文献类型：期刊文献

英文题名：Prediction Model of Lymph Node Metastasis in Cervical Cancer Based on MRI Habitat Radiomics

作者：Wang, Mei[1,2,3];Cao, Yu[1];Zhang, Weiwei[4];Liang, Yun[1];Liu, Jizhao[5];Lei, Junqiang[1,6,7]

第一作者：Wang, Mei

通信作者：Lei, JQ[1];Lei, JQ[2];Lei, JQ[3]

机构：[1]Lanzhou Univ, Clin Med Coll 1, Lanzhou 730000, Peoples R China;[2]Lanzhou Univ, Dept Obstet & Gynecol, Hosp 1, Lanzhou 730000, Peoples R China;[3]Gansu Prov Clin Res Ctr Gynecol Oncol, Lanzhou 730000, Peoples R China;[4]Gansu Univ Tradit Chinese Med, Clin Med Coll 1, Lanzhou 730000, Peoples R China;[5]Lanzhou Univ, Sch Informat Sci & Engn, Lanzhou 730000, Peoples R China;[6]Lanzhou Univ, Gansu Prov Clin Res Renter Radiol Imaging, Hosp 1, Lanzhou 730000, Peoples R China;[7]Lanzhou Univ, Intelligent Imaging Med Engn Res Ctr Gansu Prov, Hosp 1, Lanzhou 730000, Peoples R China

第一机构：Lanzhou Univ, Clin Med Coll 1, Lanzhou 730000, Peoples R China

通信机构：[1]corresponding author), Lanzhou Univ, Clin Med Coll 1, Lanzhou 730000, Peoples R China;[2]corresponding author), Lanzhou Univ, Gansu Prov Clin Res Renter Radiol Imaging, Hosp 1, Lanzhou 730000, Peoples R China;[3]corresponding author), Lanzhou Univ, Intelligent Imaging Med Engn Res Ctr Gansu Prov, Hosp 1, Lanzhou 730000, Peoples R China.

年份：2025

卷号：18

期号：1

外文期刊名：CANCERS

收录：;WOS:【SCI-EXPANDED(收录号:WOS:001657224700001)】；

基金：This study was supported by the Natural Science Foundation of Gansu Province (Grant No. 23JRRA1721); Youth Doctoral Support Project of Gansu Provincial Department of Education (Grant No. 2023QB-093); Lanzhou Science and Technology Plan Project (Grant No. 2023-2-86).

语种：英文

外文关键词：cervical cancer; radiomics; habitat radiomics; machine learning; feature engineering

摘要：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.