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Abstract
Diffuse glioma is the most prevalent malignant brain tumor, and accurate prognosis prediction is essential in personalized treatment to improve outcomes. Although numerous deep learning based methods have been proposed for prognosis prediction, most of them rely solely on local image features of tumor regions. They neglect the fact that the brain is an integrated and highly interconnected system, and tumors can disrupt the global brain connectivity, which is closely associated with prognosis. To address this limitation, we present a novel heterogeneous framework that integrates global brain connectivity and local image features for prognosis prediction. Specifically, our framework consists of a DTI based graph neural network (GNN) and a structural MR based local feature network (LFN), which are coupled through an iterative pipeline. In each iteration, the GNN models the global connectivity among brain regions (nodes) with consideration of brain compensation caused by tumor disruptions, while the LFN leverages the resulting global brain connectivity to extract effective local image features from each brain region, which are used to refine the brain nodes in the GNN for improved connectivity modeling in the next iteration. Through the iterative process, the global brain connectivity in the GNN and the local image features in the LFN are mutually enhanced, leading to accurate prognosis prediction. Extensive experiments on a public UCSF dataset of 493 diffuse glioma patients demonstrate that our framework outperforms the state-of-the-art methods. Further ablation studies reveal that the global brain connectivity is crucial and highly related to prognosis.
Links to Paper and Supplementary Materials
Main Paper (Open Access Version): https://papers.miccai.org/miccai-2026/paper/1042_paper.pdf
SharedIt Link: Not yet available
SpringerLink (DOI): Not yet available
Supplementary Material: Not Submitted
Link to the Code Repository
N/A
Link to the Dataset(s)
Cam-CAN dataset: https://cam-can.mrc-cbu.cam.ac.uk/dataset/
UCSF-PDGM dataset: https://www.cancerimagingarchive.net/collection/ucsf-pdgm/
BibTex
@InProceedings{LinJin_AHeterogeneous_MICCAI2026,
author = { Lin, Jingfeng AND Pan, Junjun AND Wang, Jinda AND Tang, Zhenyu},
title = { { A Heterogeneous Prognosis Prediction Framework with Global Brain Connectivity and Local Image Features } },
booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2026},
year = {2026},
publisher = {Springer Nature Switzerland},
volume = {LNCS 16887},
month = {September},
page = {pending}
}
Reviews
Review #1
- Please describe the contribution of the paper
This paper presents a novel heterogeneous framework for predicting the prognosis of diffuse glioma by integrating global brain connectivity derived from DTI with local image features extracted from structural MRI. The methodology couples a GNN with a LFN in an iterative pipeline, explicitly modeling the brain’s compensatory mechanisms in response to tumor disruption. The proposed framework is evaluated on the public UCSF dataset and demonstrates superior performance compared to 14 state-of-the-art methods across multiple metrics. The work addresses a valid clinical limitation—the neglect of global brain network disruption in current deep learning models—and provides a well-engineered solution to fuse complementary modalities. While the technical execution is solid and the results are promising, there are concerns regarding methodological clarity, generalizability, and computational justification that prevent a higher score.
- Please list the major strengths of the paper: you should highlight a novel formulation, an original way to use data, demonstration of clinical feasibility, a novel application, a particularly strong evaluation, or anything else that is a strong aspect of this work. Please provide details, for instance, if a method is novel, explain what aspect is novel and why this is interesting.
The explicit modeling of the brain’s compensatory mechanism via a modified adjacency matrix and sparsity loss is a compelling insight. It bridges neuro-oncology principles with graph learning design, moving beyond standard feature concatenation. The mutual refinement between the GNN (global connectivity) and LFN (local regional features) is a strong architectural contribution. The visualization of the attention matrix convergence across iterations supports the claim that the two streams enhance each other. The evaluation against 14 SOTA methods, including both radiomics，deep learning and GNN-based approaches, provides a convincing benchmark. The ablation studies effectively isolate the contribution of the compensation mechanism and the iterative pipeline.
- Please list the major weaknesses of the paper. Please provide details: for instance, if you state that a formulation, way of using data, demonstration of clinical feasibility, or application is not novel, then you must provide specific references to prior work.
The framework introduces several specific design choices that lack empirical or theoretical justification. For instance, the threshold for defining tumor-invaded regions is arbitrarily set at a voxel ratio of >40%, and the number of iterations is fixed at six based on “empirical observations. “ The paper does not discuss the sensitivity of the model to these choices nor the computational overhead of unrolling the model six times during training and inference. Regarding the CamCAN healthy reference: You use it to compute &A_{healthy} . Have you tested the model’s performance when using the average connectivity of the non-tumor hemispheres of the UCSF patients themselves as a pseudo-healthy reference? In the revision, please elaborate on Equation (7). A diagram or detailed description of how the 90x90 attention matrix transforms the 3D feature volumes of 90 regions would significantly improve the clarity of the method section.
