Scholarlink

Purpose <p>The 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.</p> Methods <p>HSI 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</p> Results <p>The 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.</p> Conclusion <p>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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Artificial neural networks as a prognostic tool using hyperspectral imaging on pretherapeutic histopathological specimens of esophageal adenocarcinoma

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月4日

查看全文
  • Christel Teresa Trifone,
  • Marianne Maktabi,
  • Philipp Bischoff,
  • Katrin Schierle,
  • Stefan Niebisch,
  • Yusef Moulla,
  • Patrick Sven Plum,
  • Boris Jansen-Winkeln,
  • Ines Gockel,
  • René Thieme

摘要

Purpose

The 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.

Methods

HSI 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

Results

The 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.

Conclusion

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.