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Demarcating infiltrative regions for aggressive resections is critical for improving prognostic outcomes but is challenging in neurosurgery. Herein, a multilayer sigmoid\u2010activated convolutional neural network (MLS\u2010CNN) is developed for rapidly distinguishing glioma tumor infiltration in brain tissue histology. Unlike conventional multiclass classifiers, the MLS\u2010CNN employs sigmoidal activation to accommodate coexisting classes within patch images. 59\u2009811 image patches (25\u2009807 infiltrating edge, 15\u2009178 normal brain, 18\u2009826 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\u2010of\u2010the\u2010art Vision Transformer (ViT) (accuracy: 89.45; AUC: 0.947). The MLS\u2010CNN 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\u2010CNN 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.<\/jats:p>","DOI":"10.1002\/aisy.202300397","type":"journal-article","created":{"date-parts":[[2023,9,21]],"date-time":"2023-09-21T22:58:43Z","timestamp":1695337123000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Detecting Tumor Infiltration in Diffuse Gliomas with Deep Learning"],"prefix":"10.1002","volume":"5","author":[{"given":"Karthik","family":"Prathaban","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering Optical Bioimaging Laboratory College of Design and Engineering National University of Singapore  Singapore 117576 Singapore"}]},{"given":"Bingcheng","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Pathology National University Health System  Singapore 119074 Singapore"}]},{"given":"Char Loo","family":"Tan","sequence":"additional","affiliation":[{"name":"Department of Pathology National University Health System  Singapore 119074 Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0104-9135","authenticated-orcid":false,"given":"Zhiwei","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering Optical Bioimaging Laboratory College of Design and Engineering National University of Singapore  Singapore 117576 Singapore"}]}],"member":"311","published-online":{"date-parts":[[2023,9,21]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.5114\/wo.2018.73893"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1093\/neuonc\/nov119"},{"key":"e_1_2_8_4_1","doi-asserted-by":"crossref","unstructured":"K.Petrecca M. 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