ReviewCA: a cancer journal for clinicians2019
Artificial intelligence in cancer imaging: Clinical challenges and applications.
Review in CA: a cancer journal for clinicians, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 953 papers, 4 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.
Serum and Tissue Metabolite-based Prediction of Sentinel Lymph Node Metastasis in Breast Cancer
Construction of a Standardized Benchmark Evaluation System for Intelligent Breast Ultrasound Image Interpretation and Systematic Performance Assessment of Multimodal Artificial Intelligence Models Based on ACR BI-RADS v2025 Criteria
Who cites it
953 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Ultrasound-based predictive models for response to neoadjuvant chemotherapy in breast cancer: a systematic review and meta-analysis.BMC cancer · 2026Pooled it
- Artificial-Intelligence-Based Radiologic, Histopathologic, and Molecular Models for the Diagnosis and Classification of Malignant Salivary Gland Tumors: A Systematic Review and Functional Meta-Synthesis.Medical sciences (Basel, Switzerland) · 2026Pooled it
- Application and integration of deep learning in tumour radiomics: bibliometrics and visualisation analysis.Frontiers in artificial intelligence · 2026Pooled it
- Machine Learning-Driven radiomics on 18 F-FDG PET for glioma diagnosis: a systematic review and meta-analysis.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Pooled it
- Artificial intelligence and extracellular vesicles in oncology: towards tumor diagnosis, prediction, and therapy.Drug delivery · 2026Review
- Development and validation of a prediction model for moderate-to-severe radiation dermatitis in patients with nasopharyngeal carcinoma: A machine learning study.Asia-Pacific journal of oncology nursing · 2026Article
- Automated Renal Tumor Segmentation in Computed Tomography Images Using a Global Attention-Based DeepLabV3+ Model: Algorithm Development and Validation.JMIR medical informatics · 2026Article
- Clinical applications of artificial intelligence-driven nitric oxide: a bibliometric and scientific mapping analysis.Medical gas research · 2026Review
- A two-stage foundation model for bladder tumor segmentation: An international multi-site studyEuropean journal of radiology artificial intelligence · 2026Article
- Identifying a better-prognosis pancreatic cancer from its benign inflammatory mimic: a machine learning approach with contrast-enhanced ultrasound for early intervention.Abdominal radiology (New York) · 2026Article
- Tumor metrics imaging core labs: primer for radiologists.Abdominal radiology (New York) · 2026Review
- Narrative review of the staging classification controversy in stage N3 small cell lung cancer: from the perspective of overlapping Veterans Administration Lung Study Group and International Association for the Study of Lung Cancer definitions.Journal of thoracic disease · 2026Review
- AI agents in cancer imaging: Concepts, advances, and clinical perspectives.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2026Article
- AI Adoption in US Cancer Centers: National Cross-Sectional Study of Institutional and Policy Determinants.Journal of medical Internet research · 2026Article
- AI-Assisted Tumor Boundary Delineation via Targeted Ultrasmall Iron Oxide Nanoprobe for High-Contrast HER2-Positive Tumor Imaging.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Applications of Artificial Intelligence in Cancer Diagnosis and Treatment.Cancer medicine · 2026Review
- Real-World Validation of the Clinical Distinctiveness of IDH-Mutant Glioblastoma in the SEER Transition Era: A Population-Based Study Integrating Machine Learning.Brain and behavior · 2026Article
- Non-invasive predictive model for incidental gallbladder carcinoma based on multimodal features: Integrating clinical data, MRI radiomics, and deep transfer learning features.Translational oncology · 2026Article
- BoneCoT: multicentre validation of a whole-body skeleton foundation model for bone metastases guided by clinician-derived chain of thought.Nature biomedical engineering · 2026Article
- Ethical and Governance Challenges of AI in Medical Imaging and Diagnostics: A Systematic Survey and Policy Framework Recommendations.Healthcare (Basel, Switzerland) · 2026Review
893 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
19 authors.
Funding
Abstract
Judgement, as one of the core tenets of medicine, relies upon the integration of multilayered data with nuanced decision making. Cancer offers a unique context for medical decisions given not only its variegated forms with evolution of disease but also the need to take into account the individual condition of patients, their ability to receive treatment, and their responses to treatment. Challenges remain in the accurate detection, characterization, and monitoring of cancers despite improved technologies. Radiographic assessment of disease most commonly relies upon visual evaluations, the interpretations of which may be augmented by advanced computational analyses. In particular, artificial intelligence (AI) promises to make great strides in the qualitative interpretation of cancer imaging by expert clinicians, including volumetric delineation of tumors over time, extrapolation of the tumor genotype and biological course from its radiographic phenotype, prediction of clinical outcome, and assessment of the impact of disease and treatment on adjacent organs. AI may automate processes in the initial interpretation of images and shift the clinical workflow of radiographic detection, management decisions on whether or not to administer an intervention, and subsequent observation to a yet to be envisioned paradigm. Here, the authors review the current state of AI as applied to medical imaging of cancer and describe advances in 4 tumor types (lung, brain, breast, and prostate) to illustrate how common clinical problems are being addressed. Although most studies evaluating AI applications in oncology to date have not been vigorously validated for reproducibility and generalizability, the results do highlight increasingly concerted efforts in pushing AI technology to clinical use and to impact future directions in cancer care.
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.