ArticleScientific reports2018
Radiomics analysis at PET/CT contributes to prognosis of recurrence and survival in lung cancer treated with stereotactic body radiotherapy.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 79 papers.
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Who cites it
79 citing papers in PubMed.
- Radiomic analysis for predicting prognosis of colorectal cancer from preoperativeJournal of translational medicine · 2022Trial
- Improving prognostic performance in resectable pancreatic ductal adenocarcinoma using radiomics and deep learning features fusion in CT images.Scientific reports · 2021Trial
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- 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
- A CT-based radiomics model for predicting pain relief after radiotherapy in patients with bone metastases: a dual-center study.Frontiers in oncology · 2026Article
- Radiomics-based high-resolution CT analysis for differentiating primary tumor sources of pulmonary metastases.Frontiers in oncology · 2026Article
- Development and validation of a nomogram for local control prediction in lung cancer patients treated with stereotactic body radiation therapy based on clinical, dosimetric, and inflammation-related parameters.BMC pulmonary medicine · 2025Article
- Article
- Brain Tumor Recurrence vs. Radiation Necrosis Classification and Patient Survivability Prediction.IEEE journal of biomedical and health informatics · 2024Article
- Impact of Preprocessing Parameters in Medical Imaging-Based Radiomic Studies: A Systematic Review.Cancers · 2024Review
- Evaluation of the performance of both machine learning models using PET and CT radiomics for predicting recurrence following lung stereotactic body radiation therapy: A single-institutional study.Journal of applied clinical medical physics · 2024Article
- Implementation of Artificial Intelligence in Personalized Prognostic Assessment of Lung Cancer: A Narrative Review.Cancers · 2024Review
- 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
- Multicentric development and evaluation of [European journal of nuclear medicine and molecular imaging · 2024Article
- Radiomics in stereotactic body radiotherapy for non-small cell lung cancer: a systematic review and radiomic quality score study.Radiation oncology journal · 2024Article
- Article
- Pretreatment and Posttreatment Tumor Metabolic Activity Assessed by FDG-PET/CT as Predictors of Tumor Recurrence and Survival Outcomes in Early-Stage Non-Small Cell Lung Cancer Treated With Stereotactic Body Radiation Therapy.Advances in radiation oncology · 2024Article
- Machine Learning in Diagnosis and Prognosis of Lung Cancer by PET-CT.Cancer management and research · 2024Review
- Radiotranscriptomics of non-small cell lung carcinoma for assessing high-level clinical outcomes using a machine learning-derived multi-modal signature.Biomedical engineering online · 2023Article
- Methodological evaluation of original articles on radiomics and machine learning for outcome prediction based on positron emission tomography (PET).Nuklearmedizin. Nuclear medicine · 2023Review
19 more citing papers are in PubMed but not listed here.
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11 authors.
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Abstract
We sought to quantify contribution of radiomics and SUVmax at PET/CT to predict clinical outcome in lung cancer patients treated with stereotactic body radiotherapy (SBRT). 150 patients with 172 lung cancers, who underwent SBRT were retrospectively included. Radiomics were applied on PET/CT. Principal components (PC) for 42 CT and PET-derived features were examined to determine which ones accounted for most of variability. Survival analysis quantified ability of radiomics and SUVmax to predict outcome. PCs including homogeneity, size, maximum intensity, mean and median gray level, standard deviation, entropy, kurtosis, skewness, morphology and asymmetry were included in prediction models for regional control (RC) [PC4-HR:0.38, p = 0.02], distant control (DC) [PC4-HR:0.51, p = 0.02 and PC1-HR:1.12, p = 0.01], recurrence free probability (RFP) [PC1-HR:1.08, p = 0.04], disease specific survival (DSS) [PC2-HR:1.34, p = 0.03 and PC3-HR:0.64, p = 0.02] and overall survival (OS) [PC4-HR:0.45, p = 0.004 and PC3-HR:0.74, p = 0.02]. In combined analysis with SUVmax, PC1 lost predictive ability over SUVmax for RFP [HR:1.1, p = 0.04] and DC [HR:1.13, p = 0.002], while PC4 remained predictive of DC independent of SUVmax [HR:0.5, p = 0.02]. Radiomics remained the only predictors of OS, DSS and RC. Neither SUVmax nor radiomics predicted recurrence free survival. Radiomics on PET/CT provided complementary information for prediction of control and survival in SBRT-treated lung cancer patients.
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