Zhao Chen, Chen Meidi, Wen Xiaobo, Song Jianping, Yuan Yifan, Huang Qiu
The School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
The Affiliated Hospital of Qingdao University, Qingdao University, Qingdao, Shandong 266000, China; Department of Radiotherapy, Yunnan Cancer Hospital, the Third Affiliated Hospital of Kunming Medical University, Kunming, Yunnan 650118, China.
Comput Med Imaging Graph. 2025 Sep;124:102585. doi: 10.1016/j.compmedimag.2025.102585. Epub 2025 Jun 9.
Accurately predicting glioblastoma (GBM) recurrence is crucial for guiding the planning of target areas in subsequent radiotherapy and radiosurgery for glioma patients. Current prediction methods can determine the likelihood and type of recurrence but cannot identify the specific region or visually display location of the recurrence. To efficiently and accurately predict the recurrence of GBM, we proposed a three-step-guided prediction method consisting of feature extraction and segmentation (FES), radiomics analysis, and tag constraints to narrow the predicted region of GBM recurrence and standardize the shape of GBM recurrence prediction. Particularly in FES we developed an adaptive fusion module and a modality fusion module to fuse feature maps from different modalities. In the modality fusion module proposed, we designed different convolution modules (Conv-D and Conv-P) specifically for diffusion tensor imaging (DTI) and Positron Emission Computed Tomography (PET) images to extract recurrence-related features. Additionally, model fusion is proposed in the stable diffusion training process to learn and integrate the individual and typical properties of the recurrent tumors from different patients. Contrasted with existing segmentation and generation methods, our three-step-guided prediction method improves the ability to predict distant recurrence of GBM, achieving a 28.93 Fréchet Inception Distance (FID), and a 0.9113 Dice Similarity Coefficient (DSC). Quantitative results demonstrate the effectiveness of the proposed method in predicting the recurrence of GBM with the type and location. To the best of our knowledge, this is the first study combines the stable diffusion and multimodal images fusion with PET and DTI from different institutions to predict both distant and local recurrence of GBM in the form of images.
准确预测胶质母细胞瘤（GBM）复发对于指导胶质瘤患者后续放疗和放射外科治疗的靶区规划至关重要。目前的预测方法可以确定复发的可能性和类型，但无法识别复发的具体区域或直观显示其位置。为了高效准确地预测GBM复发，我们提出了一种三步引导预测方法，包括特征提取与分割（FES）、放射组学分析和标签约束，以缩小GBM复发的预测区域并规范GBM复发预测的形状。特别是在FES中，我们开发了一个自适应融合模块和一个模态融合模块，以融合来自不同模态的特征图。在提出的模态融合模块中，我们专门为扩散张量成像（DTI）和正电子发射计算机断层扫描（PET）图像设计了不同的卷积模块（Conv-D和Conv-P），以提取与复发相关的特征。此外，在稳定扩散训练过程中提出了模型融合，以学习和整合不同患者复发肿瘤的个体和典型特征。与现有的分割和生成方法相比，我们的三步引导预测方法提高了预测GBM远处复发的能力，实现了28.93的弗雷歇因距离（FID）和0.9113的骰子相似系数（DSC）。定量结果证明了该方法在预测GBM复发类型和位置方面的有效性。据我们所知，这是第一项将稳定扩散与多模态图像融合以及来自不同机构的PET和DTI相结合，以图像形式预测GBM远处和局部复发的研究。
