ArticleJournal of translational medicine2022
Radiomics features for assessing tumor-infiltrating lymphocytes correlate with molecular traits of triple-negative breast cancer.
Article in Journal of translational medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 1 of them a synthesis that pooled it.
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Who cites it
40 citing papers in PubMed, 1 synthesis or guideline pooled it, 64 citations in OpenAlex.
- Thematic trends and knowledge-map of tumor-infiltrating lymphocytes in breast cancer: a scientometric analysis.Frontiers in oncology · 2024Pooled it
- Deep interpretable radiogenomic workflow deciphers tumor microenvironment from breast MRI and identifies clinician-interpretable biomarkers.NPJ precision oncology · 2026Article
- Molecular subgroups and biomarker guided precision immunotherapy in triple-negative breast cancer: advances, challenges, and future directions.Frontiers in immunology · 2026Review
- Radiomics and Deep Learning: Bridging Breast Cancer Imaging Phenotypes and Genomic Heterogeneity.Breast cancer (Dove Medical Press) · 2026Review
- AI-based neoadjuvant immunotherapy response prediction across pan-cancer: a comprehensive review.Cancer cell international · 2025Review
- Predicting axillary lymph node metastasis in breast cancer patients using CNN-GCN on DCE-MRI: a multicenter study.BMC medical imaging · 2025Article
- Differentiating triple-negative breast cancer and atypical fibroadenomas using an ultrasound-based radiomics nomogram.BMC medical imaging · 2025Article
- Can MRI radiomics predict neck metastasis at initial diagnosis in patients with squamous cell carcinoma of the tongue?Oral radiology · 2025Article
- Tumor-Infiltrating Lymphocytes in Breast and Female Genital Tract Cancers: Overlooked Potential and Unexplored Frontiers.Cancer medicine · 2025Review
- Spatial-temporal radiogenomics in predicting neoadjuvant chemotherapy efficacy for breast cancer: a comprehensive review.Journal of translational medicine · 2025Review
- Role of Preoperative Breast MRI in Predicting Tumor-Infiltrating Lymphocytes in Breast Cancer: Is There an Association with Tumor Biological Subtypes?Biomedicines · 2025Article
- Computed tomography-based deep learning radiomics model for preoperative prediction of tumor immune microenvironment in colorectal cancer.World journal of gastrointestinal oncology · 2025Article
- Radiomics based on multiple machine learning methods for diagnosing early bone metastases not visible on CT images.Skeletal radiology · 2025Article
- Development and validation of a gradient boosting machine-based model for predicting tumor-infiltrating lymphocyte proportions in breast cancer.American journal of translational research · 2025Article
- The expression of epidermal growth factor receptor 2 and its relationship with tumor-infiltrating lymphocytes and clinical pathological features in breast cancer patients.Open life sciences · 2025Article
- Radiomics with structural magnetic resonance imaging, surface morphometry features, neurology scales, and clinical metrics to evaluate the neurodevelopment of preschool children with corrected tetralogy of Fallot.Translational pediatrics · 2024Article
- Radiomics in breast cancer: Current advances and future directions.Cell reports. Medicine · 2024Review
- Review
- Article
- A Multiparametric MRI-based Radiomics Model for Stratifying Postoperative Recurrence in Luminal B Breast Cancer.Journal of imaging informatics in medicine · 2024Article
Corrections and comments
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Authors and funding
9 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTumor-infiltrating lymphocytes (TILs) have become a promising biomarker for assessing tumor immune microenvironment and predicting immunotherapy response. However, the assessment of TILs relies on invasive pathological slides.
methodsWe retrospectively extracted radiomics features from magnetic resonance imaging (MRI) to develop a radiomic cohort of triple-negative breast cancer (TNBC) (n = 139), among which 116 patients underwent transcriptomic sequencing. This radiomic cohort was randomly divided into the training cohort (n = 98) and validation cohort (n = 41) to develop radiomic signatures to predict the level of TILs through a non-invasive method. Pathologically evaluated TILs in the H&E sections were set as the gold standard. Elastic net and logistic regression were utilized to perform radiomics feature selection and model training, respectively. Transcriptomics was utilized to infer the detailed composition of the tumor microenvironment and to validate the radiomic signatures.
resultsWe selected three radiomics features to develop a TILs-predicting radiomics model, which performed well in the validation cohort (AUC 0.790, 95% confidence interval (CI) 0.638-0.943). Further investigation with transcriptomics verified that tumors with high TILs predicted by radiomics (Rad-TILs) presented activated immune-related pathways, such as antigen processing and presentation, and immune checkpoints pathways. In addition, a hot immune microenvironment, including upregulated T cell infiltration gene signatures, cytokines, costimulators and major histocompatibility complexes (MHCs), as well as more CD8
conclusionsOur study demonstrated the feasibility of radiomics model in predicting TILs status and provided a method to make the features interpretable, which will pave the way toward precision medicine for TNBC.
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