Predicting lymph node metastasis in early cervical cancer using spatial features at perfusion habitat Imaging based on DCE-MRI

Motivation: Cervical cancer has significant spatial heterogeneity, resulting in tumor recurrence and metastasis. The exploration of tumor spatial features may be valuable for predicting lymph node metastasis in cervical cancer. Goal(s): Combined with landscape ecological analysis and DCE-MRI construction of blood perfusion landscape to predict lymph node metastasis of early cervical cancer. Approach: Based on DCE-MRI pharmacokinetic parameter map, perfusion habitat imaging was constructed, and landscape ecological analysis was introduced to extract the spatial features of habitat imaging. Results: The spatial heterogeneity features of blood perfusion obtained by landscape analysis can predict lymph node metastasis of early cervical cancer. Impact: In this study, we innovatively introduced landscape analysis method to obtain the spatial heterogeneity features of blood perfusion, which demonstrated good performance for predicting lymph node metastasis of early cervical cancer.