ArticleJournal of cancer research and clinical oncology2025
Artificial neural networks as a prognostic tool using hyperspectral imaging on pretherapeutic histopathological specimens of esophageal adenocarcinoma.
Article in Journal of cancer research and clinical oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
purposeThe integration of artificial intelligence (AI) with hyperspectral imaging (HSI) offers a promising avenue for improving pre-therapeutic prognosis, a key factor in optimizing cancer treatment strategies. This study explores the potential of artificial neural networks (ANNs) to predict the effectiveness of preoperative chemo- or radiochemotherapy in esophageal adenocarcinoma (EAC), using HSI data derived from histopathological tissue samples.
methodsHSI data were obtained from pre-therapeutic histopathological samples of 21 patients with EAC. Following annotation and spectral extraction, the data underwent pre-processing steps including normalization, shuffling, and batch organization. Three artificial neural network (ANN) models-2D convolutional neural networks (2D-CNNs), 3D convolutional neural networks (3D-CNNs), and Hybrid-Spectral Networks (Hybrid-SN)-were trained to predict treatment response. Model performance was assessed using sensitivity, specificity, accuracy, and F1-score, offering insights into their clinical utility
resultsThe 3D-CNN model achieved the highest accuracy (0.68 ± 0.09) and F1-score (0.66 ± 0.08), highlighting its strength in capturing both spatial and spectral information. The Hybrid-SN model demonstrated the highest sensitivity (0.79 ± 0.19), indicating strong performance in identifying responders to neoadjuvant therapy. In contrast, the 2D-CNN model achieved the highest specificity (0.73 ± 0.15), reflecting its effectiveness in correctly identifying non-responders.
conclusionThis 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.
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