Shin, Hyungseob Park, Ji Eun Jun, Yohan Eo, Taejoon Lee, Jeongryong Kim, Ji Eun Lee, Da Hyun Moon, Hye Hyeon Park, Sang Ik Kim, Seonok Hwang, Dosik Kim, Ho Sung 2024-01-19T09:03:23Z 2024-01-19T09:03:23Z 2023-05-25 2023-08 0938-7994 https://pubs.kist.re.kr/handle/201004/113468 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&apos;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. English Springer Verlag Deep learning referral suggestion and tumour discrimination using explainable artificial intelligence applied to multiparametric MRI Article 10.1007/s00330-023-09710-0 1 European Radiology, v.33, no.8, pp.5859 - 5870 European Radiology 33 8 5859 5870 N scie scopus 000982825000001 2-s2.0-85158168788 Radiology, Nuclear Medicine & Medical Imaging Radiology, Nuclear Medicine & Medical Imaging Article TUMEFACTIVE DEMYELINATING LESIONS BRAIN-ABSCESS Deep learning Brain diseases Image interpretation computer-assisted Brain tumours