A Deep Learning–based Model for Detecting Abnormalities on                     Brain MR Images for Triaging: Preliminary Results from a Multisite                     Experience

Gauriau, Romane; Bizzo, Bernardo C.; Kitamura, Felipe; Landi, Osvaldo; Ferraciolli, Suely Fazio; Macruz, Fabíola Bezerra de Carvalho; Sanchez, Tiago Arruda; García, Mora; Vedolin, Leonardo; Domingues, Romeu Côrtes; Gasparetto, Emerson L.; Andriole, Katherine P.

doi:10.1148/ryai.2021200184

Cited by 11 publications

(11 citation statements)

References 16 publications

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“…However, large performance gaps were seen across clinical settings and study designs, partially owing to the well-documented effect of domain shift [ 53 ]. For example, Gauriau et al [ 33 ] tested an algorithm with moderately low sensitivity and specificity of 77% and 65%, respectively. These results were, however, attained on a large out-of-distribution dataset with a comprehensive representation of almost all diseases seen in everyday clinical practice.…”

Section: Discussionmentioning

confidence: 99%

Automated Identification of Multiple Findings on Brain MRI for Improving Scan Acquisition and Interpretation Workflows: A Systematic Review

et al. 2022

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We conducted a systematic review of the current status of machine learning (ML) algorithms’ ability to identify multiple brain diseases, and we evaluated their applicability for improving existing scan acquisition and interpretation workflows. PubMed Medline, Ovid Embase, Scopus, Web of Science, and IEEE Xplore literature databases were searched for relevant studies published between January 2017 and February 2022. The quality of the included studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 tool. The applicability of ML algorithms for successful workflow improvement was qualitatively assessed based on the satisfaction of three clinical requirements. A total of 19 studies were included for qualitative synthesis. The included studies performed classification tasks (n = 12) and segmentation tasks (n = 7). For classification algorithms, the area under the receiver operating characteristic curve (AUC) ranged from 0.765 to 0.997, while accuracy, sensitivity, and specificity ranged from 80% to 100%, 72% to 100%, and 65% to 100%, respectively. For segmentation algorithms, the Dice coefficient ranged from 0.300 to 0.912. No studies satisfied all clinical requirements for successful workflow improvements due to key limitations pertaining to the study’s design, study data, reference standards, and performance reporting. Standardized reporting guidelines tailored for ML in radiology, prospective study designs, and multi-site testing could help alleviate this.

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Section: Discussionmentioning

confidence: 99%

Automated Identification of Multiple Findings on Brain MRI for Improving Scan Acquisition and Interpretation Workflows: A Systematic Review

et al. 2022

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“…Another DL approach to perform data classification was proposed by Gauriau et al ( 2021 ). The goal of their study was to distinguish MR images for the presence of brain pathology ( i.e ., either “likely normal” or “likely abnormal”) using FLAIR images.…”

Section: Data Harmonizationmentioning

confidence: 99%

“…The ability to appropriately manage, understand and aggregate large, multi-site and heterogeneous datasets is a key requirement when developing automated, computer-assisted tools to study brain structures and monitor brain abnormalities (Leite et al, 2016 ; Pinheiro et al, 2019 ; Gauriau et al, 2021 ). This requirement is particularly true when developing deep learning (DL)-based methods (Lundervold and Lundervold, 2019 ; Pan et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Deep Learning in Large and Multi-Site Structural Brain MR Imaging Datasets

Bento

Fantini

Park

et al. 2022

Front. Neuroinform.