- Please rate the clarity and organization of this paper
Good
- Please comment on the reproducibility of the paper. Please be aware that providing code and data is a plus, but not a requirement for acceptance.
The submission does not mention open access to source code or data, but provides a clear and detailed description of the algorithm to ensure reproducibility.
- Based on your review and your understanding of the MICCAI Scientific Code of Ethics, do you believe this submission may involve a potential ethics concern or violation?
N/A
- Optional: If you have any additional comments to share with the authors, please provide them here. Please also refer to our Reviewer’s guide on what makes a good review and pay specific attention to the different assessment criteria for the different paper categories: https://conferences.miccai.org/2026/en/REVIEWER-GUIDELINES.html
N/A
- Rate the paper on a scale of 1-6, 6 being the strongest (6-4: accept; 3-1: reject). Please use the entire range of the distribution. Spreading the score helps create a distribution for decision-making.
(4) Weak Accept — marginally above the acceptance threshold, but would not mind if rejected, dependent on rebuttal
- Please justify your recommendation. What were the major factors that led you to your overall score for this paper?
The paper offers a conceptually strong contribution by coupling global brain connectivity with local imaging features through an iterative GNN-LFN framework and explicitly modeling compensatory reorganization, which is clinically novel. The comprehensive benchmarking against 14 methods on a public cohort is commendable. However, the work is held back by several unresolved issues: arbitrary parameter choices (e.g., fixed six iterations and a 40% tumor threshold) lack sensitivity analysis, the heavy reliance on an external healthy control dataset (CamCAN) raises practical generalizability concerns, and the technical description of connectivity-guided feature fusion (Equation 7) remains opaque. A convincing rebuttal that clarifies these points would solidify acceptance; otherwise, I would not object to rejection.
- Reviewer confidence
Confident but not absolutely certain (3)
- [Post rebuttal] After reading the authors’ rebuttal, please state your final opinion of the paper.
Accept
- [Post rebuttal] Please justify your final decision from above.
The main reason is that the paper presents a well-motivated multimodal prognostic framework with a clear degree of novelty and promising empirical performance. The integration of structural connectomics with local MRI features is clinically meaningful, and the iterative mutual refinement scheme is more interesting than standard multimodal fusion. The benchmarking is extensive, and the rebuttal successfully addresses several concerns about external reference data, structural priors, fairness of baselines, and computational practicality. Although some concerns are not fully eliminated, they have been sufficiently reduced such that they no longer outweigh the paper’s strengths. In particular, the authors now provide a more credible justification for the use of Cam-CAN, explain why the contralateral hemisphere is not an appropriate substitute, and support the compensatory prior with an alternative-prior comparison. These clarifications improve my confidence that the proposed design choices are thoughtful rather than arbitrary. I still believe the revised manuscript should more clearly present the sensitivity analyses and improve the explanation of the connectivity-guided feature fusion mechanism. However, these now seem like fixable presentation and completeness issues, rather than flaws that undermine the central contribution. On balance, I think the paper is marginally above the acceptance threshold.
Review #2
- Please describe the contribution of the paper
This paper presents a heterogeneous framework for diffuse glioma prognosis prediction that couples DTI-driven structural connectivity with regional features extracted from structural MRI in an iterative pipeline, and further introduces a compensation-aware graph design for Cox-based survival risk prediction.
- Please list the major strengths of the paper: you should highlight a novel formulation, an original way to use data, demonstration of clinical feasibility, a novel application, a particularly strong evaluation, or anything else that is a strong aspect of this work. Please provide details, for instance, if a method is novel, explain what aspect is novel and why this is interesting.
The paper presents a relatively novel architecture that fuses structural connectivity and region-level imaging features through an iterative coupling scheme, where global connectivity guides local representations and refined local features are fed back to update graph nodes. The paper also introduces clinically and biologically motivated priors into prognosis modeling by explicitly considering whole-brain network disruption and a compensation-related mechanism in diffuse glioma.
- Please list the major weaknesses of the paper. Please provide details: for instance, if you state that a formulation, way of using data, demonstration of clinical feasibility, or application is not novel, then you must provide specific references to prior work.
