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Prediction Model of Lymph Node Metastasis in Cervical Cancer Based on MRI Habitat Radiomics
1
The First Clinical Medical College, Lanzhou University, Lanzhou 730000, China
2
Department of Obstetrics and Gynecology, The First Hospital of Lanzhou University, Lanzhou 730000, China
3
Gansu Provincial Clinical Research Center for Gynecological Oncology, Lanzhou 730000, China
4
The First Clinical Medical College of Gansu University of Traditional Chinese Medicine, Lanzhou 730000, China
5
School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China
6
Gansu Province Clinical Research Renter for Radiology Imaging, The First Hospital of Lanzhou University, Lanzhou 730000, China
7
Intelligent Imaging Medical Engineering Research Center of Gansu Province, The First Hospital of Lanzhou University, Lanzhou 730000, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Cancers 2026, 18(1), 152; https://doi.org/10.3390/cancers18010152
Submission received: 24 November 2025
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Revised: 25 December 2025
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Accepted: 30 December 2025
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Published: 31 December 2025
(This article belongs to the Section Methods and Technologies Development)
Simple Summary
Lymph node metastasis is an important factor affecting treatment decisions and prognosis in patients with cervical cancer, but it is difficult to accurately assess before surgery using conventional imaging methods. In this study, we developed a new prediction model based on magnetic resonance imaging (MRI) radiomics that takes tumor heterogeneity into account. By dividing tumors into different intratumoral subregions (habitats) and combining imaging features with clinical information, we were able to more accurately predict pelvic lymph node metastasis in patients with early-stage cervical cancer. Our results show that this habitat-based radiomics model performs better than traditional clinical or whole-tumor radiomics models and may help clinicians better plan individualized treatment strategies before surgery.
Abstract
Background: Radiomics provides a non-invasive approach for predicting lymph node metastasis (LNM) in cervical cancer, but conventional whole-tumor analysis often overlooks intratumoral heterogeneity. Methods: This study aimed to develop and validate an MRI-based habitat radiomics model for preoperative prediction of pelvic LNM in early-stage cervical cancer. Tumor regions were delineated on diffusion-weighted imaging, and intratumoral habitats were generated using unsupervised 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. Four models—clinical, conventional radiomics, habitat radiomics, and combined—were constructed and evaluated. Results: In internal validation, the combined model achieved the best performance (AUC = 0.895), outperforming the clinical (AUC = 0.799), conventional radiomics (AUC = 0.611), and habitat models (AUC = 0.872). Calibration and decision curve analyses demonstrated good agreement and clinical utility. Conclusions: Integrating habitat-based radiomics with clinical factors significantly improves the preoperative prediction of LNM, providing a robust and clinically applicable tool for individualized management of cervical cancer patients.
