ArticleTranslational lung cancer research2022
FDG PET and CT radiomics in diagnosis and prognosis of non-small-cell lung cancer.
Article in Translational lung cancer research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prediction of PD-L1 expression in nonsmall cell lung cancer using artificial intelligence models based on radiomics: a systematic review and meta-analysis.European respiratory review : an official journal of the European Respiratory Society · 2026Pooled it
- An explainable imaging-clinical biomarker for non-small cell lung cancer prognostication based on normalised hotspot to centroid distance and [European journal of nuclear medicine and molecular imaging · 2026Article
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- Noninvasive Prediction of Programmed Cell Death Protein-Ligand 1 Expression in Locally Advanced Non-small Cell Lung Cancer byAnnals of surgical oncology · 2025Article
- Research on the Precise Differentiation of Pathological Subtypes of Non-Small Cell Lung Cancer Based onCancers · 2025Article
- A prognostic PET radiomic model for risk stratification in non-small cell lung cancer: integrating radiogenomics and clinical features to predict survival and uncover tumor biology insights.Journal of cancer research and clinical oncology · 2025Article
- Using 18F-FDG PET/CT to predict programmed cell death ligand expression in non-small cell lung cancer via metabolic tumour heterogeneity.The British journal of radiology · 2025Article
- Prediction of pathological complete response to neoadjuvant chemoimmunotherapy in non-small cell lung cancer usingBMC cancer · 2025Article
- DeepGR: a deep-learning prognostic model based on glycolytic radiomics for non-small cell lung cancer.Translational lung cancer research · 2024Article
- Prognostic nomogram combiningScientific reports · 2024Article
- Does FDG PET-Based Radiomics Have an Added Value for Prediction of Overall Survival in Non-Small Cell Lung Cancer?Journal of clinical medicine · 2024Article
- MRI-based intratumoral and peritumoral radiomics for preoperative prediction of glioma grade: a multicenter study.Frontiers in oncology · 2024Article
- Radiomics based onFrontiers in oncology · 2024Article
- What is the real value of predictive models?Translational lung cancer research · 2022Article
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Abstract
Background: Methods: Our patients were retrospectively considered. They all had a FDG PET-CT and immuno-histo-chemistry (IHC) to assess PD-L1 expression at the beginning of the disease. A prognosis univariate and multivariate Cox survival analyses was performed for overall survival (OS) and progression free survival (PFS) prediction, including a training/testing procedure. Two sets of 47 PET and 47 CT radiomics features (RFs) were extracted. Difference between RFs according to PD-L1 expression, the histology status and the stage level were tested using suited non parametric statistical tests and the receiver operating characteristics (ROC) curve and the area under curve (AUC). Results: From 2017 to 2019, 212 NSCLC patients treated in our institution were included. The main conventional prognostic variables were stage and gender with a low added prognostic value in the models including PET and CT RFs. Neither PET nor CT RFs were significant to separate the different levels of PD-L1 expression. Several RFs differ between adenocarcinoma (ADC) and squamous cell carcinoma (SCC) tumours and a large number of PET and CT RFs are significantly linked to patient stage. Conclusions: In our population, PET and CT RFs show their intrinsic power to predict survival but do not significantly improve OS and PFS prediction in the different multivariate models, in comparison to conventional data. It would seem necessary to carry out one's own survival analysis before determining a radiomics signature.
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