ReviewCancer management and research2020
Application and Prospects of Molecular Imaging in Immunotherapy.
Review in Cancer management and research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed.
- Advances in PD-L1 Targeted Molecular Imaging Radiotracers Research: From Preclinical Exploration to Clinical Application.Molecular imaging and biology · 2026Review
- The application and prospect of granzyme B-targeted PET imaging in cancer immunotherapy.Journal of translational medicine · 2025Review
- Immunoexpressions of PD-L1 and EZH2 in Endometrial Carcinoma: Associations with Clinicopathological Parameters.Diagnostics (Basel, Switzerland) · 2025Article
- Prediction of pathological response to neoadjuvant immunochemotherapy with baseline and post-treatmentBMC cancer · 2025Article
- Innovative optical imaging strategies for monitoring immunotherapy in the tumor microenvironments.Cancer medicine · 2024Review
- Current status and progress of PD-L1 detection: guiding immunotherapy for non-small cell lung cancer.Clinical and experimental medicine · 2024Review
- Recent Advancement of PD-L1 Detection Technologies and Clinical Applications in the Era of Precision Cancer Therapy.Journal of Cancer · 2023Review
- Article
- Imaging approaches and radiomics: toward a new era of ultraprecision radioimmunotherapy?Journal for immunotherapy of cancer · 2022Review
- Immuno-Imaging (PET/SPECT)-Quo Vadis?Molecules (Basel, Switzerland) · 2022Review
- Molecular imaging of tumor-associated macrophages in cancer immunotherapy.Therapeutic advances in medical oncology · 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
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recently, immunotherapies that target the interactions of programmed cell death 1 (PD-1) with its major ligands, programmed death ligand 1 (PD-L1) and programmed death ligand 2 (PD-L2), have achieved significant success. To date, several immune checkpoint inhibitors targeting the PD-1/PD-L1 pathway have been developed to treat melanoma, non-small cell lung cancer, head and neck cancer, renal cell carcinoma, and urothelial carcinoma. Despite promising outcomes with immunotherapy, there are many limitations to several current immune biomarkers for predicting immune benefits and to traditional imaging for evaluating the efficacy and prognosis of immunotherapy and monitoring adverse reactions. In this review, we recommend a novel imaging method, molecular imaging. This paper reviews the application and prospects of molecular imaging in the context of current immunotherapies in regard to the following aspects: 1) detecting the expression of PD-1/PD-L1; 2) evaluating the efficacy of immunotherapy; 3) assessing patient prognosis with immunotherapy; 4) monitoring the toxicity of immunotherapy; and 5) other targets imaging.
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.