ReviewJournal of imaging2020
Applications of Computational Methods in Biomedical Breast Cancer Imaging Diagnostics: A Review.
Review in Journal of imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 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
14 citing papers in PubMed, 44 citations in OpenAlex.
- Roles of artificial intelligence and high frame-rate contrast-enhanced ultrasound in the differential diagnosis of Breast Imaging Reporting and Data System 4 breast nodules.Gland surgery · 2025Article
- A Network Pharmacology Identified Metastasis Target for Oral Squamous Cell Carcinoma Originating from Breast Cancer with a Potential Inhibitor fromPharmaceuticals (Basel, Switzerland) · 2024Article
- Conditional generative adversarial network driven radiomic prediction of mutation status based on magnetic resonance imaging of breast cancer.Journal of translational medicine · 2024Article
- Urinary VOCs as biomarkers of early stage lung tumour development in mice.Cancer biomarkers : section A of Disease markers · 2024Article
- A machine learning approach towards assessing consistency and reproducibility: an application to graft survival across three kidney transplantation eras.Frontiers in digital health · 2024Article
- Article
- Secondary Breast Malignancy from Renal Cell Carcinoma: Challenges in Diagnosis and Treatment-Case Report.Diagnostics (Basel, Switzerland) · 2023Article
- Delineating intra-tumoral heterogeneity and tumor evolution in breast cancer using precision-based approaches.Frontiers in genetics · 2023Review
- Artificial Intelligence (AI) in Breast Imaging: A Scientometric Umbrella Review.Diagnostics (Basel, Switzerland) · 2022Review
- A Machine Learning Method with Filter-Based Feature Selection for Improved Prediction of Chronic Kidney Disease.Bioengineering (Basel, Switzerland) · 2022Article
- COVID-19 Diagnosis: A Review of Rapid Antigen, RT-PCR and Artificial Intelligence Methods.Bioengineering (Basel, Switzerland) · 2022Review
- LINC01140 Targeting miR-452-5p/RGS2 Pathway to Attenuate Breast Cancer Tumorigenesis.Disease markers · 2022Article
- Deep Learning in Medical Image Analysis.Journal of imaging · 2021Article
- The computational model lifecycle: Opportunities and challenges for computational medicine in the healthcare ecosystem.Science progressReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the exponential increase in new cases coupled with an increased mortality rate, cancer has ranked as the second most prevalent cause of death in the world. Early detection is paramount for suitable diagnosis and effective treatment of different kinds of cancers, but this is limited to the accuracy and sensitivity of available diagnostic imaging methods. Breast cancer is the most widely diagnosed cancer among women across the globe with a high percentage of total cancer deaths requiring an intensive, accurate, and sensitive imaging approach. Indeed, it is treatable when detected at an early stage. Hence, the use of state of the art computational approaches has been proposed as a potential alternative approach for the design and development of novel diagnostic imaging methods for breast cancer. Thus, this review provides a concise overview of past and present conventional diagnostics approaches in breast cancer detection. Further, we gave an account of several computational models (machine learning, deep learning, and robotics), which have been developed and can serve as alternative techniques for breast cancer diagnostics imaging. This review will be helpful to academia, medical practitioners, and others for further study in this area to improve the biomedical breast cancer imaging diagnosis.
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.