TY - GEN
T1 - Thread the Needle
T2 - 28th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2025
AU - Zhong, Yi
AU - Zheng, Xubin
AU - Shen, Xiongri
AU - Wang, Jiaqi
AU - Zhao, Leilei
AU - Song, Zhenxi
AU - Zhang, Zhiguo
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
PY - 2026
Y1 - 2026
N2 - Glioma remains one of the most lethal malignancy, making accurate prognosis crucial for personalized treatment and improved patient outcome. Existing models based on non-invasive magnetic resonance imaging (MRI) offer convenience, but they suffer from the poor performance and generalizability compared to genomic biomarkers, limiting their clinical adoption. Genomic biomarkers, such as IDH mutation and 1p/19q co-deletion, provide superior prognostic value but are restricted by their reliance on invasive surgical sampling. In this study, we hypothesize that these genomic biomarkers can guide the development of more robust MRI-based prognostic models, and propose a genomics-guided prompt learning framework that leverages both MRI and transcriptomic data to enhance survival prediction. Specifically, we introduce a novel visual modeling strategy for comprehensive glioma MRI representation and a Prompt-bridged Attention mechanism that can fuse multiple modalities during training and enhance visual representations during inference. Experimental results demonstrate that our proposed method achieves c-indeces of 0.6709 and 0.6904 on UCSF-PDGM and TCGA-GBM datasets, respectively, with highly significant p-values of 5.27×10-14 and 6.72×10-7. These results substantially outperform existing methods, presenting a promising step toward reliable and non-invasive glioma prognosis prediction.
AB - Glioma remains one of the most lethal malignancy, making accurate prognosis crucial for personalized treatment and improved patient outcome. Existing models based on non-invasive magnetic resonance imaging (MRI) offer convenience, but they suffer from the poor performance and generalizability compared to genomic biomarkers, limiting their clinical adoption. Genomic biomarkers, such as IDH mutation and 1p/19q co-deletion, provide superior prognostic value but are restricted by their reliance on invasive surgical sampling. In this study, we hypothesize that these genomic biomarkers can guide the development of more robust MRI-based prognostic models, and propose a genomics-guided prompt learning framework that leverages both MRI and transcriptomic data to enhance survival prediction. Specifically, we introduce a novel visual modeling strategy for comprehensive glioma MRI representation and a Prompt-bridged Attention mechanism that can fuse multiple modalities during training and enhance visual representations during inference. Experimental results demonstrate that our proposed method achieves c-indeces of 0.6709 and 0.6904 on UCSF-PDGM and TCGA-GBM datasets, respectively, with highly significant p-values of 5.27×10-14 and 6.72×10-7. These results substantially outperform existing methods, presenting a promising step toward reliable and non-invasive glioma prognosis prediction.
KW - Genomics-Radiomics
KW - Prompt Learning
KW - Survival prediction
UR - https://www.scopus.com/pages/publications/105017841914
U2 - 10.1007/978-3-032-04981-0_59
DO - 10.1007/978-3-032-04981-0_59
M3 - 会议稿件
AN - SCOPUS:105017841914
SN - 9783032049803
T3 - Lecture Notes in Computer Science
SP - 625
EP - 635
BT - Medical Image Computing and Computer Assisted Intervention, MICCAI 2025 - 28th International Conference, Proceedings
A2 - Gee, James C.
A2 - Hong, Jaesung
A2 - Sudre, Carole H.
A2 - Golland, Polina
A2 - Alexander, Daniel C.
A2 - Iglesias, Juan Eugenio
A2 - Venkataraman, Archana
A2 - Kim, Jong Hyo
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 23 September 2025 through 27 September 2025
ER -