ReviewInternational journal of radiation oncology, biology, physics2018
Challenges and Promises of PET Radiomics.
Review in International journal of radiation oncology, biology, physics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04276025 (Relationship Between Histopathological Features, Mutation Status, 18F-FDG PET/CT Radiomic Imaging Parameters as Well as Clinical Outcome in Patients With Treatment-naïve Non-small Cell Lung Cancer), which is not on this map. Cited by 81 papers, 2 of them syntheses 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.
Relationship Between Histopathological Features, Mutation Status, 18F-FDG PET/CT Radiomic Imaging Parameters as Well as Clinical Outcome in Patients With Treatment-naïve Non-small Cell Lung Cancer
Who cites it
81 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Joint EANM/SNMMI guideline on radiomics in nuclear medicine : Jointly supported by the EANM Physics Committee and the SNMMI Physics, Instrumentation and Data Sciences Council.European journal of nuclear medicine and molecular imaging · 2023Guideline
- Current progress and quality of radiomic studies for predicting EGFR mutation in patients with non-small cell lung cancer using PET/CT images: a systematic review.The British journal of radiology · 2021Pooled it
- Metabolic heterogeneity-based radiomic model predicts treatment outcomes of primary mediastinal B-cell lymphoma patients in the IELSG37 study.British journal of haematology · 2026Trial
- Construction of a comprehensive predictive model for axillary lymph node metastasis in breast cancer: a retrospective study.BMC cancer · 2023Trial
- Predicting locally advanced rectal cancer response to neoadjuvant therapy withEuropean journal of nuclear medicine and molecular imaging · 2019Trial
- Pretreatment Staging FDG PET-Derived Radiomic Score Predicts Progression-Free Survival in Locally Advanced Rectal Cancer Treated with Total Neoadjuvant Therapy.Diagnostics (Basel, Switzerland) · 2026Article
- Enhancing PET/CT Radiomics Robustness Through Graph Signal Processing.Diagnostics (Basel, Switzerland) · 2026Article
- The Current Landscape of Artificial Intelligence in Positron Emission Tomography (PET) Imaging Across the Cancer Continuum.Journal of clinical medicine · 2026Review
- Evaluation of Volumetric Reference Ranges for SPECT MPI Parameters and the Predictive Power of Dyssynchrony Parameters: A Cross-Sectional StudyCurrent medical imaging · 2026Article
- Future of radiotheranostics.BMJ oncology · 2026Review
- Current Uses for Medical Imaging With Orthopaedic Implant Technology.Journal of orthopaedic research : official publication of the Orthopaedic Research Society · 2026Review
- The study of dual-phaseScientific reports · 2025Article
- The Role of [18F]FDG PET-Based Radiomics and Machine Learning for the Evaluation of Cardiac Sarcoidosis: A Narrative Literature Review.Medicina (Kaunas, Lithuania) · 2025Review
- Multivariate Framework of Metabolism in Advanced Prostate Cancer Using Whole Abdominal and Pelvic HyperpolarizedCancers · 2025Article
- CT radiomics to predict pathologic complete response after neoadjuvant immunotherapy plus chemoradiotherapy in locally advanced esophageal squamous cell carcinoma.European radiology · 2025Article
- Long-axial field-of-view PET/CT improves radiomics feature reliability.European journal of nuclear medicine and molecular imaging · 2025Article
- Spatial profiling of the metabolism-immune axis in ovarian cancer.Frontiers in pharmacology · 2025Review
- The Role of Artificial Intelligence and Radiomics in the Management of Lymphomas by PET/CT: The Clairvoyance in Clinic.Cancer management and research · 2025Review
- Circulating tumor DNA in diffuse large B-cell lymphoma: analysis of response assessment, correlation with PET/CT and clone evolution.Hematology, transfusion and cell therapy · 2024Article
- Enhancing Survival Outcome Predictions in Metastatic Non-Small Cell Lung Cancer Through PET Radiomics Analysis.Cancers · 2024Article
21 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
purposeRadiomics describes the extraction of multiple, otherwise invisible, features from medical images that, with bioinformatic approaches, can be used to provide additional information that can predict underlying tumor biology and behavior. METHODS AND MATERIALS: Radiomic signatures can be used alone or with other patient-specific data to improve tumor phenotyping, treatment response prediction, and prognosis, noninvasively. The data describing
resultsIn relation to radiation therapy practice, early data have reported the use of radiomic approaches to better define tumor volumes and predict radiation toxicity and treatment response.
conclusionsAlthough at an early stage of development, with many technical challenges remaining and a need for standardization, promise nevertheless exists that PET radiomics will contribute to personalized medicine, especially with the availability of increased computing power and the development of machine-learning approaches for imaging.
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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.