Intelligent noninvasive meningioma grading with a fully automatic segmentation using interpretable multiparametric deep learning

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

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

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%, and 71.7%, respectively. The AUC and accuracy of the combined DL grading model were higher than those of the human readers (AUCs of 0.675–0.690 and accuracies of 65.6–68.9%, respectively). The RCAM of the DL grading model showed activated maps at the surface regions of meningiomas indicating that the model recognized the features at the tumor margin for grading. Conclusions: An interpretable multiparametric DL model combining T1C and T2 can enable fully automatic grading of meningiomas along with segmentation. Key Points: • The multiparametric DL model showed robustness in grading and segmentation on external validation. • The diagnostic performance of the combined DL grading model was higher than that of the human readers. • The RCAM interpreted that DL grading model recognized the meaningful features at the tumor margin for grading.

Original languageEnglish
Pages (from-to)6124-6133
Number of pages10
JournalEuropean Radiology
Volume33
Issue number9
DOIs
StatePublished - Sep 2023

Bibliographical note

Publisher Copyright:
© 2023, The Author(s), under exclusive licence to European Society of Radiology.

Keywords

  • Deep learning
  • Interpretable
  • Magnetic resonance imaging
  • Meningioma, grading

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