ReviewJapanese journal of radiology2024
CT and MRI of abdominal cancers: current trends and perspectives in the era of radiomics and artificial intelligence.
Review in Japanese journal of radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 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.
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.
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Who cites it
19 citing papers in PubMed, 30 citations in OpenAlex.
- Practical applications of photon-counting CT in oncology: an organ-based pictorial review.Insights into imaging · 2026Article
- Whole cross-sectional human ultrasound tomography.Nature biomedical engineering · 2026Article
- Artificial superintelligence alignment in healthcare.Japanese journal of radiology · 2026Review
- Predictive imaging in abdominal oncology: current trends and future directions.Japanese journal of radiology · 2026Review
- Application and prospect of artificial intelligence in diagnostic imaging of prostate cancer.NPJ digital medicine · 2026Review
- A deep learning-based automated pipeline for colorectal cancer detection in contrast-enhanced CT images.Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society · 2026Article
- A Real-Time Online Nomogram Integrating Systemic Inflammatory Response Index and Lactate Dehydrogenase to Predict Pathological Response in Gastric Cancer Patients Receiving Neoadjuvant Chemoimmunotherapy.Cancer management and research · 2026Article
- Recent applications of artificial intelligence in cancer radiotherapy and immunotherapy: current status and future directions.Frontiers in immunology · 2026Review
- Illuminating radiogenomic signatures in pediatric-type diffuse gliomas: insights into molecular, clinical, and imaging correlations. Part I: high-grade group.La Radiologia medica · 2025Review
- The Evolution and Clinical Impact of Deep Learning Technologies in Breast MRI.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2025Review
- Advancing clinical MRI exams with artificial intelligence: Japan's contributions and future prospects.Japanese journal of radiology · 2025Review
- Stereotactic arrhythmia radioablation for ventricular tachycardia: a review of clinical trials and emerging roles of imaging.Journal of radiation research · 2025Review
- A bibliometric analysis of the advance of artificial intelligence in medicine.Frontiers in medicine · 2025Article
- Prognostic value of the C-reactive protein-albumin-lymphocyte index versus traditional inflammatory markers after radical resection of colorectal cancer: a retrospective cohort study.Frontiers in medicine · 2025Article
- Data set terminology of deep learning in medicine: a historical review and recommendation.Japanese journal of radiology · 2024Review
- Predicting prognosis for epithelial ovarian cancer patients receiving bevacizumab treatment with CT-based deep learning.NPJ precision oncology · 2024Article
- Recent trends in AI applications for pelvic MRI: a comprehensive review.La Radiologia medica · 2024Review
- AI Use in Mammography for Diagnosing Metachronous Contralateral Breast Cancer.Journal of imaging · 2024Article
- Understanding Gastric GIST: From Pathophysiology to Personalized Treatment.Journal of clinical medicine · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Abdominal cancers continue to pose daily challenges to clinicians, radiologists and researchers. These challenges are faced at each stage of abdominal cancer management, including early detection, accurate characterization, precise assessment of tumor spread, preoperative planning when surgery is anticipated, prediction of tumor aggressiveness, response to therapy, and detection of recurrence. Technical advances in medical imaging, often in combination with imaging biomarkers, show great promise in addressing such challenges. Information extracted from imaging datasets owing to the application of radiomics can be used to further improve the diagnostic capabilities of imaging. However, the analysis of the huge amount of data provided by these advances is a difficult task in daily practice. Artificial intelligence has the potential to help radiologists in all these challenges. Notably, the applications of AI in the field of abdominal cancers are expanding and now include diverse approaches for cancer detection, diagnosis and classification, genomics and detection of genetic alterations, analysis of tumor microenvironment, identification of predictive biomarkers and follow-up. However, AI currently has some limitations that need further refinement for implementation in the clinical setting. This review article sums up recent advances in imaging of abdominal cancers in the field of image/data acquisition, tumor detection, tumor characterization, prognosis, and treatment response evaluation.
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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.