1.Unfair and incomplete baseline comparison. The experimental comparison is not sufficiently fair to support the claimed contribution. The paper mainly contrasts local image feature (LIF) methods and global brain connectivity (GBC) methods, but does not include multimodal baselines that combine DTI-based structural networks with structural MRI features under comparable settings. In particular, the manuscript lacks simple but strong baselines such as direct early/late fusion of a DTI graph branch and an sMRI encoder branch, cross-attention fusion, or a unified heterogeneous graph model without the compensation prior. Without such comparisons, it is difficult to determine whether the performance gain truly comes from the proposed core idea, or simply from using more modalities, more modules, and stronger inductive bias.
2.The claimed mechanism is not clearly validated. The contribution and functional role of the proposed compensation mechanism are not convincingly established. The so-called compensatory mechanism appears more like a strong hand-crafted prior than a mechanism learned from data and rigorously verified. The model explicitly rewrites the graph structure by enforcing connections between tumor-invaded regions and peritumoral/contralateral regions, and further imposes sparsity on tumor-node attention to favor a limited set of compensatory regions. As a result, the mechanism is injected into the model rather than discovered by it. More importantly, the paper does not show that this prior is necessary or correct for prognosis prediction: there are no comparisons with weaker or more generic structural priors, no stability analysis across different tumor thresholds, compensation definitions, or sparsity weights, and no direct evidence that the gain comes specifically from modeling compensation rather than from graph modification in general.
3.Key hyperparameters and design choices are insufficiently justified. Several important methodological choices appear arbitrary and are not adequately motivated. For example, the paper directly adopts AAL-90 for parcellation, uses PANDA to construct white matter connectivity from DTI, binarizes edges with a fixed threshold of 0.2, and defines tumor-invaded regions using a tumor voxel ratio greater than 40%. However, the manuscript does not explain how these thresholds were selected, whether they were inherited from prior work, tuned on training data, or chosen heuristically. No sensitivity analysis is provided, which weakens the credibility and reproducibility of the method.
4.The writing and presentation are weak. The manuscript is difficult to follow in several places. The introduction does not clearly develop the motivation for the design, and the method section does not cleanly separate the high-level architecture from implementation details and hyperparameter choices. Some definitions are also unclear or inconsistent; for example, the specific image encoder used for local feature extraction is not clearly described, and the notation around Eq. (8) is confusing (What is A_ori?). The experimental section is also poorly organized, with dataset description, baselines, and evaluation metrics presented in a mixed manner. In addition, the result analysis is relatively shallow, and Fig. 3 is not easy to interpret.
- Please rate the clarity and organization of this paper
Poor
- Please comment on the reproducibility of the paper. Please be aware that providing code and data is a plus, but not a requirement for acceptance.
The submission does not provide sufficient information for reproducibility.
- Based on your review and your understanding of the MICCAI Scientific Code of Ethics, do you believe this submission may involve a potential ethics concern or violation?
N/A
- Optional: If you have any additional comments to share with the authors, please provide them here. Please also refer to our Reviewer’s guide on what makes a good review and pay specific attention to the different assessment criteria for the different paper categories: https://conferences.miccai.org/2026/en/REVIEWER-GUIDELINES.html
N/A
- Rate the paper on a scale of 1-6, 6 being the strongest (6-4: accept; 3-1: reject). Please use the entire range of the distribution. Spreading the score helps create a distribution for decision-making.
(2) Reject — should be rejected, independent of rebuttal
- Please justify your recommendation. What were the major factors that led you to your overall score for this paper?
While the paper explores an interesting and clinically motivated direction, I do not think the current version provides sufficiently strong evidence to support its main claims. The iterative coupling of structural connectivity and regional imaging features is a meaningful idea, and the attempt to incorporate whole-brain network disruption and compensation-related priors is a positive aspect.
That said, my main concerns are about rigor and validation. The baseline comparison is not fully convincing, since strong modality-matched multimodal baselines are missing; several key design choices and hyperparameters are insufficiently justified; and the proposed compensation mechanism is not yet validated clearly enough to show that the observed gains truly come from this specific modeling idea. In addition, the presentation could be improved in terms of clarity and organization. Overall, I felt the paper has potential, but in its current form it does not yet meet the standard for acceptance.
- Reviewer confidence
Very confident (4)
- [Post rebuttal] After reading the authors’ rebuttal, please state your final opinion of the paper.
N/A
- [Post rebuttal] Please justify your final decision from above.
