ReviewBritish journal of cancer2023
Artificial intelligence: opportunities and challenges in the clinical applications of triple-negative breast cancer.
Review in British journal of cancer, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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
19 citing papers in PubMed, 46 citations in OpenAlex.
- Multidimensional MRI radiomics-based model predicts recurrence risk in triple-negative breast cancer.NPJ precision oncology · 2026Article
- Enhancing cancer classification accuracy with a self-attention network using panel capture sequencing data.Briefings in bioinformatics · 2026Article
- Artificial Intelligence in Triple-Negative Breast Cancer: Applications in Diagnosis, Treatment Response, and Prognosis.Diagnostics (Basel, Switzerland) · 2026Review
- Artificial intelligence for triple-negative breast cancer from imaging to multi-omics.Frontiers in oncology · 2026Review
- Targeting intrinsically disordered nuclear protein 1 (NUPR1) with single-domain antibodies alleviates triple-negative breast cancer (TNBC) progression in vivo.Cell death & disease · 2025Article
- Artificial Intelligence for the Diagnosis and Management of Cancers: Potentials and Challenges.MedComm · 2025Review
- Bioinformatics Strategies in Breast Cancer Research.Biomolecules · 2025Review
- Full Field Digital Mammography Dataset from a Population Screening Program.Scientific data · 2025Article
- Inhibition of ATM enhances the immunogenicity of triple-negative breast cancer by promoting MHC-I expression.Cell death & disease · 2025Article
- Recent advances and challenges in colorectal cancer: From molecular research to treatment.World journal of gastroenterology · 2025Review
- Triple-Negative Breast Cancer on the Rise: Breakthroughs and Beyond.Breast cancer (Dove Medical Press) · 2025Review
- A Review on the Applications of Artificial Intelligence for Diagnosing and Treating Cervical Cancer.International journal of women's health · 2025Review
- Latest Therapeutical Approaches for Triple-Negative Breast Cancer: From Preclinical to Clinical Research.International journal of molecular sciences · 2024Review
- Review
- Empowering personalized pharmacogenomics with generative AI solutions.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Radiologic imaging biomarkers in triple-negative breast cancer: a literature review about the role of artificial intelligence and the way forward.BJR artificial intelligence · 2024Review
- Quantifying spatial CXCL9 distribution with image analysis predicts improved prognosis of triple-negative breast cancer.Frontiers in genetics · 2024Article
- Application of Novel Transcription Factor Machine Learning Model and Targeted Drug Combination Therapy Strategy in Triple Negative Breast Cancer.International journal of molecular sciences · 2023Article
- Molecular mechanisms and therapeutic applications of huaier in breast cancer treatment.Frontiers in pharmacology · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Triple-negative breast cancer (TNBC) accounts for 15-20% of all invasive breast cancer subtypes. Owing to its clinical characteristics, such as the lack of effective therapeutic targets, high invasiveness, and high recurrence rate, TNBC is difficult to treat and has a poor prognosis. Currently, with the accumulation of large amounts of medical data and the development of computing technology, artificial intelligence (AI), particularly machine learning, has been applied to various aspects of TNBC research, including early screening, diagnosis, identification of molecular subtypes, personalised treatment, and prediction of prognosis and treatment response. In this review, we discussed the general principles of artificial intelligence, summarised its main applications in the diagnosis and treatment of TNBC, and provided new ideas and theoretical basis for the clinical diagnosis and treatment of TNBC.
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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.