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Deep learning referral suggestion and tumour discrimination using explainable artificial intelligence applied to multiparametric MRI

Authors
 Hyungseob Shin  ;  Ji Eun Park  ;  Yohan Jun  ;  Taejoon Eo  ;  Jeongryong Lee  ;  Ji Eun Kim  ;  Da Hyun Lee  ;  Hye Hyeon Moon  ;  Sang Ik Park  ;  Seonok Kim  ;  Dosik Hwang  ;  Ho Sung Kim 
Citation
 EUROPEAN RADIOLOGY, Vol.33(8) : 5859-5870, 2023-08 
Journal Title
EUROPEAN RADIOLOGY
ISSN
 0938-7994 
Issue Date
2023-08
MeSH
Artificial Intelligence ; Deep Learning* ; Humans ; Magnetic Resonance Imaging / methods ; Multiparametric Magnetic Resonance Imaging* / methods ; Neoplasms* / diagnostic imaging ; Retrospective Studies
Keywords
Brain diseases ; Brain tumours ; Deep learning ; Image interpretation, computer-assisted
Abstract
Objectives An appropriate and fast clinical referral suggestion is important for intra-axial mass-like lesions (IMLLs) in the emergency setting. We aimed to apply an interpretable deep learning (DL) system to multiparametric MRI to obtain clinical referral suggestion for IMLLs, and to validate it in the setting of nontraumatic emergency neuroradiology. Methods A DL system was developed in 747 patients with IMLLs ranging 30 diseases who underwent pre- and post-contrast T1-weighted (T1CE), FLAIR, and diffusion-weighted imaging (DWI). A DL system that segments IMLLs, classifies tumourous conditions, and suggests clinical referral among surgery, systematic work-up, medical treatment, and conservative treatment, was developed. The system was validated in an independent cohort of 130 emergency patients, and performance in referral suggestion and tumour discrimination was compared with that of radiologists using receiver operating characteristics curve, precision-recall curve analysis, and confusion matrices. Multiparametric interpretable visualisation of high-relevance regions from layer-wise relevance propagation overlaid on contrast-enhanced T1WI and DWI was analysed. Results The DL system provided correct referral suggestions in 94 of 130 patients (72.3%) and performed comparably to radiologists (accuracy 72.6%, McNemar test; p = .942). For distinguishing tumours from non-tumourous conditions, the DL system (AUC, 0.90 and AUPRC, 0.94) performed similarly to human readers (AUC, 0.81 similar to 0.92, and AUPRC, 0.88 similar to 0.95). Solid portions of tumours showed a high overlap of relevance, but non-tumours did not (Dice coefficient 0.77 vs. 0.33, p < .001), demonstrating the DL's decision. Conclusions Our DL system could appropriately triage patients using multiparametric MRI and provide interpretability through multiparametric heatmaps, and may thereby aid neuroradiologic diagnoses in emergency settings.
Full Text
https://link.springer.com/article/10.1007/s00330-023-09710-0
DOI
10.1007/s00330-023-09710-0
Appears in Collections:
2. College of Dentistry (치과대학) > Dept. of Oral and Maxillofacial Radiology (영상치의학교실) > 1. Journal Papers
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/199452
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