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FoTNet Enables Preoperative Differentiation of Malignant Brain Tumors with Deep Learning

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Cancer Prevention, Detection, and Intervention (CaPTion 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15199))

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Abstract

Glioblastoma (GBM), primary central nervous system lymphoma (PCNSL), and brain metastases (BM) are three common malignant central nervous system tumors. Accurate preoperative differentiation is essential for appropriate treatment planning and prognosis, however, it’s challenging to differentiate these tumors using MRI due to their similar anatomical structures and imaging characteristics. In this paper, we first construct a new multi-center brain MRI dataset, including 315 training cases (GBM 64, PCNSL 59, BM 192) and 124 external test cases (24:23:77). Moreover, we propose a novel framework FoTNet for accurate diagnosis of the three tumors. Our model achieves a classification accuracy of 92.5% and an average AUC of 0.9754, outperforming previous methods. Our results demonstrates the great potential of AI in assisting physicians in differentiating between GBM, PCNSL, and BM, particularly in resource-limited clinical settings.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (Grant No. 62106222), the Natural Science Foundation of Zhejiang Province, China (Grant No. LZ23F020008), the Foundation of medical and health technology of Zhejiang province, China (2023RC189) and the Zhejiang University-Angelalign Inc. R&D Center for Intelligent Healthcare.

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Correspondence to Zuozhu Liu or Junhui Lv .

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Hong, C., Wang, H., Wu, Z., Liu, Z., Lv, J. (2025). FoTNet Enables Preoperative Differentiation of Malignant Brain Tumors with Deep Learning. In: Ali, S., van der Sommen, F., Papież, B.W., Ghatwary, N., Jin, Y., Kolenbrander, I. (eds) Cancer Prevention, Detection, and Intervention. CaPTion 2024. Lecture Notes in Computer Science, vol 15199. Springer, Cham. https://doi.org/10.1007/978-3-031-73376-5_2

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  • DOI: https://doi.org/10.1007/978-3-031-73376-5_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-73375-8

  • Online ISBN: 978-3-031-73376-5

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