ReviewRadiology. Imaging cancer2019
Imaging-based Biomarkers for Predicting and Evaluating Cancer Immunotherapy Response.
Review in Radiology. Imaging cancer, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 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
25 citing papers in PubMed, 56 citations in OpenAlex.
- Multiparametric MRI to Predict Response to Irreversible Electroporation Plus Anti-PD-1 Immunotherapy in Pancreatic Ductal Adenocarcinoma.Magnetic resonance in medicine · 2026Article
- Integrating C-reactive protein flare and early MRI dynamics for enhanced prediction of immunotherapy response.Journal for immunotherapy of cancer · 2025Article
- AI-based neoadjuvant immunotherapy response prediction across pan-cancer: a comprehensive review.Cancer cell international · 2025Review
- Predictive value ofBMC cancer · 2025Article
- Harnessing Biomarker Activatable Probes for Early Stratification and Timely Assessment of Therapeutic Efficacy in Cancer.Exploration (Beijing, China) · 2025Review
- Metabolic crossroads: unravelling immune cell dynamics in gastrointestinal cancer drug resistance.Cancer drug resistance (Alhambra, Calif.) · 2025Review
- Article
- Correlation of CT parameters and PD-L1 expression status in gastric cancer.Abdominal radiology (New York) · 2024Article
- Biomarkers for Early Cancer Detection: A Landscape View of Recent Advancements, Spotlighting Pancreatic and Liver Cancers.ACS pharmacology & translational science · 2024Review
- Principles of risk assessment in colon cancer: immunity is key.Oncoimmunology · 2024Review
- A Review of AI-Based Radiomics and Computational Pathology Approaches in Triple-Negative Breast Cancer: Current Applications and Perspectives.Clinical breast cancer · 2023Review
- Contrast-enhanced CT-based radiomic analysis for determining the response to anti-programmed death-1 therapy in esophageal squamous cell carcinoma patients: A pilot study.Thoracic cancer · 2023Article
- Radiomics and machine learning analysis by computed tomography and magnetic resonance imaging in colorectal liver metastases prognostic assessment.La Radiologia medica · 2023Article
- An overview and a roadmap for artificial intelligence in hematology and oncology.Journal of cancer research and clinical oncology · 2023Review
- Patterns of pseudoprogression across different cancer entities treated with immune checkpoint inhibitors.Cancer imaging : the official publication of the International Cancer Imaging Society · 2023Article
- Biomarkers as Biomedical Bioindicators: Approaches and Techniques for the Detection, Analysis, and Validation of Novel Biomarkers of Diseases.Pharmaceutics · 2023Review
- Colorectal liver metastases patients prognostic assessment: prospects and limits of radiomics and radiogenomics.Infectious agents and cancer · 2023Review
- Post-Surgical Imaging Assessment in Rectal Cancer: Normal Findings and Complications.Journal of clinical medicine · 2023Review
- Article
- Evolution of Radiological Treatment Response Assessments for Cancer Immunotherapy: From iRECIST to Radiomics and Artificial Intelligence.Korean journal of radiology · 2022Review
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 at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Proper patient selection for immunotherapy is critical, as certain tumor microenvironments are more permissible to therapy than others. Currently, the use of programmed cell death ligand-1 (PD-L1) and microsatellite instability high and/or mismatch repair deficiency are used as biomarkers for immunotherapy response. To improve tumor characterization, methodologies are being developed to combine imaging with tumor immune environment characterization. Imaging of tumors from immunotherapy responders and nonresponders with various imaging modalities has led to the development of criteria that could predict patient response to immunotherapy. Additionally, radiomics-based artificial intelligence methods are being used to characterize tumor microenvironments to predict and evaluate immunotherapy responses, as well as to predict risk of immune-related adverse events. Molecular imaging techniques are also being developed for various modalities to observe tumor expression of immunotherapy targets, such as PD-L1 and, to confirm the target is being expressed on resident tumors. In all, the advancements of imaging techniques to define tumor immunologic characteristics will help to stratify patients who are more likely to respond to immunotherapies.
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.