patients and healthy subjects. The DL and manual measurements outperformed the automated measurements of the built-in software.

Dataset Information

A clinically applicable deep-learning model for detecting intracranial aneurysm in computed tomography angiography images.

Publications

A clinically applicable deep-learning model for detecting intracranial aneurysm in computed tomography angiography images.

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