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

Abstract 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 Pat...

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Main Authors: Xinwei Chen, Huan Jiang, Min Pan, Chengmin Feng, Yanshi Li, Lin Chen, Yuxi Luo, Long Liu, Juan Peng, Guohua Hu
Format: Article
Language:English
Published: BMC 2025-05-01
Series:Journal of Translational Medicine
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Online Access:https://doi.org/10.1186/s12967-025-06474-7
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author Xinwei Chen
Huan Jiang
Min Pan
Chengmin Feng
Yanshi Li
Lin Chen
Yuxi Luo
Long Liu
Juan Peng
Guohua Hu
author_facet Xinwei Chen
Huan Jiang
Min Pan
Chengmin Feng
Yanshi Li
Lin Chen
Yuxi Luo
Long Liu
Juan Peng
Guohua Hu
author_sort Xinwei Chen
collection DOAJ
description Abstract 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 prognostic value of the RF-habitat radiomics model. The RF-habitat radiomics model and the hybrid model notably surpassed the clinical model in predictive performance. Moreover, the RF-habitat radiomics model was associated with the abundance level of exhaustion-associated CD8 + T cells, uncovering the immune microenvironment characteristics contributing to occult LNM in HNSCC. Conclusions The RF-habitat radiomics model demonstrated excellent performance for predicting occult LNM in HNSCC across three cohorts, providing a non-invasive solution for occult LNM. Furthermore, radiogenomic analysis further revealed the biological associations of the model, primarily related to T cell dysfunction.
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spelling doaj-art-bdb9dcf9a2874d869e114c8eb5896f682025-08-20T02:10:49ZengBMCJournal of Translational Medicine1479-58762025-05-0123111710.1186/s12967-025-06474-7Habitat radiomics predicts occult lymph node metastasis and uncovers immune microenvironment of head and neck cancerXinwei Chen0Huan Jiang1Min Pan2Chengmin Feng3Yanshi Li4Lin Chen5Yuxi Luo6Long Liu7Juan Peng8Guohua Hu9Department of Otorhinolaryngology, The First Affiliated Hospital of Chongqing Medical UniversityDepartment of Radiology, The First Affiliated Hospital of Chongqing Medical UniversityDepartment of Otorhinolaryngology, The First Affiliated Hospital of Chongqing Medical UniversityDepartment of Otorhinolaryngology, The First Affiliated Hospital of Chongqing Medical UniversityDepartment of Otorhinolaryngology, The First Affiliated Hospital of Chongqing Medical UniversityDepartment of Otorhinolaryngology, The First Affiliated Hospital of Chongqing Medical UniversityDepartment of Radiology, Zigong Fourth People’s HospitalDepartment of Radiology, The People’s Hospital of HechuanDepartment of Radiology, The First Affiliated Hospital of Chongqing Medical UniversityDepartment of Otorhinolaryngology, The First Affiliated Hospital of Chongqing Medical UniversityAbstract 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 prognostic value of the RF-habitat radiomics model. The RF-habitat radiomics model and the hybrid model notably surpassed the clinical model in predictive performance. Moreover, the RF-habitat radiomics model was associated with the abundance level of exhaustion-associated CD8 + T cells, uncovering the immune microenvironment characteristics contributing to occult LNM in HNSCC. Conclusions The RF-habitat radiomics model demonstrated excellent performance for predicting occult LNM in HNSCC across three cohorts, providing a non-invasive solution for occult LNM. Furthermore, radiogenomic analysis further revealed the biological associations of the model, primarily related to T cell dysfunction.https://doi.org/10.1186/s12967-025-06474-7Head and neck squamous cell carcinomaRadiogenomicsHabitat radiomicsOccult lymph node metastasisImmune microenvironment
spellingShingle Xinwei Chen
Huan Jiang
Min Pan
Chengmin Feng
Yanshi Li
Lin Chen
Yuxi Luo
Long Liu
Juan Peng
Guohua Hu
Habitat radiomics predicts occult lymph node metastasis and uncovers immune microenvironment of head and neck cancer
Journal of Translational Medicine
Head and neck squamous cell carcinoma
Radiogenomics
Habitat radiomics
Occult lymph node metastasis
Immune microenvironment
title Habitat radiomics predicts occult lymph node metastasis and uncovers immune microenvironment of head and neck cancer
title_full Habitat radiomics predicts occult lymph node metastasis and uncovers immune microenvironment of head and neck cancer
title_fullStr Habitat radiomics predicts occult lymph node metastasis and uncovers immune microenvironment of head and neck cancer
title_full_unstemmed Habitat radiomics predicts occult lymph node metastasis and uncovers immune microenvironment of head and neck cancer
title_short Habitat radiomics predicts occult lymph node metastasis and uncovers immune microenvironment of head and neck cancer
title_sort habitat radiomics predicts occult lymph node metastasis and uncovers immune microenvironment of head and neck cancer
topic Head and neck squamous cell carcinoma
Radiogenomics
Habitat radiomics
Occult lymph node metastasis
Immune microenvironment
url https://doi.org/10.1186/s12967-025-06474-7
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