ReviewBioMed research international2018
A New Challenge for Radiologists: Radiomics in Breast Cancer.
Review in BioMed research international, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers.
What it found
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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.
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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.
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Who cites it
47 citing papers in PubMed.
- Machine learning-based radiomics nomograms to predict number of fields in postoperative IMRT for breast cancer.Journal of applied clinical medical physics · 2024Trial
- Relationship between radiosensitivity of peripheral blood lymphocytes, breast cancer cells, and breast cancer stem cells in patients with breast cancer.Biochemistry and biophysics reports · 2026Article
- Multimodal Imaging-Based Prediction of Pathological Complete Response After Neoadjuvant Chemotherapy for Breast Cancer.Journal of imaging informatics in medicine · 2026Article
- Breast cancer risk assessment for screening: a hybrid artificial intelligence approach.European radiology · 2026Article
- Creation and Verification of a Machine Learning-based Model for Predicting Breast Cancer Risk in Patients with BI-RADS 4 Breast Lesions.Current medical imaging · 2026Article
- A CT-based radiomics model for predicting pain relief after radiotherapy in patients with bone metastases: a dual-center study.Frontiers in oncology · 2026Article
- Multimodal feature fusion model for breast mass malignant risk stratification.Frontiers in oncology · 2026Article
- Article
- The role of artificial intelligence in enhancing breast cancer screening and diagnosis: A review of current advances.BioImpacts : BI · 2025Review
- An updated overview of radiomics-based artificial intelligence (AI) methods in breast cancer screening and diagnosis.Radiological physics and technology · 2024Review
- Role of Radiology in the Diagnosis and Treatment of Breast Cancer in Women: A Comprehensive Review.Cureus · 2024Review
- Prediction of heart failure and all-cause mortality using cardiac ultrasomics in patients with breast cancer.The international journal of cardiovascular imaging · 2024Article
- Differentiation of invasive ductal and lobular carcinoma of the breast using MRI radiomic features: a pilot study.F1000Research · 2024Article
- The use of radiomics in magnetic resonance imaging for the pre-treatment characterisation of breast cancers: A scoping review.Journal of medical radiation sciences · 2023Article
- A Radiomics-Based Model for Potentially More Accurate Identification of Subtypes of Breast Cancer Brain Metastases.Yonsei medical journal · 2023Article
- Adversarial Attack and Defense in Breast Cancer Deep Learning Systems.Bioengineering (Basel, Switzerland) · 2023Article
- A Radiomics Study: Classification of Breast Lesions by Textural Features from Mammography Images.Journal of digital imaging · 2023Article
- The utilization of artificial intelligence applications to improve breast cancer detection and prognosis.Saudi medical journal · 2023Review
- Breast cancer heterogeneity and its implication in personalized precision therapy.Experimental hematology & oncology · 2023Review
- An integrated radiomics nomogram based on conventional ultrasound improves discriminability between fibroadenoma and pure mucinous carcinoma in breast.Frontiers in oncology · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionOver the last decade, the field of medical imaging experienced an exponential growth, leading to the development of radiomics, with which innumerable quantitative features are obtained from digital medical images, providing a comprehensive characterization of the tumor. This review aims to assess the role of this emerging diagnostic tool in breast cancer, focusing on the ability of radiomics to predict malignancy, response to neoadjuvant chemotherapy, prognostic factors, molecular subtypes, and risk of recurrence. EVIDENCE ACQUISITION: A literature search on PubMed and on Cochrane database websites to retrieve English-written systematic reviews, review articles, meta-analyses, and randomized clinical trials published from August 2013 up to July 2018 was carried out.
resultsTwenty papers (19 retrospective and 1 prospective studies) conducted with different conventional imaging modalities were included. DISCUSSION: The integration of quantitative information with clinical, histological, and genomic data could enable clinicians to provide personalized treatments for breast cancer patients. Current limitations of a routinely application of radiomics are represented by the limited knowledge of its basics concepts among radiologists and by the lack of efficient and standardized systems of feature extraction and data sharing.
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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.