文献类型：期刊文献

英文题名：An interpretable machine learning model combining MRI-DKI habitat radiomic features and clinical biomarkers for noninvasive prediction of lymphatic metastasis in rectal cancer: a prospective study

作者：Peng, Leping[1];Li, Feixiang[1];Zhang, Fan[1];Ma, Fang[1];Zhang, Xiuling[1];Zhang, Xiaoyue[2];Chen, Dongdong[3];Huang, Gang[4];Wang, Lili[4]

第一作者：Peng, Leping

通信作者：Huang, G[1];Wang, LL[1]

机构：[1]Gansu Univ Chinese Med, Lanzhou, Peoples R China;[2]Philips Healthcare, Dept Clin & Tech Support, Xian, Peoples R China;[3]Gansu Prov Hosp, Gastrointestinal Ward, Gen Surg Dept, Lanzhou, Peoples R China;[4]Gansu Prov Hosp, Dept Radiol, Lanzhou, Peoples R China

第一机构：甘肃中医药大学

通信机构：[1]corresponding author), Gansu Prov Hosp, Dept Radiol, Lanzhou, Peoples R China.

年份：2026

卷号：17

期号：1

外文期刊名：INSIGHTS INTO IMAGING

收录：;Scopus(收录号：2-s2.0-105033657881);WOS:【SCI-EXPANDED(收录号:WOS:001723046300001)】；

基金：The study was supported by Gansu Provincial Youth Fund Project (No. 20JR5RA143), the Gansu Provincial Hospital Research Fund Project (No. 23GSSYF-4 and No. 21GSSYB-4), and the Gansu Provincial Department of Education: Graduate Student "Innovation and Entrepreneurship" Project of Gansu University of Chinese Medicine, 2025CXCY-071.

语种：英文

外文关键词：Rectal cancer; Diffusion kurtosis imaging; Radiomics; Habitat; Lymphatic metastasis

摘要：ObjectiveTumor heterogeneity exerts a significant influence on lymphovascular invasion (LVI) and lymph node metastasis (LNM) in rectal cancer (RC), thereby affecting patient treatment outcomes and prognosis. This study aims to develop a combined model integrating diffusion kurtosis imaging (DKI) based habitat radiomic features with clinical immune-inflammatory biomarkers to predict lymphatic metastatic risk in RC.Materials and methodsThis prospective study included 151 pathologically confirmed patients with rectal adenocarcinoma who underwent preoperative MRI (training cohort: 105 cases; testing cohort: 46 cases). Two radiologists manually delineated the whole-tumor VOI slice by slice on the mean diffusivity (MD) maps using ITK-SNAP software, and the VOIs were subsequently mapped onto the mean kurtosis (MK) maps. K-means clustering was applied for subregion segmentation. Predictive models for LVI and LNM were built using the Random Forest and Extra Trees algorithms, respectively. The Shapley additive explanation method was used to quantify the contribution of each feature to the decision-making of the combined model (Model 3).ResultsLogistic regression analysis demonstrated NHR and EMVI as independent predictors of LVI, while BMI, CA19-9, PNI, and EMVI were independent predictors of LNM. Model 3, which integrated clinical immune-inflammatory biomarkers, conventional radiomic features, and habitat radiomic features, demonstrated the best performance. The AUCs for predicting LVI and LNM were 0.937 vs. 0.864 and 0.901 vs. 0.947 in the training and testing cohorts, respectively.ConclusionThe habitat radiomics score is a novel and robust quantitative biomarker. Model 3 has demonstrated good performance in assessing the risk of lymphatic metastasis of RC.Critical relevance statementHabitat radiomics features derived from DKI parameter maps, combined with clinical immune-inflammatory biomarkers, can predict the risk of lymphatic metastasis of RC, potentially complementing biopsy-based identification of high-risk regions and advancing risk stratification for clinical decision-making in RC management.Key PointsAccurate assessment of lymphatic metastasis risk in rectal cancer is crucial for clinical decision-making and personalized treatment optimization.Diffusion kurtosis imaging-derived parameters and habitat radiomic features can quantify and characterize intratumoral heterogeneity.The combined model provides higher predictive performance for LVI and LNM in rectal cancer.