ReviewLa Radiologia medica2022
A narrative review on current imaging applications of artificial intelligence and radiomics in oncology: focus on the three most common cancers.
Review in La Radiologia medica, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 65 papers, 3 of them syntheses 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
65 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Radiomics and artificial intelligence-based prediction of tumor response in digestive system neoplasm: a systematic review and meta-analysis.Frontiers in medicine · 2026Pooled it
- Predictive Performance of Radiomics-Based Machine Learning for Colorectal Cancer Recurrence Risk: Systematic Review and Meta-Analysis.JMIR medical informatics · 2025Pooled it
- Delta radiomics: an updated systematic review.La Radiologia medica · 2024Pooled it
- CT Radiomics Combined With Clinicopathological Features to Predict Invasive Mucinous Adenocarcinoma in Patients With Lung Adenocarcinoma.Technology in cancer research & treatmentTrial
- Review
- Challenges in clinical translation of artificial intelligence and real-time imaging navigation in radical gastrectomy.World journal of gastroenterology · 2025Review
- An integrated strategy based on radiomics and quantum machine learning: diagnosis and clinical interpretation of pulmonary ground-glass nodules.BMC medical imaging · 2025Article
- Predicting PD-L1 status in NSCLC patients using deep learning radiomics based on CT images.Scientific reports · 2025Article
- Artificial intelligence in fracture detection on radiographs: a literature review.Japanese journal of radiology · 2025Review
- Review
- Article
- Addressing the current challenges in the clinical application of AI-based Radiomics for cancer imaging.Frontiers in medicine · 2025Review
- Radiomics in radiology: What the radiologist needs to know about technical aspects and clinical impact.La Radiologia medica · 2024Review
- Ultrasound Findings After Breast Cancer Radiation Therapy: Cutaneous, Pleural, Pulmonary, and Cardiac Changes.Korean journal of radiology · 2024Review
- Radiomics and 256-slice-dual-energy CT in the automated diagnosis of mild acute pancreatitis: the innovation of formal methods and high-resolution CT.La Radiologia medica · 2024Article
- Histopathological correlations of CT-based radiomics imaging biomarkers in native kidney biopsy.BMC medical imaging · 2024Article
- Fusion of shallow and deep features fromQuantitative imaging in medicine and surgery · 2024Article
- The role of an artificial intelligence software in clinical senology: a mammography multi-reader study.La Radiologia medica · 2024Article
- Exploring non-invasive precision treatment in non-small cell lung cancer patients through deep learning radiomics across imaging features and molecular phenotypes.Biomarker research · 2024Article
- Scientific Status Quo of Small Renal Lesions: Diagnostic Assessment and Radiomics.Journal of clinical medicine · 2024Review
5 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The use of artificial intelligence (AI) and radiomics in the healthcare setting to advance disease diagnosis and management and facilitate the creation of new therapeutics is gaining popularity. Given the vast amount of data collected during cancer therapy, there is significant concern in leveraging the algorithms and technologies available with the underlying goal of improving oncologic care. Radiologists will attain better precision and effectiveness with the advent of AI technology, making machine-assisted medical services a valuable and important option for future oncologic medical care. As a result, it is critical to figure out which specific radiology activities are best positioned to gain from AI and radiomics models and methods of oncologic imaging, while also considering the algorithms' capabilities and constraints. Our purpose is to overview the current evidence and future prospects of AI and radiomics algorithms used in oncologic imaging efforts with an emphasis on the three most frequent cancers worldwide, i.e., lung cancer, breast cancer and colorectal cancer. We discuss how AI and radiomics could be used to detect and characterize cancers and assess therapy response.
Indexed as
Identifiers
35771379What 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.