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Journal of Imaging Informatics in Medicine

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10-01-2024 | Meningioma

An MRI-Based Deep Transfer Learning Radiomics Nomogram to Predict Ki-67 Proliferation Index of Meningioma

Authors: Chongfeng Duan, Dapeng Hao, Jiufa Cui, Gang Wang, Wenjian Xu, Nan Li, Xuejun Liu

Published in: Journal of Imaging Informatics in Medicine | Issue 2/2024

Abstract

The objective of this study was to predict Ki-67 proliferation index of meningioma by using a nomogram based on clinical, radiomics, and deep transfer learning (DTL) features. A total of 318 cases were enrolled in the study. The clinical, radiomics, and DTL features were selected to construct models. The calculation of radiomics and DTL score was completed by using selected features and correlation coefficient. The deep transfer learning radiomics (DTLR) nomogram was constructed by selected clinical features, radiomics score, and DTL score. The area under the receiver operator characteristic curve (AUC) was calculated. The models were compared by Delong test of AUCs and decision curve analysis (DCA). The features of sex, size, and peritumoral edema were selected to construct clinical model. Seven radiomics features and 15 DTL features were selected. The AUCs of clinical, radiomics, DTL model, and DTLR nomogram were 0.746, 0.75, 0.717, and 0.779 respectively. DTLR nomogram had the highest AUC of 0.779 (95% CI 0.6643–0.8943) with an accuracy rate of 0.734, a sensitivity value of 0.719, and a specificity value of 0.75 in test set. There was no significant difference in AUCs among four models in Delong test. The DTLR nomogram had a larger net benefit than other models across all the threshold probability. The DTLR nomogram had a satisfactory performance in Ki-67 prediction and could be a new evaluation method of meningioma which would be useful in the clinical decision-making.

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Title: An MRI-Based Deep Transfer Learning Radiomics Nomogram to Predict Ki-67 Proliferation Index of Meningioma
Authors: Chongfeng Duan
Dapeng Hao
Jiufa Cui
Gang Wang
Wenjian Xu
Nan Li
Xuejun Liu
Publication date: 10-01-2024
Publisher: Springer International Publishing
Keywords: Meningioma
Meningioma
Magnetic Resonance Imaging
Magnetic Resonance Imaging
Published in: Journal of Imaging Informatics in Medicine / Issue 2/2024
Print ISSN: 2948-2925
Electronic ISSN: 2948-2933
DOI: https://doi.org/10.1007/s10278-023-00937-3

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An MRI-Based Deep Transfer Learning Radiomics Nomogram to Predict Ki-67 Proliferation Index of Meningioma

Abstract

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Abstract

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