Predicting intraoperative 5-ALA-induced tumor fluorescence via MRI and deep learning in gliomas with radiographic lower-grade characteristics
Our findings highlight the potential of a U-Net model, coupled with a Random Forest classifier, for pre-operative prediction of intraoperative fluorescence. We achieved high accuracy using the features extracted by the U-Net model pre-trained for brain tumor segmentation. While the model can still be improved, it has the potential for evaluating when to administer 5-ALA to gliomas lacking typical high-grade radiographic features.
Authors
- Sidong Liu (ORCID: https://orcid.org/0000-0002-2371-0713)
- Eric Suero Molina (ORCID: https://orcid.org/0000-0001-5652-2105)
- Carlo Russo (ORCID: https://orcid.org/0000-0001-8296-4345)
- Hermann Krähling (ORCID: https://orcid.org/0000-0003-2639-8400)
- Antonio Di Ieva (ORCID: https://orcid.org/0000-0002-5341-5416)
- Ghasem Azemi (ORCID: https://orcid.org/0000-0002-8675-6858)
- Walter Stummer (ORCID: https://orcid.org/0000-0002-3460-0640)
- Zeynep Canan Özdemir (ORCID: https://orcid.org/0000-0002-9172-9627)
- Alex Valls Chavarria
Institutions
- University Hospital Münster (DE)
- Macquarie University (AU)
Publication Details
- Journal
- Journal of Neuro-Oncology
- Published
- 2024-11-19
- DOI
- https://doi.org/10.1007/s11060-024-04875-0
- Citations
- 7
- Primary Topic
- Glioma Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 3.07