Habitat radiomics predicts occult lymph node metastasis and uncovers immune microenvironment of head and neck cancer

作者：Xinwei Chen, Huan Jiang, Min Pan, Chengmin Feng, Yanshi Li, Lin Chen, Yuxi Luo, Long Liu, Juan Peng, Guohua Hu · 发表于：Journal of Translational Medicine · 年份：2025 · DOI：10.1186/s12967-025-06474-7 · 被引用次数：19 · 研究领域：Radiomics and Machine Learning in Medical Imaging、Head and Neck Cancer Studies、Esophageal Cancer Research and Treatment

BACKGROUND: Occult lymph node metastasis (LNM) is a key prognostic factor for patients with head and neck squamous cell carcinoma (HNSCC). This study was to establish radiomics models derived from intratumoral, peritumoral, and habitat regions for identifying occult LNM in HNSCC. METHODS: Patients with pathologically confirmed HNSCC from three medical Centers (from March 2014 to April 2024) and The Cancer Genome Atlas (TCGA) were enrolled. Center 1 was split into training (n = 330) and internal test sets (n = 154), while Center 2 and Center 3 served as the external test set (n = 183). Genomic set (n = 50) from TCGA and single-cell RNA sequencing set (n = 6) from Center 1 were used for biological analysis. We used the intratumoral, peritumoral, and habitat volumes of interest (VOIs) to extract radiomics features, respectively. Based on Logistic Regression (LR), Support Vector Machine (SVM), and Random Forest (RF) classifiers, nine radiomics models were built to confirm the optimal predictive performance. The best-performing model, along with clinical-radiologic data, was combined to develop a hybrid model. The log-rank test was used to evaluate the model's prognostic performance. Additionally, bulk and single-cell RNA sequencing were applied for investigating the biological mechanisms underlying the optimal model. RESULTS: The RF-habitat radiomics model showed the best performance, achieving AUCs of 0.835-0.919 across all datasets. Survival analysis further confirmed the progn...