Prediction Model of Lymph Node Metastasis in Cervical Cancer Based on MRI Habitat Radiomics.

Area of Science:

• Oncology
• Medical Imaging
• Radiology

Background:

• Radiomics offers non-invasive prediction of lymph node metastasis (LNM) in cervical cancer.
• Conventional whole-tumor analysis often misses intratumoral heterogeneity, limiting prediction accuracy.

Purpose of the Study:

• To develop and validate an MRI-based habitat radiomics model for preoperative prediction of pelvic LNM in early-stage cervical cancer.
• To assess the model's performance against clinical and conventional radiomics approaches.

Main Methods:

• Tumor regions were delineated on diffusion-weighted MRI.
• Intratumoral habitats were identified using K-means clustering.
• Radiomic features were extracted from whole tumors and habitat subregions, combined with clinical variables, and selected using correlation analysis and LASSO regression.

Main Results:

• The combined model (habitat radiomics + clinical factors) achieved the highest performance (AUC = 0.895) in internal validation.
• This outperformed the clinical (AUC = 0.799), conventional radiomics (AUC = 0.611), and habitat-only models (AUC = 0.872).
• Calibration and decision curve analyses confirmed good agreement and clinical utility.

Conclusions:

• Integrating habitat-based radiomics with clinical factors significantly enhances preoperative LNM prediction in cervical cancer.
• This approach provides a robust and clinically applicable tool for individualized patient management.
• Habitat radiomics effectively captures intratumoral heterogeneity for improved prognostic accuracy.