ArticleClinical physiology and functional imaging2025
Deep learning-based 3D classification of head and neck cancer PET/MRI: Radiologist comparison and Grad-CAM interpretability.
Article in Clinical physiology and functional imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Deep learning-based 3D classification of head and neck cancer PET/MRI: Radiologist comparison and Grad-CAM interpretability.Clinical physiology and functional imaging · 2025Article
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7 authors.
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Abstract
purposeTo develop and evaluate a three-dimensional convolutional neural network for automated classification of PET/MRI images in head and neck cancer (HNC) patients, assessing its performance against radiologist interpretation and its potential as a diagnostic aid.
methodsData from 202 patients with HNC who underwent
resultsThe PET-based model achieved an AUC of 0.92 on the test set, with an accuracy of 90%, a sensitivity of 100% and a specificity of 80%. PET/MRI and MRI-based models underperformed relative to the PET-based model. The radiologist achieved perfect classification accuracy. Analysis of Grad-CAM showed that the model classifications are based on real areas of interest. In addition, it gave valuable insight into using similar systems in identifying false positive findings.
conclusionThe PET-based model demonstrated high sensitivity, indicating its potential as a pre-screening tool for HNC. However, specificity requires improvement to reduce false-positive rates. Enhanced datasets and refinement of model architecture will be crucial before clinical adoption. Grad-CAM provides valuable insights into model decisions, aiding clinical integration.
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