ArticleCancers2021
Explainable Artificial Intelligence Reveals Novel Insight into Tumor Microenvironment Conditions Linked with Better Prognosis in Patients with Breast Cancer.
Article in Cancers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 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
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Predictive value of radiomics-based machine learning for the disease-free survival in breast cancer: a systematic review and meta-analysis.Frontiers in oncology · 2023Pooled it
- Artificial intelligence for triple-negative breast cancer from imaging to multi-omics.Frontiers in oncology · 2026Review
- Application of Artificial Intelligence in Stem Cells and Gene Therapy for Gynecological Cancers.Current stem cell research & therapy · 2026Review
- The role of AI for improved management of breast cancer: Enhanced diagnosis and health disparity mitigation.Computer methods and programs in biomedicine · 2025Review
- Development and validation of a machine learning-driven mitochondrial gene signature for the diagnosis of breast cancer.Frontiers in immunology · 2025Article
- The role of artificial intelligence in enhancing breast cancer screening and diagnosis: A review of current advances.BioImpacts : BI · 2025Review
- Explainable AI-driven depression detection from social media using natural language processing and black box machine learning models.Frontiers in artificial intelligence · 2025Article
- Enhancing Tumor Targeted Therapy: The Role of iRGD Peptide in Advanced Drug Delivery Systems.Cancers · 2024Review
- Discovering novel prognostic biomarkers of hepatocellular carcinoma using eXplainable Artificial Intelligence.Expert systems with applications · 2024Article
- Explainable artificial intelligence in breast cancer detection and risk prediction: A systematic scoping review.Cancer innovation · 2024Article
- Immune, metabolic landscapes of prognostic signatures for lung adenocarcinoma based on a novel deep learning framework.Scientific reports · 2024Article
- Cancer Metastasis Prediction and Genomic Biomarker Identification through Machine Learning and eXplainable Artificial Intelligence in Breast Cancer Research.Diagnostics (Basel, Switzerland) · 2023Article
- Non-Coding RNAs: Foes or Friends for Targeting Tumor Microenvironment.Non-coding RNA · 2023Review
- Human Activity Recognition with an HMM-Based Generative Model.Sensors (Basel, Switzerland) · 2023Article
- Analyzing breast cancer invasive disease event classification through explainable artificial intelligence.Frontiers in medicine · 2023Article
- Utilization of model-agnostic explainable artificial intelligence frameworks in oncology: a narrative review.Translational cancer research · 2022Review
- Factors Predicting Surgical Effort Using Explainable Artificial Intelligence in Advanced Stage Epithelial Ovarian Cancer.Cancers · 2022Article
- Mapping intellectual structures and research hotspots in the application of artificial intelligence in cancer: A bibliometric analysis.Frontiers in oncology · 2022Article
- Profiling hearing aid users through big data explainable artificial intelligence techniques.Frontiers in neurology · 2022Article
- Expression, prognosis value, and immune infiltration of lncRNA ASB16-AS1 identified by pan-cancer analysis.Bioengineered · 2021Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We investigated the data-driven relationship between immune cell composition in the tumor microenvironment (TME) and the ≥5-year survival rates of breast cancer patients using explainable artificial intelligence (XAI) models. We acquired TCGA breast invasive carcinoma data from the cbioPortal and retrieved immune cell composition estimates from bulk RNA sequencing data from TIMER2.0 based on EPIC, CIBERSORT, TIMER, and xCell computational methods. Novel insights derived from our XAI model showed that B cells, CD8
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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.