Jun, Yohan Park, Yae Won Shin, Hyungseob Shin, Yejee Lee, Jeong Ryong Han, Kyunghwa Ahn, Sung Soo Lim, Soo Mee Hwang, Dosik Lee, Seung-Koo 2024-01-19T09:01:04Z 2024-01-19T09:01:04Z 2023-05-18 2023-09 0938-7994 https://pubs.kist.re.kr/handle/201004/113370 ObjectivesTo establish a robust interpretable multiparametric deep learning (DL) model for automatic noninvasive grading of meningiomas along with segmentation.MethodsIn 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.ResultsOn 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.ConclusionsAn interpretable multiparametric DL model combining T1C and T2 can enable fully automatic grading of meningiomas along with segmentation. English Springer Verlag Intelligent noninvasive meningioma grading with a fully automatic segmentation using interpretable multiparametric deep learning Article 10.1007/s00330-023-09590-4 1 European Radiology, v.33, no.9, pp.6124 - 6133 European Radiology 33 9 6124 6133 N scie scopus 000971718300005 2-s2.0-85152524786 Radiology, Nuclear Medicine & Medical Imaging Radiology, Nuclear Medicine & Medical Imaging Article CONSENSUS RECOMMENDATIONS CLINICAL-TRIALS DIAGNOSIS PROTOCOL BRAIN Interpretable Deep learning Magnetic resonance imaging Meningioma grading