ReviewJournal of medical imaging and radiation oncology2022
Role of radiomics in predicting immunotherapy response.
Review in Journal of medical imaging and radiation oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 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
18 citing papers in PubMed, 27 citations in OpenAlex.
- Radiomics signature based on robust features derived from diffusion data for differentiation between benign and malignant solitary pulmonary lesions.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Trial
- PET/CT imaging for assessment of tumor immunotherapy response: a new perspective on tumor metabolic reprogramming and immune escape.La Radiologia medica · 2026Review
- Timing matters: how mechanism-guided sequencing shapes the efficacy of radiotherapy-immunotherapy combinations : A systematic review.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2026Review
- Radiomics analysis of restaging MRI for detection of pathological complete response in locally advanced rectal cancer.European journal of radiology open · 2026Article
- Artificial intelligence in gastrointestinal and hepatopancreatobiliary surgery: innovations, integration, and future directions.Annals of medicine and surgery (2012) · 2026Review
- Deep learning and radiomics models in patients with advanced non-small cell lung cancer treated with immunotherapy combined with stereotactic radiotherapy.Scientific reports · 2026Article
- QVT score, a radiomic biomarker of vascular complexity, enables prognostication and monitoring of NSCLC immunotherapy.Journal for immunotherapy of cancer · 2026Article
- AI-based neoadjuvant immunotherapy response prediction across pan-cancer: a comprehensive review.Cancer cell international · 2025Review
- Radiomics-based prediction of HCC response to atezolizumab/bevacizumab.Oncology letters · 2025Article
- Prediction of PD-L1 and CD68 in Clear Cell Renal Cell Carcinoma with Green Learning.Journal of imaging · 2025Article
- The impact of Inter-observation variation on radiomic features of pulmonary nodules.Frontiers in oncology · 2025Article
- Integrated noninvasive diagnostics for prediction of survival in immunotherapy.Immuno-oncology technology · 2024Article
- A Novel Data Augmentation Method for Radiomics Analysis Using Image Perturbations.Journal of imaging informatics in medicine · 2024Article
- Radiomics nomogram for predicting chemo-immunotherapy efficiency in advanced non-small cell lung cancer.Scientific reports · 2024Article
- Nonoperative Management of dMMR/MSI-H Colorectal Cancer following Neoadjuvant Immunotherapy: A Narrative Review.Clinics in colon and rectal surgery · 2023Review
- Radiomics, a Promising New Discipline: Example of Hepatocellular Carcinoma.Diagnostics (Basel, Switzerland) · 2023Review
- A robust radiomic-based machine learning approach to detect cardiac amyloidosis using cardiac computed tomography.Frontiers in radiology · 2023Article
- Leveraging high-resolution omics data for predicting responses and adverse events to immune checkpoint inhibitors.Computational and structural biotechnology journal · 2023Review
Corrections and comments
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Authors and funding
1 author at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Immunotherapies have revolutionised cancer management. Despite their success, durable responses are limited to a subset of patients. Prediction of immunotherapy response in patients has proven to be difficult due to a lack of robust biomarkers. Routinely collected imaging may offer an additional information source to personalise patient treatment, with advantages over tissue-based biomarkers. Quantitative image analysis or radiomics, which involves the high-throughput extraction of imaging features, has the potential to non-invasively predict cancer histology, outcomes and prognosis. This review evaluates the value of radiomics in patients undergoing immunotherapy, with a summary provided of the performance of radiomics models in predicting immunotherapy response and toxicity, as well as immune correlates. Much of the literature focussed on clinical endpoints and correlates to tissue biomarkers, particularly in lung cancer, while few studies investigated association with immune-related adverse events. Strengths of the studies included more frequent use of clinical trial datasets, homogenous patient cohorts and high-quality diagnostic scans. Limitations of the studies include heterogeneity in study methodology, lack of well-defined homogenous imaging datasets, limited open publishing of imaging datasets, coding and parameters used for radiomics signature development and limited use of external validation datasets. Future research should address the above limitations, as well as further explore the relationship between radiomics and immune-related adverse effects and less well-studied biological correlates such tumour mutational burden, and incorporate known clinical prognostic scores into radiomics models.
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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.