ArticleJournal of translational medicine2025
Intratumoral microbiota-aided fusion radiomics model for predicting tumor response to neoadjuvant chemoimmunotherapy in triple-negative breast cancer.
Article in Journal of translational medicine, 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
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Microbiome and Response to Therapy in Triple Negative Breast Cancer: A Systematic Review.Oncology research · 2026Pooled it
- Beyond pCR Prediction: Subtype-Specific Artificial Intelligence for Treatment Tailoring in Breast Cancer Neoadjuvant Therapy.Cancers · 2026Review
- Intratumoral microbiota exhibiting varied responses to neoadjuvant chemotherapy in triple-negative breast cancer revealed by 2bRAD-M.Journal of translational medicine · 2026Article
- 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
- Radiogenomic landscape of the hallmarks of cancer.Biomarker research · 2026Review
- Integrating tumor habitat heterogeneity with a hybrid deep learning architecture for ultrasound radiomics: a dual-center study on non-invasive prediction of PD-L1 expression in triple-negative breast cancer.Breast cancer research : BCR · 2026Article
- Article
- SPP1+ Macrophages and the Orchestration of Spatially Organized Immunosuppression in Cancer.Biomedicines · 2026Review
- Prediction of neoadjuvant therapy response in breast cancer based on interpretable artificial intelligence.International journal of surgery (London, England) · 2026Article
- Integrating Ultrasound and DCE-MRI Improves Accuracy in Differentiating Breast Adenosis from Carcinoma.Breast cancer (Dove Medical Press) · 2026Article
- Development and verification of a prognostic nomogram model for triple-negative breast cancer patients with neoadjuvant therapy.Frontiers in oncology · 2026Article
- The intratumoral microbiota in breast cancer: from basic research to clinical translation.Gut microbes · 2025Review
- Monitoring Pharmacological Treatment of Breast Cancer with MRI.Current issues in molecular biology · 2025Review
- Prediction of axillary lymph node metastasis in triple negative breast cancer using MRI radiomics and clinical features.Scientific reports · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
backgroundNeoadjuvant chemoimmunotherapy (NACI) has emerged as the standard treatment for early-stage triple-negative breast cancer (TNBC). However, reliable biomarkers for identifying patients who are likely to benefit from NACI are lacking. This study aims to develop an intratumoral microbiota-aided radiomics model for predicting pathological complete response (pCR) in patients with TNBC.
methodsIntratumoral microbiota are characterized by 16S rDNA sequencing and quantified through experimental assays. Single-cell RNA sequencing is performed to analyze the tumor microenvironment of tumors with various responses to NACI. Radiomics features are extracted from tumor regions on longitudinal magnetic resonance images (MRIs) scanned before and after NACI in the training set. On the basis of treatment response (pCR or non-pCR) and intratumoral microbiota scoring, we select key radiomics features and construct a fusion model integrating multi-timepoint (pre-NACI and post-NACI) MRI to predict the efficacy of immunotherapy, followed by independent external validation.
resultsA total of 124 patients are enrolled, with 88 in the training set and 36 in the validation set. Tumors from patients who achieves pCR present a significantly greater intratumoral microbiota load than tumors from patients who achieve non-pCR (p < 0.05). Additionally, tumors in non-pCR group exhibit greater infiltration of tumor-associated SPP1
conclusionsOur intratumoral microbiota-aided radiomics model could serve as a powerful and noninvasive tool for predicting the response of patients with early-stage TNBC to NACI.
Indexed as
Identifiers
What OpenQuestion holds
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.