Benchmarking deep learning models for laryngeal cancer staging using the LaryngealCT dataset.
Authors
Affiliations (5)
Affiliations (5)
- School of Information Technology, Deakin University, Burwood, VIC, Australia.
- Department of Head and Neck Surgery, Kasturba Medical College, Manipal Academy of Higher Education, Manipal, India.
- Department of Head and Neck Surgery, Kasturba Medical College, Manipal Academy of Higher Education, Manipal, India. [email protected].
- Department of Radiodiagnosis and Imaging, Kasturba Medical College, Manipal Academy of Higher Education, Manipal, India.
- Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India.
Abstract
Laryngeal cancer imaging research lacks standardised public datasets to enable reproducible deep learning (DL) model development. We present LaryngealCT, a curated benchmark of 1,029 computed tomography (CT) scans aggregated from six collections from The Cancer Imaging Archive (TCIA). Uniform 1 mm isotropic volumes of interest encompassing the larynx were extracted using a weakly supervised parameter search framework validated by clinical experts. Six 3D DL architectures (custom 3D CNN, ResNet18/50/101, DenseNet121 and MedicalNet-pretrained ResNet50) were benchmarked on (i) early (Tis-T2) vs. advanced (T3-T4) and (ii) T4 vs. non-T4 classification tasks. On the independent test set, the 3D CNN achieved the strongest overall performance across global and per-class metrics (Accuracy = 0.854, F1-macro = 0.841) in early vs. advanced classification. In the T4 task, AU-ROC values exceeded 0.82 for most models, but sensitivity for T4 disease remained limited (≤ 0.412), with ResNet101 showing the most promising calibrated T4 recall (0.706). Model explainability assessed using GradCAM ++ with thyroid cartilage overlays for the T4 classification task revealed anatomically plausible peri-cartilage activations although spatial overlap remained modest. Through open-source data, pretrained models, and integrated explainability tools, LaryngealCT offers a reproducible foundation for AI-driven research to support future clinical decision-making in laryngeal oncology.