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The purpose of this study was to establish a robust interpretable deep learning (DL) model for the automatic noninvasive grading of meningiomas along with segmentation. Over 250 meningioma patients who underwent a preoperative brain MRI, including T2-weighted (T2) and contrast-enhanced T1-weighted (T1C) images, were included in the training set. The authors were able to determine that an interpretable multiparametric DL model combining T1C and T2 can enable the 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.

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

Authors: 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

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