ReviewCancers2023
PET Radiomics and Response to Immunotherapy in Lung Cancer: A Systematic Review of the Literature.
Review in Cancers, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Insights into pet-based radiogenomics in oncology: an updated systematic review.European journal of nuclear medicine and molecular imaging · 2025Pooled it
- Article
- The SPECT/CT radiomics-based classification of skeletal metastases and benign bone lesions.Scientific reports · 2026Article
- Review
- Article
- Survival impact of a KEAP1-NFE2L2 radiomics model in PDL1 ≥ 50% non-small cell lung cancer treated with pembrolizumab: the PEMBROMIC study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Machine learning-based prediction of invasiveness in lung adenocarcinoma presenting as ground-glass nodules using radiomics and clinical CT features.BMC cancer · 2025Article
- The Role of 18F-FDG PET/CT in Monitoring Immunotherapy Response in Non-Small Cell Lung Cancer: Current Evidence and Challenges: A Narrative Review.Diagnostics (Basel, Switzerland) · 2025Review
- Machine Learning Models Derived from [Bioengineering (Basel, Switzerland) · 2025Article
- Multi-modal radiomics model based on four imaging modalities for predicting pathological complete response to neoadjuvant treatment in breast cancer.BMC cancer · 2025Article
- 18F-FDG PET/CT Radiomics for Predicting Therapy Response in Primary Mediastinal B-Cell Lymphoma: A Bi-Centric Pilot Study.Cancers · 2025Article
- The Role of Radiomics and Artificial Intelligence Applied to Staging PSMA PET in Assessing Prostate Cancer Aggressiveness.Journal of clinical medicine · 2025Review
- Unveiling the biological side of PET-derived biomarkers: a simulation-based approach applied to PDAC assessment.European journal of nuclear medicine and molecular imaging · 2025Article
- Article
- Radiomics Results for Adrenal Mass Characterization Are Stable and Reproducible Under Different Software.Life (Basel, Switzerland) · 2025Article
- A systematic review of the role of artificial intelligence in automating computed tomography-based adaptive radiotherapy for head and neck cancer.Physics and imaging in radiation oncology · 2025Review
- Research trends of artificial intelligence and radiomics in lung cancer: a bibliometric analysis.Quantitative imaging in medicine and surgery · 2024Article
- ML Models Built Using Clinical Parameters and Radiomic Features Extracted fromDiagnostics (Basel, Switzerland) · 2024Article
- A distributed feature selection pipeline for survival analysis using radiomics in non-small cell lung cancer patients.Scientific reports · 2024Article
- Validation of a multiomic model of plasma extracellular vesicle PD-L1 and radiomics for prediction of response to immunotherapy in NSCLC.Journal of experimental & clinical cancer research : CR · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The aim of this review is to provide a comprehensive overview of the existing literature concerning the applications of positron emission tomography (PET) radiomics in lung cancer patient candidates or those undergoing immunotherapy. MATERIALS AND
methodsA systematic review was conducted on databases and web sources. English-language original articles were considered. The title and abstract were independently reviewed to evaluate study inclusion. Duplicate, out-of-topic, and review papers, or editorials, articles, and letters to editors were excluded. For each study, the radiomics analysis was assessed based on the radiomics quality score (RQS 2.0). The review was registered on the PROSPERO database with the number CRD42023402302.
resultsFifteen papers were included, thirteen were qualified as using conventional radiomics approaches, and two used deep learning radiomics. The content of each study was different; indeed, seven papers investigated the potential ability of radiomics to predict PD-L1 expression and tumor microenvironment before starting immunotherapy. Moreover, two evaluated the prediction of response, and four investigated the utility of radiomics to predict the response to immunotherapy. Finally, two papers investigated the prediction of adverse events due to immunotherapy.
conclusionsRadiomics is promising for the evaluation of TME and for the prediction of response to immunotherapy, but some limitations should be overcome.
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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.