ArticleBMC research notes2022
Radiomics combined with clinical characteristics predicted the progression-free survival time in first-line targeted therapy for advanced non-small cell lung cancer with EGFR mutation.
Article in BMC research notes, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
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Who cites it
10 citing papers in PubMed.
- Radiology-based artificial intelligence for predicting targeted therapy response in pan-cancer: a comprehensive review.Journal of translational medicine · 2025Review
- Progression-Free Survival Prediction Model Based on AI-Enhanced Dynamic Radiomics for Personalized EGFR-TKI Treatment Monitoring Patients With Lung Adenocarcinoma.Thoracic cancer · 2025Article
- Machine learning in image-based outcome prediction after radiotherapy: A review.Journal of applied clinical medical physics · 2025Review
- Dual-Phase Enhanced CT-Derived Radiomics Nomogram for Progression-Free Survival Prediction in Stage IV Lung Adenocarcinoma.Cancer medicine · 2024Article
- Explainable Machine Learning Predictions for the Benefit From Chemotherapy in Advanced Non-Small Cell Lung Cancer Without Available Targeted Mutations.The clinical respiratory journal · 2024Article
- Fusion of shallow and deep features fromQuantitative imaging in medicine and surgery · 2024Article
- Review
- Development and External Validation of a PET Radiomic Model for Prognostication of Head and Neck Cancer.Cancers · 2023Article
- AI/ML advances in non-small cell lung cancer biomarker discovery.Frontiers in oncology · 2023Review
- Prolonged durability of extensive contiguous spinal metastasis stabilization in non-small cell lung cancer patients receiving targeted therapy: two case reports and a literature review.The Journal of international medical research · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
objectiveThis study was to explore the most appropriate radiomics modeling method to predict the progression-free survival of EGFR-TKI treatment in advanced non-small cell lung cancer with EGFR mutations. Different machine learning methods may vary considerably and the selection of a proper model is essential for accurate treatment outcome prediction. Our study were established 176 discrimination models constructed with 22 feature selection methods and 8 classifiers. The predictive performance of each model were evaluated using the AUC, ACC, sensitivity and specificity, where the optimal model was identified.
resultsThere were totally 107 radiomics features and 7 clinical features obtained from each patient. After feature selection, the top-ten most relevant features were fed to train 176 models. Significant performance variations were observed in the established models, with the best performance achieved by the logistic regression model using gini-index feature selection (AUC = 0.797, ACC = 0.722, sensitivity = 0.758, specificity = 0.693). The median R-score was 0.518 (IQR, 0.023-0.987), and the patients were divided into high-risk and low-risk groups based on this cut-off value. The KM survival curves of the two groups demonstrated evident stratification results (p = 0.000).
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Registered trials
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