ArticleEJNMMI research2023
The predictive value of [
Article in EJNMMI research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 3 of them syntheses that pooled it.
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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.
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Who cites it
17 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Diagnostic performance of machine learning-based radiomics models for predicting epidermal growth factor receptor mutation status in lung adenocarcinoma in Chinese patients: A systematic review and meta-analysis.The Journal of international medical research · 2026Pooled it
- Prediction of oncogene mutation status in non-small cell lung cancer: a systematic review and meta-analysis with a special focus on artificial intelligence-based methods.European radiology · 2026Pooled it
- Predictive value ofFrontiers in oncology · 2024Pooled it
- Differentiation of early relapse and late relapse in intermediate- and high-risk neuroblastoma with anAbdominal radiology (New York) · 2024Trial
- Integrating Lymph Node Metastasis and Programmed Death-Ligand 1 Prediction in Non-Small Cell Lung Cancer From a Single PET/CT Scan: Multicenter Radiomics Study.JMIR medical informatics · 2026Article
- A deep learning framework for lesion-level treatment response prediction in hodgkin lymphoma using PET/CT tensor radiomics.EJNMMI physics · 2026Article
- Interpretable machine learning model integrating CT radiomics, CTR, and clinical features for EGFR mutation prediction in ≤3 cm lung adenocarcinoma nodules.Annals of medicine · 2025Article
- Radiology-based artificial intelligence for predicting targeted therapy response in pan-cancer: a comprehensive review.Journal of translational medicine · 2025Review
- An Integrated Clinical-Radiomics-Deep Learning Model Based onCancer medicine · 2025Article
- A Machine Learning Model Based on Radiomic Features as a Tool to Identify Active Giant Cell Arteritis on [Diagnostics (Basel, Switzerland) · 2025Article
- MVASA-HGN: multi-view adaptive semantic-aware heterogeneous graph network forQuantitative imaging in medicine and surgery · 2025Article
- BATF promotes tumor progression and association with FDG PET-derived parameters in colorectal cancer.Journal of translational medicine · 2024Article
- Subregion-specificAmerican journal of nuclear medicine and molecular imaging · 2024Article
- Clinical application ofJapanese journal of radiology · 2024Review
- New research progress on 18F-FDG PET/CT radiomics for EGFR mutation prediction in lung adenocarcinoma: a review.Frontiers in oncology · 2023Review
- OptimalFrontiers in oncology · 2023Article
- Explainable 1-mm Peritumoral CT Radiomics forCancer control : journal of the Moffitt Cancer CenterArticle
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Authors and funding
8 authors.
Funding
Abstract
backgroundThis study aims to construct radiomics models based on [
methodsA total of 515 patients were retrospectively collected and divided into a training set (n = 404) and an independent testing set (n = 111) according to their examination time. After semi-automatic segmentation of PET/CT images, the radiomics features were extracted, and the best feature sets of CT, PET, and PET/CT modalities were screened out. Nine radiomics models were constructed using logistic regression (LR), random forest (RF), and support vector machine (SVM) methods. According to the performance in the testing set, the best model of the three modalities was kept, and its radiomics score (Rad-score) was calculated. Furthermore, combined with the valuable clinical parameters (gender, smoking history, nodule type, CEA, SCC-Ag), a joint radiomics model was built.
resultsCompared with LR and SVM, the RF Rad-score showed the best performance among the three radiomics models of CT, PET, and PET/CT (training and testing sets AUC: 0.688, 0.666, and 0.698 vs. 0.726, 0.678, and 0.704). Among the three joint models, the PET/CT joint model performed the best (training and testing sets AUC: 0.760 vs. 0.730). The further stratified analysis found that CT_RF had the best prediction effect for stage I-II lesions (training set and testing set AUC: 0.791 vs. 0.797), while PET/CT joint model had the best prediction effect for stage III-IV lesions (training and testing sets AUC: 0.722 vs. 0.723).
conclusionsCombining with clinical parameters can improve the predictive performance of PET/CT radiomics model, especially for patients with advanced lung adenocarcinoma.
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