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Large, multi-site, heterogeneous brain imaging datasets are increasingly required for the training, validation, and testing of advanced deep learning (DL)-based automated tools, including structural magnetic resonance (MR) image-based diagnostic and treatment monitoring approaches. When assembling a number of smaller datasets to form a larger dataset, understanding the underlying variability between different acquisition and processing protocols across the aggregated dataset (termed “batch effects”) is critical. The presence of variation in the training dataset is important as it more closely reflects the true underlying data distribution and, thus, may enhance the overall generalizability of the tool. However, the impact of batch effects must be carefully evaluated in order to avoid undesirable effects that, for example, may reduce performance measures. Batch effects can result from many sources, including differences in acquisition equipment, imaging technique and parameters, as well as applied processing methodologies. Their impact, both beneficial and adversarial, must be considered when developing tools to ensure that their outputs are related to the proposed clinical or research question (i.e., actual disease-related or pathological changes) and are not simply due to the peculiarities of underlying batch effects in the aggregated dataset. We reviewed applications of DL in structural brain MR imaging that aggregated images from neuroimaging datasets, typically acquired at multiple sites. We examined datasets containing both healthy control participants and patients that were acquired using varying acquisition protocols. First, we discussed issues around Data Access and enumerated the key characteristics of some commonly used publicly available brain datasets. Then we reviewed methods for correcting batch effects by exploring the two main classes of approaches: Data Harmonization that uses data standardization, quality control protocols or other similar algorithms and procedures to explicitly understand and minimize unwanted batch effects; and Domain Adaptation that develops DL tools that implicitly handle the batch effects by using approaches to achieve reliable and robust results. In this narrative review, we highlighted the advantages and disadvantages of both classes of DL approaches, and described key challenges to be addressed in future studies.

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“…Artificial intelligence approaches for routine MRI images have been proven to be efficient ways to achieve semantic segmentation of lesions and extraction of multidimensional information (18)(19)(20). State-of-the-art deep learning architectures such as convolutional neural network (CNN) have powerful performance in brain tumor classification, objection, and segmentation (21)(22)(23). And another advantage is to implement transfer learning that uses large pretrained model weights and fine-tunes the classification layers to obtain higher accuracy with few data.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Model for Intracranial Hemangiopericytoma and Meningioma Classification

Chen

Jiang

et al. 2022

Front. Oncol.

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BackgroundIntracranial hemangiopericytoma/solitary fibrous tumor (SFT/HPC) is a rare type of neoplasm containing malignancies of infiltration, peritumoral edema, bleeding, or bone destruction. However, SFT/HPC has similar radiological characteristics as meningioma, which had different clinical managements and outcomes. This study aims to discriminate SFT/HPC and meningioma via deep learning approaches based on routine preoperative MRI.MethodsWe enrolled 236 patients with histopathological diagnosis of SFT/HPC (n = 144) and meningioma (n = 122) from 2010 to 2020 in Xiangya Hospital. Radiological features were extracted manually, and a radiological diagnostic model was applied for classification. And a deep learning pretrained model ResNet-50 was adapted to train T1-contrast images for predicting tumor class. Deep learning model attention mechanism was visualized by class activation maps.ResultsOur study reports that SFT/HPC was found to have more invasion to venous sinus (p = 0.001), more cystic components (p < 0.001), and more heterogeneous enhancement patterns (p < 0.001). Deep learning model achieved a high classification accuracy of 0.889 with receiver-operating characteristic curve area under the curve (AUC) of 0.91 in the validation set. Feature maps showed distinct clustering of SFT/HPC and meningioma in the training and test cohorts, respectively. And the attention of the deep learning model mainly focused on the tumor bulks that represented the solid texture features of both tumors for discrimination.

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A Deep Learning–based Model for Detecting Abnormalities on Brain MR Images for Triaging: Preliminary Results from a Multisite Experience

Cited by 11 publications

References 16 publications

Automated Identification of Multiple Findings on Brain MRI for Improving Scan Acquisition and Interpretation Workflows: A Systematic Review

Automated Identification of Multiple Findings on Brain MRI for Improving Scan Acquisition and Interpretation Workflows: A Systematic Review

Deep Learning in Large and Multi-Site Structural Brain MR Imaging Datasets

Deep Learning Model for Intracranial Hemangiopericytoma and Meningioma Classification

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