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<p>Gliomas are the most common primary malignant tumors of the central nervous system and show marked imaging heterogeneity, making accurate preoperative prediction of 24-month overall survival status important for individualized treatment planning and prognostic counseling. Summary receiver operating characteristic analyses were used to systematically compare combinations of convolutional backbone depth and input channel counts to identify an optimal sequence–channel configuration. Guided by these findings, we developed an end-to-end multisequence–multichannel fusion aggregation (EMFA) framework that integrates deep transfer learning image representations with radiomics features. A multi-instance learning (MIL) module was further incorporated to enable adaptive within-sequence weighting and cross-sequence feature aggregation, improving model interpretability. In the held-out test cohort, the EMFA framework achieved an area under the receiver operating characteristic curve (AUC) of 0.899 and an accuracy of 0.935 (93.5%), showing competitive performance relative to corresponding single-sequence baselines. The configuration analysis indicated that nine-channel inputs for T1-weighted and T2-weighted imaging and a three-channel input for contrast-enhanced T1-weighted imaging provided the best overall performance. These results suggest that EMFA offers an effective and scalable strategy for fusing multisequence magnetic resonance imaging (MRI) information for 24-month glioma survival-status prediction, supporting imaging-informatics–driven clinical decision support and potential future translation into neuroradiology workflows.</p>

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End-to-end 2.5D multisequence–multichannel fusion model for preoperative survival prediction in glioma: a retrospective study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月4日

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  • Peichen Lv,
  • Yiming Ren,
  • Jingru Chang,
  • Man Wang,
  • Yangyingqiu Liu,
  • Yanwei Miao,
  • Xiaoyang He

摘要

Gliomas are the most common primary malignant tumors of the central nervous system and show marked imaging heterogeneity, making accurate preoperative prediction of 24-month overall survival status important for individualized treatment planning and prognostic counseling. Summary receiver operating characteristic analyses were used to systematically compare combinations of convolutional backbone depth and input channel counts to identify an optimal sequence–channel configuration. Guided by these findings, we developed an end-to-end multisequence–multichannel fusion aggregation (EMFA) framework that integrates deep transfer learning image representations with radiomics features. A multi-instance learning (MIL) module was further incorporated to enable adaptive within-sequence weighting and cross-sequence feature aggregation, improving model interpretability. In the held-out test cohort, the EMFA framework achieved an area under the receiver operating characteristic curve (AUC) of 0.899 and an accuracy of 0.935 (93.5%), showing competitive performance relative to corresponding single-sequence baselines. The configuration analysis indicated that nine-channel inputs for T1-weighted and T2-weighted imaging and a three-channel input for contrast-enhanced T1-weighted imaging provided the best overall performance. These results suggest that EMFA offers an effective and scalable strategy for fusing multisequence magnetic resonance imaging (MRI) information for 24-month glioma survival-status prediction, supporting imaging-informatics–driven clinical decision support and potential future translation into neuroradiology workflows.