ReviewCancers2023
Impact of Imaging Biomarkers and AI on Breast Cancer Management: A Brief Review.
Review in Cancers, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 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
17 citing papers in PubMed, 43 citations in OpenAlex.
- A review of application of Artificial Intelligence in breast cancer detection and treatment.Discover oncology · 2026Review
- Imaging Biomarkers in Radiotherapy.Cancers · 2026Review
- Lymph node disease in 2-deoxy-2-fluorodeoxyglucose positron emission tomography/computed tomography imaging: Advances in artificial intelligence-driven automatic segmentation and precise diagnosis.World journal of clinical oncology · 2025Review
- Article
- Targeting breast cancer: the promise of phage-based nanomedicines.Breast cancer research and treatment · 2025Review
- Exploring the Evolution of Breast Cancer Imaging: A Review of Conventional and Emerging Modalities.Cureus · 2025Review
- Unveiling the functions of five recently characterized lncRNAs in cancer progression.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2025Review
- Early and hereditary breast cancer: advances in risk stratification and imaging approaches.Therapeutic advances in medical oncology · 2025Review
- Ultrasound elastography features as promising predictors of pathological complete response in patients with HER2-positive breast cancer undergoing neoadjuvant therapy.American journal of translational research · 2025Article
- Ultrasound elastography: advances and challenges in early detection of breast cancer.Frontiers in oncology · 2025Review
- Exploring AI Approaches for Breast Cancer Detection and Diagnosis: A Review Article.Breast cancer (Dove Medical Press) · 2025Review
- Cross-modal deep learning model for predicting pathologic complete response to neoadjuvant chemotherapy in breast cancer.NPJ precision oncology · 2024Article
- Cancer research in the United Arab Emirates from birth to present: A bibliometric analysis.Heliyon · 2024Article
- A concentrated machine learning-based classification system for age-related macular degeneration (AMD) diagnosis using fundus images.Scientific reports · 2024Article
- An AI-based novel system for predicting respiratory support in COVID-19 patients through CT imaging analysis.Scientific reports · 2024Article
- Deep learning radiomics based on multimodal imaging for distinguishing benign and malignant breast tumours.Frontiers in medicine · 2024Article
- Artificial intelligence and machine learning for joint disorder detection: Promising advances in diagnostics.International journal of health sciencesArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Breast cancer stands out as the most frequently identified malignancy, ranking as the fifth leading cause of global cancer-related deaths. The American College of Radiology (ACR) introduced the Breast Imaging Reporting and Data System (BI-RADS) as a standard terminology facilitating communication between radiologists and clinicians; however, an update is now imperative to encompass the latest imaging modalities developed subsequent to the 5th edition of BI-RADS. Within this review article, we provide a concise history of BI-RADS, delve into advanced mammography techniques, ultrasonography (US), magnetic resonance imaging (MRI), PET/CT images, and microwave breast imaging, and subsequently furnish comprehensive, updated insights into Molecular Breast Imaging (MBI), diagnostic imaging biomarkers, and the assessment of treatment responses. This endeavor aims to enhance radiologists' proficiency in catering to the personalized needs of breast cancer patients. Lastly, we explore the augmented benefits of artificial intelligence (AI), machine learning (ML), and deep learning (DL) applications in segmenting, detecting, and diagnosing breast cancer, as well as the early prediction of the response of tumors to neoadjuvant chemotherapy (NAC). By assimilating state-of-the-art computer algorithms capable of deciphering intricate imaging data and aiding radiologists in rendering precise and effective diagnoses, AI has profoundly revolutionized the landscape of breast cancer radiology. Its vast potential holds the promise of bolstering radiologists' capabilities and ameliorating patient outcomes in the realm of breast cancer management.
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.