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Intelligent Noninvasive Meningioma Grading with a Fully Automatic Segmentation Using Interpretable Multiparametric Deep Learning by Yohan Jun; Yae Won Park; Hyungseob Shin; Yejee Shin; Jeong Ryong Lee; Kyunghwa Han; Sung Soo Ahn; Soo Mee Lim; Dosik Hwang; Seung-Koo Lee is a Medicine article available to read on EtoBox.

What is Intelligent Noninvasive Meningioma Grading with a Fully Automatic Segmentation Using Interpretable Multiparametric Deep Learning about?

Objectives To establish a robust interpretable multiparametric deep learning (DL) model for automatic noninvasive grading of meningiomas along with segmentation. Methods In total, 257 patients with pathologically confirmed meningiomas (162 low-grade, 95 high-grade) who underwent a preoperative brain MRI, including T2-weighted (T2) and contrast-enhanced T1-weighted images (T1C), were included in the institutional training set. A two-stage DL grading model was constructed for segmentation and classification based on multiparametric three-dimensional U-net and ResNet. The models were validated in the external validation set consisting of 61 patients with meningiomas (46 low-grade, 15 high-grade). Relevance-weighted Class Activation Mapping (RCAM) method was used to interpret the DL features contributing to the prediction of the DL grading model. Results On external validation, the combined T1C and T2 model showed a Dice coefficient of 0.910 in segmentation and the highest performance for meningioma grading compared to the T2 or T1C only models, with an area under the curve (AUC) of 0.770 (95% confidence interval: 0.644-0.895) and accuracy, sensitivity, and specificity of 72.1%, 73.3%,

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Author

Yohan Jun; Yae Won Park; Hyungseob Shin; Yejee Shin; Jeong Ryong Lee; Kyunghwa Han; Sung Soo Ahn; Soo Mee Lim; Dosik Hwang; Seung-Koo Lee

Publisher

Springer Science and Business Media LLC

Published

2023

Language

EN

Field

Medicine (Health Sciences)