Pilot Study on Contrast-Enhanced CT-Based 3D Cascaded Segmentation of Hypopharyngeal Cancer and the Stability of the...

Hypopharyngeal cancer (HPC), a malignant head and neck tumor with poor prognosis, requires accurate pretreatment segmentation for surgical planning and radiomics analysis. However, no prior research has reported automated segmentation of hypopharyngeal cancer on CECT images or investigated the impact of such segmentation on radiomics features. This study developed a 3D cascaded HPCNet framework for automated HPC segmentation on CECT images and evaluated the stability of extracted radiomics features. Pretreatment CECT images of 180 patients with HPC were retrospectively acquired, and a 5-fold cross-validation strategy was adopted for dataset division. The cascaded HPCNet framework significantly outperformed the nnUNet in the segmentation of HPC, achieving the highest median DSC (0.793 vs. 0.768, p $<$ 0.05), Jaccard index (0.650 vs. 0.635, p $<$ 0.05), precision (0.808 vs. 0.797, p $<$ 0.05), and recall (0.795 vs. 0.778, p $<$ 0.05), particularly when the tumor volume was $\le$ 10 $c{m}^3$ (DSC: 0.796 vs. 0.762, p $<$ 0.05). The maximum tumor diameter predicted by the cascaded HPCNet framework was highly correlated with the maximum tumor diameter delineated manually (p $<$ 0.0001). Five of the seven extracted radiomics feature groups exhibited high correlation, with ICC $>$ 0.80. In conclusion, the proposed cascaded HPCNet framework can achieve high-precision automatic segmentation of HPC on CECT images, and the extracted radiomics features exhibit a high level of stability.