ArticleActa oncologica (Stockholm, Sweden)2013
Prognostic value of metabolic metrics extracted from baseline positron emission tomography images in non-small cell lung cancer.
Article in Acta oncologica (Stockholm, Sweden), 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 25 papers.
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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.
Determination of the Predictive Value of FDG-PET-CT Scans, Blood Proteins and Blood Cells for the Prognosis for Patients With Lung Cancer Receiving Concurrent Chemo-Radiation
Radiomics for the Prediction of Survival in GBM After Radiotherapy With/Without Temozolomide
Who cites it
25 citing papers in PubMed, 54 citations in OpenAlex.
- Trial
- Radiomics in laryngeal squamous cell carcinoma: state of the art.Acta otorhinolaryngologica Italica : organo ufficiale della Societa italiana di otorinolaringologia e chirurgia cervico-facciale · 2026Review
- Decision tree-based machine learning algorithm for prediction of acute radiation esophagitis.Biochemistry and biophysics reports · 2025Article
- A Bayesian adaptive biomarker stratified phase II randomized clinical trial design for radiotherapies with competing risk survival outcomes.Statistical methods in medical research · 2024Article
- Application of radiomics in diagnosis and treatment of lung cancer.Frontiers in pharmacology · 2023Review
- Article
- A narrative review of prognosis prediction models for non-small cell lung cancer: what kind of predictors should be selected and how to improve models?Annals of translational medicine · 2021Review
- Preselection of robust radiomic features does not improve outcome modelling in non-small cell lung cancer based on clinical routine FDG-PET imaging.EJNMMI research · 2021Article
- Multiple Testing, Cut-Point Optimization, and Signs of Publication Bias in Prognostic FDG-PET Imaging Studies of Head and Neck and Lung Cancer: A Review and Meta-Analysis.Diagnostics (Basel, Switzerland) · 2020Review
- Radiomics in lung cancer for oncologists.Journal of clinical and translational research · 2020Article
- Cone-beam CT radiomics features might improve the prediction of lung toxicity after SBRT in stage I NSCLC patients.Thoracic cancer · 2020Article
- Radiomics and Texture Analysis in Laryngeal Cancer. Looking for New Frontiers in Precision Medicine through Imaging Analysis.Cancers · 2019Review
- Repeatability of Multiparametric Prostate MRI Radiomics Features.Scientific reports · 2019Article
- Developing and validating a novel metabolic tumor volume risk stratification system for supplementing non-small cell lung cancer staging.European journal of nuclear medicine and molecular imaging · 2018Article
- Current Methods to Define Metabolic Tumor Volume in Positron Emission Tomography: Which One is Better?Nuclear medicine and molecular imaging · 2018Review
- Drug Resistance in Non-Small Cell Lung Cancer: A Potential for NOTCH Targeting?Frontiers in oncology · 2018Review
- Radiomics for Response and Outcome Assessment for Non-Small Cell Lung Cancer.Technology in cancer research & treatment · 2018Review
- [European journal of nuclear medicine and molecular imaging · 2017Article
- PET Radiomics in NSCLC: state of the art and a proposal for harmonization of methodology.Scientific reports · 2017Review
- Whole body metabolic tumor volume is a prognostic marker in patients with newly diagnosed stage 3B non-small cell lung cancer, confirmed with external validation.European journal of hybrid imaging · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors at 4 institutions in 2 countries.
Funding
Abstract
backgroundMaximum, mean and peak SUV of primary tumor at baseline FDG-PET scans, have often been found predictive for overall survival in non-small cell lung cancer (NSCLC) patients. In this study we further investigated the prognostic power of advanced metabolic metrics derived from intensity volume histograms (IVH) extracted from PET imaging.
methodsA cohort of 220 NSCLC patients (mean age, 66.6 years; 149 men, 71 women), stages I-IIIB, treated with radiotherapy with curative intent were included (NCT00522639). Each patient underwent standardized pre-treatment CT-PET imaging. Primary GTV was delineated by an experienced radiation oncologist on CT-PET images. Common PET descriptors such as maximum, mean and peak SUV, and metabolic tumor volume (MTV) were quantified. Advanced descriptors of metabolic activity were quantified by IVH. These comprised five groups of features: absolute and relative volume above relative intensity threshold (AVRI and RVRI), absolute and relative volume above absolute intensity threshold (AVAI and RVAI), and absolute intensity above relative volume threshold (AIRV). MTV was derived from the IVH curves for volumes with SUV above 2.5, 3 and 4, and of 40% and 50% maximum SUV. Univariable analysis using Cox Proportional Hazard Regression was performed for overall survival assessment.
resultsRelative volume above higher SUV (80%) was an independent predictor of OS (p = 0.05). None of the possible surrogates for MTV based on volumes above SUV of 3, 40% and 50% of maximum SUV showed significant associations with OS [p (AVAI3) = 0.10, p (AVAI4) = 0.22, p (AVRI40%) = 0.15, p (AVRI50%) = 0.17]. Maximum and peak SUV (r = 0.99) revealed no prognostic value for OS [p (maximum SUV) = 0.20, p (peak SUV) = 0.22].
conclusionsNew methods using more advanced imaging features extracted from PET were analyzed. Best prognostic value for OS of NSCLC patients was found for relative portions of the tumor above higher uptakes (80% SUV).
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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.