ArticleNPJ precision oncology2025
Machine learning model for predicting tertiary lymphoid structures and treatment response in triple-negative breast cancer.
Article in NPJ precision oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The translational chasm in machine learning for triple-negative breast cancer: a quantitative landscape assessment.Frontiers in oncology · 2026Pooled it
- B cells and humoral immunity in melanoma: regulatory and autoimmune-like features and implications for immunotherapy.Oncoimmunology · 2026Review
- Habitat imaging based on enhanced CT for predicting occult central lymph node metastasis in papillary thyroid carcinoma.European radiology · 2026Article
- Spatial habitat radiomics predicts tertiary lymphoid structure status and identifies an IDO1Journal for immunotherapy of cancer · 2026Article
- Explainable AI-Derived Spatial Pathological Features of Tumor, Necrosis, and Lymphocytes Identify Key Histological Signatures for Residual Cancer Burden Assessment in Breast Cancer.Diagnostics (Basel, Switzerland) · 2026Article
- TLS as Predictors and Targets in Neoadjuvant Chemoimmunotherapy for NSCLC.Thoracic cancer · 2026Review
- Association between intra- and peritumoral heterogeneity on B-mode ultrasound and overall survival in patients with liver metastases treated with immunotherapy.European radiology experimental · 2026Article
- Tertiary lymphoid structures in breast cancer: Mechanistic insights, subtype-specific patterns, and therapeutic implications.World journal of surgical oncology · 2026Review
- Therapeutic potential of tertiary lymphoid structures in breast cancer (Review).Molecular medicine reports · 2026Review
- Artificial intelligence integrated multi-omics and multimodal studies promote the efficacy of neoadjuvant chemotherapy in breast cancer: opportunities, challenges, and future perspectives.Breast cancer research : BCR · 2026Review
- Clinical and immunological significance of tertiary lymphoid structure maturation heterogeneity in brain metastases of lung adenocarcinoma.Journal of translational medicine · 2026Article
- Prediction of tumor-infiltrating lymphocytes through habitat radiomics and exploration of response mechanisms in neoadjuvant immunochemotherapy-treated lung cancer.Cancer immunology, immunotherapy : CII · 2026Article
- Tertiary lymphoid structures in neoadjuvant and perioperative cancer immunotherapy: a review and proposed framework for biomarker interpretation and validation.Frontiers in immunology · 2026Review
- Artificial intelligence for triple-negative breast cancer from imaging to multi-omics.Frontiers in oncology · 2026Review
Corrections and comments
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Authors and funding
13 authors.
Funding
Abstract
This study developed a machine learning model for predicting the presence of tertiary lymphoid structures (TLSs) and treatment response to neoadjuvant therapy (NAT) in triple-negative breast cancer (TNBC). This multicenter study retrospectively included 697 patients, including the training cohort (n = 137), the TLS validation cohort (n = 63) and the NAT response validation cohorts (n = 560). Five machine learning models were developed to predict the presence of TLSs, and the XGBoost model, which exhibited the best performance, was selected as the radiomics-based TLS (rTLS) predictive model. The rTLS predictive model demonstrated robust predictive performance, including across various patient subgroups. Prognostic analysis showed that the rTLS predictive score was significantly correlated with disease-free survival (DFS) in TNBC receiving NAT, and was identified as a strong independent prognostic factor. Pathomic features further explained the pathological heterogeneity of TNBC with different responses to NAT. Overall, the rTLS predictive model, which accurately predicted the presence of TLSs and treatment response to NAT in TNBC, held promise for future clinical application in formulating personalized strategies for TNBC, ultimately improving prognosis, aiding in individualized patient treatment.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.