ArticleMolecular imaging
Prognostic and Predictive Values of Metabolic Parameters of
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13 citing papers in PubMed, 1 synthesis or guideline pooled it, 21 citations in OpenAlex.
- Predictive value of baseline metabolic tumor volume for non-small-cell lung cancer patients treated with immune checkpoint inhibitors: A meta-analysis.Frontiers in oncology · 2022Pooled it
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- A Thorough Review of the Clinical Applications of Artificial Intelligence in Lung Cancer.Cancers · 2025Review
- GWO+RuleFit: rule-based explainable machine-learning combined with heuristics to predict mid-treatment FDG PET response to chemoradiation for locally advanced non-small cell lung cancer.Physics in medicine and biology · 2024Article
- Machine Learning in Diagnosis and Prognosis of Lung Cancer by PET-CT.Cancer management and research · 2024Review
- Tumor to liver maximum standardized uptake value ratio of FDG-PET/CT parameters predicts tumor treatment response and survival of stage III non-small cell lung cancer.BMC medical imaging · 2023Article
- Volumetric parameters of the primary tumor and whole-body tumor burden derived from baselineEuropean journal of hybrid imaging · 2022Article
- Glucose Metabolism Reprogramming of Primary Tumor and the Liver Is Associated With Disease-Free Survival in Patients With Early NSCLC.Frontiers in oncology · 2021Article
- Prognostic Significance of SUVmax Combined With Lactate Dehydrogenase in Advanced Lung Cancer Patients Treated With Immune Checkpoint Inhibitor Plus Chemotherapy: A Retrospective Study.Frontiers in oncology · 2021Article
- Enhanced glucose metabolism mediated by CD147 is associated withThoracic cancer · 2020Article
- Prognostication Based on Texture Analysis of BaselineIndian journal of nuclear medicine : IJNM : the official journal of the Society of Nuclear Medicine, IndiaArticle
- Next-generation radiomic sequencing in non-small cell lung cancer: an alternative model to predict mutations from [18F]FDG PET/CT.Therapeutic advances in respiratory diseaseArticle
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesIncreasing interests have been focused on using artificial intelligence (AI) to extend prognostic value of medical imaging. Feature extraction is a critical step for successful application of AI. The aim of this study was to explore several metabolic parameters measured by
methodsA set of metabolic parameters of PET/CT and clinical characteristics were detected from 137 patients with NSCLC treated with at least 1 cycle of chemotherapy. Survival receiver-operating characteristic (ROC) analysis was used to define the more significant parameters chosen for the following survival analysis. Patient survival was analyzed by Kaplan-Meier method, log-rank test, and Cox regression.
resultsSurvival ROC showed that maximum standardized uptake value (SUVmax), metabolic tumor volume 50% (MTV50), and total lesion glycolysis 50% (TLG50) had larger area under the curve, and the optimal cutoff values were 11.72, 4.04, and 34.55, respectively. Univariate and multivariate analyses synergistically showed that late PET/CT stage and MTV50 >4.04 were independent factors of poor survival in patients with NSCLC who received chemotherapy.
conclusionsSeveral potential prognostic biomarkers of PET/CT imaging have been extracted for predicting survival and selecting patients with NSCLC who are more likely to benefit from chemotherapy. The identification may accelerate the development of AI methods to improve treatment outcome for NSCLC.
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