Scholarlink

Surgical resection is the standard treatment for glioma patients, yet postoperative outcomes remain highly variable. In this study, we investigated structural brain reorganisation following surgery using a multiscale imaging approach. We extracted five key radiomic features from T1-weighted MRI scans within 116 anatomical regions and the tumor area and applied Support Vector Machine classification to identify regions predictive of shorter- versus longer-term survival. To characterise global structural variability, both Principal Component Analysis (PCA) and Uniform Manifold Approximation and Projection (UMAP) were applied across patients to derive low-dimensional spatial representations. Region-based analysis highlighted peri-tumoral cortices, as well as subcortical and cerebellar structures, as most discriminative. At the global level, the application of Euclidean distance computed in the embedding spaces revealed more pronounced longitudinal changes in shorter-term survivors, whereas longer-term survivors exhibited comparatively more stable patterns over time. Overlaying radiomic regions on Euclidean distance maps from the PCA components indicated partial spatial correspondence, suggesting complementary information from local and global features. These results demonstrate the utility of combining regional radiomics with global manifold learning to characterise postoperative brain changes and their association with survival. This multiscale framework may inform future prognostic models and guide surgical and therapeutic strategies in glioma patients.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

From Regions to Manifolds: Linking Radiomic Signatures with PCA and UMAP Brain Representations in Glioma

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • Carmen Jimenez-Mesa,
  • Yizhou Wan,
  • Giulio Sansone,
  • Nicolas J. Gallego-Molina,
  • Andres Ortiz,
  • Cristobal Vazquez-Garcia,
  • Juan M. Gorriz,
  • Stephen J. Price,
  • John Suckling,
  • Michail Mamalakis

摘要

Surgical resection is the standard treatment for glioma patients, yet postoperative outcomes remain highly variable. In this study, we investigated structural brain reorganisation following surgery using a multiscale imaging approach. We extracted five key radiomic features from T1-weighted MRI scans within 116 anatomical regions and the tumor area and applied Support Vector Machine classification to identify regions predictive of shorter- versus longer-term survival. To characterise global structural variability, both Principal Component Analysis (PCA) and Uniform Manifold Approximation and Projection (UMAP) were applied across patients to derive low-dimensional spatial representations. Region-based analysis highlighted peri-tumoral cortices, as well as subcortical and cerebellar structures, as most discriminative. At the global level, the application of Euclidean distance computed in the embedding spaces revealed more pronounced longitudinal changes in shorter-term survivors, whereas longer-term survivors exhibited comparatively more stable patterns over time. Overlaying radiomic regions on Euclidean distance maps from the PCA components indicated partial spatial correspondence, suggesting complementary information from local and global features. These results demonstrate the utility of combining regional radiomics with global manifold learning to characterise postoperative brain changes and their association with survival. This multiscale framework may inform future prognostic models and guide surgical and therapeutic strategies in glioma patients.