N/A
Review #3
- Please describe the contribution of the paper
The authors propose a heterogeneous deep learning framework for the prognosis prediction of diffuse glioma. The framework integrates global brain connectivity (derived from DTI) via a Graph Attention Network (GAT) and local image features (derived from multimodal sMR) via a Local Feature Network (LFN). These two networks are coupled iteratively to mutually refine node features and graph topology. The authors also introduce a “compensatory mechanism” by modifying the adjacency matrix to force connections between tumor-invaded regions and peritumoral/contralateral regions.
- Please list the major strengths of the paper: you should highlight a novel formulation, an original way to use data, demonstration of clinical feasibility, a novel application, a particularly strong evaluation, or anything else that is a strong aspect of this work. Please provide details, for instance, if a method is novel, explain what aspect is novel and why this is interesting.
1.The idea of iteratively coupling structural MRI (local features) and DTI (global connectivity) is conceptually elegant and well-motivated for brain tumor analysis. 2.The authors evaluate their method against a robust set of 14 state-of-the-art methods, showing strong quantitative improvements across multiple metrics.
- The ablation study clearly demonstrates the added value of the iterative pipeline and the compensation module.
- Please list the major weaknesses of the paper. Please provide details: for instance, if you state that a formulation, way of using data, demonstration of clinical feasibility, or application is not novel, then you must provide specific references to prior work.
1.Diffuse gliomas frequently cause severe structural deformation (mass effect), shifting midline structures and distorting normal anatomy. Standard linear or non-linear registration to a healthy atlas (like AAL) fails drastically in the presence of large tumors. 2.Equation (1) explicitly hardcodes connections between tumor-invaded regions and peritumoral/contralateral regions. The model highlights peritumoral and contralateral regions because the authors manually wired the graph to prioritize them, not necessarily because the network organically discovered a biological compensatory mechanism. 3.Cam-CAN is a completely different dataset with likely different acquisition protocols, scanner types, and preprocessing pipelines compared to the UCSF glioma dataset. The authors do not address how they harmonize the DTI data between UCSF and Cam-CAN.
- Please rate the clarity and organization of this paper
Good
- Please comment on the reproducibility of the paper. Please be aware that providing code and data is a plus, but not a requirement for acceptance.
The submission does not mention open access to source code or data, but provides a clear and detailed description of the algorithm to ensure reproducibility.
- Based on your review and your understanding of the MICCAI Scientific Code of Ethics, do you believe this submission may involve a potential ethics concern or violation?
N/A
- Optional: If you have any additional comments to share with the authors, please provide them here. Please also refer to our Reviewer’s guide on what makes a good review and pay specific attention to the different assessment criteria for the different paper categories: https://conferences.miccai.org/2026/en/REVIEWER-GUIDELINES.html
N/A
- Rate the paper on a scale of 1-6, 6 being the strongest (6-4: accept; 3-1: reject). Please use the entire range of the distribution. Spreading the score helps create a distribution for decision-making.
(3) Weak Reject — marginally below the acceptance threshold, but would not mind if accepted, dependent on rebuttal
- Please justify your recommendation. What were the major factors that led you to your overall score for this paper?
The methodological flaw of computing patient-specific connectivity alterations by subtracting a healthy baseline from a completely different dataset/scanner invalidates the core input of the model. The results are highly likely driven by confounding site effects.
- Reviewer confidence
Confident but not absolutely certain (3)
- [Post rebuttal] After reading the authors’ rebuttal, please state your final opinion of the paper.
Accept
- [Post rebuttal] Please justify your final decision from above.
The authors have satisfactorily addressed these concerns: 1.The clarification that ComBat harmonization and age-matching were applied during preprocessing significantly alleviates my concerns regarding confounding site effects. This is a standard and robust approach. 2.The use of ANTs-based nonlinear registration with lesion masking is the correct methodological choice for dealing with mass effect in glioma patients. 3.The authors provided a compelling justification for their structural prior by comparing it to a fully connected strategy, demonstrating a significant performance drop (C-index 0.663 vs 0.717) when the model is left to learn connections without the clinical prior.
I am raising my score to a “Weak Accept” on the strict condition that all these clarifications are explicitly integrated into the final camera-ready version of the paper, as the method is not reproducible or scientifically sound without them.
Author Feedback
We thank the reviewers for recognizing the novelty of our framework (R1–R3). We respond to the key concerns regarding methodology and validation as follows:
Q1.The reliance on the external Cam-CAN dataset and potential domain shift/harmonization issues: To mitigate potential domain shift between the Cam-CAN and UCSF datasets, we applied ComBat harmonization during preprocessing, which is a widely used strategy for multi-site data harmonization (Solanes et al., NeuroImage 2023). Furthermore, the selected Cam-CAN subjects were age-matched to the UCSF cohort (55.91 ± 17.71 vs. 56.80 ± 15.06 years), reducing confounding from age-related connectome changes.
