Detecting Tumor Infiltration in Diffuse Gliomas with Deep Learning

Glioblastoma tumor recurrences often occur in brain tissue areas harboring infiltrating tumor cells, resembling healthy tissue in brain imaging. Demarcating infiltrative regions for aggressive resections is critical for improving prognostic outcomes but is challenging in neurosurgery. Herein, a multilayer sigmoid‐activated convolutional neural network (MLS‐CNN) is developed for rapidly distinguishing glioma tumor infiltration in brain tissue histology. Unlike conventional multiclass classifiers, the MLS‐CNN employs sigmoidal activation to accommodate coexisting classes within patch images. 59 811 image patches (25 807 infiltrating edge, 15 178 normal brain, 18 826 cellular tumor) from 73 brain tissue samples are extracted to train the classifier. The model achieves an accuracy of 91.70% (sensitivity: 91.62%; specificity: 91.78%) and area under the curve (AUC) of 0.964 in distinguishing infiltrating edges, outperforming the current state‐of‐the‐art Vision Transformer (ViT) (accuracy: 89.45; AUC: 0.947). The MLS‐CNN is computationally efficient, generating predictions within 11.5 s in comparison to 81.4 s for ViT. The predictions strongly correlate with In Situ Hybridization expression intensities, validating the utility of the MLS‐CNN model in spatial genomics investigations in gliomas. The robust model can therefore serve as an automatic and accurate classifier to help pathologists identify infiltrative glioma for better diagnosis and patient outcomes in brain oncology.

Object Details

• Location: Deutsche Nationalbibliothek Frankfurt am Main
• Extent: Online-Ressource
• Language: Englisch

References and Relationships

• Bibliographic citation: Detecting Tumor Infiltration in Diffuse Gliomas with Deep Learning ; day:21 ; month:09 ; year:2023 ; extent:12 ; Advanced intelligent systems ; (21.09.2023) (gesamt 12)

Contributors, Places and Time

• Creator: Prathaban, Karthik ; Wu, Bingcheng ; Tan, Char Loo ; Huang, Zhiwei

Further information

• DOI: 10.1002/aisy.202300397
• URN: urn:nbn:de:101:1-2023092215045008081155
• Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Last update: 14.08.2025, 10:49 AM CEST