Artificial neural networks as a prognostic tool using hyperspectral imaging on pretherapeutic histopathological specimens of esophageal adenocarcinoma
This study demonstrates the potential of combining HSI with ANNs to predict treatment response in EAC. Among the models evaluated, the 3D-CNN showed the most balanced performance, effectively leveraging spatial and spectral features, while the Hybrid-SN and 2D-CNN models excelled in sensitivity and specificity, respectively. These findings underline the feasibility of using AI-driven analysis of histopathological HSI data to support personalized treatment planning in EAC, paving the way for more accurate and tailored therapeutic strategies.
Authors
- Yusef Moulla (ORCID: https://orcid.org/0000-0002-5936-0217)
- Stefan Niebisch (ORCID: https://orcid.org/0000-0001-7049-2854)
- P Bischoff
- Marianne Maktabi (ORCID: https://orcid.org/0000-0001-6954-4530)
- Boris Jansen‐Winkeln (ORCID: https://orcid.org/0000-0002-3996-9391)
- Patrick Sven Plum (ORCID: https://orcid.org/0000-0002-8165-4553)
- Katrin Schierle (ORCID: https://orcid.org/0000-0002-5188-5537)
- Ines Gockel (ORCID: https://orcid.org/0000-0001-7423-713X)
- René Thieme (ORCID: https://orcid.org/0000-0002-0537-3979)
- Christel Teresa Trifone
Institutions
- Anhalt University of Applied Sciences (DE)
- Klinikum Magdeburg (DE)
- University Hospital Leipzig (DE)
- SLK-Kliniken Heilbronn (DE)
- Klinikum Chemnitz (DE)
- Leipzig University (DE)
Publication Details
- Journal
- Journal of Cancer Research and Clinical Oncology
- Published
- 2025-10-04
- DOI
- https://doi.org/10.1007/s00432-025-06340-5
- Primary Topic
- Esophageal Cancer Research and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Deutsche Forschungsgemeinschaft
- Universität Leipzig