Q2.Why not use the contralateral non-tumor hemisphere as a pseudo-healthy reference? Given that glioma can induce whole-brain network alterations, the contralateral hemisphere may also exhibit connectivity disruption or compensatory reorganization, making it unsuitable as a reliable pseudo-healthy reference.
Q3.Robustness of atlas registration to tumor mass effect: To reduce the influence of tumor mass effect on atlas registration, we performed ANTs-based nonlinear registration with lesion masking (Wei et al., TMI 2023). This reduces the contribution of tumor-related abnormal regions to the similarity metric during optimization, making the registration primarily driven by non-lesioned brain tissues.
Q4.Lack of fair, modality-matched multimodal baselines: Our comparison includes modality-matched multimodal baselines: DC-R2SNs, IDH-GNN, and MaskGNN share our modality setting, with node features from sMRI and edge/connectivity features from DTI.
Q5.Necessity of the compensatory structural prior: The compensatory structural prior reduces the model search space, thereby improving performance, and this design is supported by clinical evidence (Duffau, Cortex 2014). We have also tested alternative structural priors, including a more flexible fully connected strategy in which tumor regions were connected to all normal brain regions, allowing the model to automatically learn potential compensatory connections. However, this strategy only achieved a C-index of 0.663, lower than 0.717 obtained with the compensatory structural prior. This may be because the fully connected strategy introduces many biologically less meaningful connections and aggravates graph over-smoothing, thereby reducing performance.
Q6.Hyperparameter sensitivity, training/inference time, and reproducibility: Based on clinical evidence that cross-regional compensation is less likely to occur when tumor involvement is below 30% or above 60% (Duffau, Cortex 2014), we tested tumor-voxel thresholds within the 30%–60% range. Model performance remained overall stable across this range, with 40% slightly outperforming the other thresholds. For the number of unrolling iterations, we observed that the attention matrix and brain-region features generally converged within 6 steps; further increasing the number of iterations only introduced additional computational cost without improving model performance. Under Ubuntu 22.04 with an RTX 3090 GPU, the training and inference times were 26.95 min per epoch and 41.9 s, respectively. To ensure reproducibility, we will release the code upon acceptance.
Meta-Review
Meta-review #1
- Your recommendation
Invite for Rebuttal
- Please justify your decision. In case you deviate from the reviewers’ recommendations, explain in detail the reasons why. In case of an invitation for rebuttal, clarify which points are important to address in the rebuttal.
Although reviewers appreciated the novel GNN-LFN framework integrating DTI and MRI for glioma prognosis (score: 4), critical concerns regarding methodology and validation warrant a rebuttal (scores: 2, 3). To support acceptance, the authors must address: (1) the reliance on the external Cam-CAN dataset and potential domain shift/harmonization issues; (2) the robustness of atlas registration against tumor mass effects; (3) the lack of fair, modality-matched multimodal baselines; and (4) justifications and sensitivity analyses for key handcrafted priors and hyperparameters (e.g., tumor threshold, iterations).
- After you have reviewed the rebuttal and updated reviews, please provide your recommendation based on all reviews and the authors’ rebuttal.
Accept
- Please justify your recommendation.
As strongly advised by Reviewers 1 and 3, acceptance is conditional upon explicitly incorporating these critical clarifications, along with the hyperparameter sensitivity analyses discussed in the rebuttal, into the camera-ready manuscript to guarantee scientific rigor and reproducibility.
Meta-review #2
- After you have reviewed the rebuttal and updated reviews, please provide your recommendation based on all reviews and the authors’ rebuttal.
Accept
- Please justify your recommendation.
After carefully reviewing the authors’ rebuttal, I have revised my initial assessment to Accept. The authors have provided a thorough, evidence-based response that directly addresses the core concerns raised by all three reviewers.
Meta-review #3
- After you have reviewed the rebuttal and updated reviews, please provide your recommendation based on all reviews and the authors’ rebuttal.
Accept
- Please justify your recommendation.
I recommend acceptance. Although the paper is borderline, the rebuttal substantially addressed the major concerns regarding domain shift, lesion-aware registration, modality-matched baselines, and the compensation prior. Two reviewers now support acceptance, and the remaining issues are mainly about presentation and completeness rather than core validity. The camera-ready version should explicitly include the rebuttal clarifications.