ArticleFrontiers in oncology2022
A combined predictive model based on radiomics features and clinical factors for disease progression in early-stage non-small cell lung cancer treated with stereotactic ablative radiotherapy.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06405815 (Development of a Tumor and Peritumor Radiomics-based Model to Predict the High-risk Surgical Margin and Prognosis of T3 and T4 Non-Small Cell Lung Cancer), which is not on this map. Cited by 11 papers.
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Development of a Tumor and Peritumor Radiomics-based Model to Predict the High-risk Surgical Margin and Prognosis of T3 and T4 Non-Small Cell Lung Cancer
Who cites it
11 citing papers in PubMed, 16 citations in OpenAlex.
- Clinical-radiomic model development for local recurrence prediction in stereotactic body radiation therapy-treated lung cancer: comparative analysis with radiobiological models.Scientific reports · 2026Article
- MRI-based clinical-radiologic model to predict the efficacy and prognosis of interventional with systemic therapy in advanced hepatocellular carcinoma: a multicenter study.Journal of translational medicine · 2026Article
- Radiation-Induced Lung Injury CT Features: Early Non-Small Cell Lung Cancer SBRT Prognosticators.Current medical imaging · 2025Article
- A nomogram predicts the new high-grade patterns of pulmonary invasive non-mucinous adenocarcinoma based on the radiomics and clinical features.American journal of translational research · 2025Article
- Long-term survival and failure patterns in inoperable early-stage non-small cell lung cancer following stereotactic body radiotherapy: a single-institution retrospective study.Scientific reports · 2024Article
- Can Patient Characteristics and Pre-Treatment MRI Features Predict Survival After Stereotactic Ablative Radiotherapy (SABR) Treatment in Hepatocellular Carcinoma (HCC): Preliminary Assessment.Current oncology (Toronto, Ont.) · 2024Article
- Radiogenomics models for predicting prognosis in locally advanced non-small cell lung cancer patients undergoing definitive chemoradiotherapy.Translational lung cancer research · 2024Article
- Prognostic Value ofKorean journal of radiology · 2024Article
- 2.5D peritumoural radiomics predicts postoperative recurrence in stage I lung adenocarcinoma.Frontiers in oncology · 2024Article
- Overexpression of TWF1 promotes lung adenocarcinoma progression and is associated with poor prognosis in cancer patients through the MMP1 signaling pathway.Journal of thoracic disease · 2023Article
- Artificial Intelligence-suggested Predictive Model of Survival in Patients Treated With Stereotactic Radiotherapy for Early Lung Cancer.In vivo (Athens, Greece)Article
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Authors and funding
35 authors at 6 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: To accurately assess disease progression after Stereotactic Ablative Radiotherapy (SABR) of early-stage Non-Small Cell Lung Cancer (NSCLC), a combined predictive model based on pre-treatment CT radiomics features and clinical factors was established. Methods: This study retrospectively analyzed the data of 96 patients with early-stage NSCLC treated with SABR. Clinical factors included general information (e.g. gender, age, KPS, Charlson score, lung function, smoking status), pre-treatment lesion status (e.g. diameter, location, pathological type, T stage), radiation parameters (biological effective dose, BED), the type of peritumoral radiation-induced lung injury (RILI). Independent risk factors were screened by logistic regression analysis. Radiomics features were extracted from pre-treatment CT. The minimum Redundancy Maximum Relevance (mRMR) and the Least Absolute Shrinkage and Selection Operator (LASSO) were adopted for the dimensionality reduction and feature selection. According to the weight coefficient of the features, the Radscore was calculated, and the radiomics model was constructed. Multiple logistic regression analysis was applied to establish the combined model based on radiomics features and clinical factors. Receiver Operating Characteristic (ROC) curve, DeLong test, Hosmer-Lemeshow test, and Decision Curve Analysis (DCA) were used to evaluate the model's diagnostic efficiency and clinical practicability. Results: With the median follow-up of 59.1 months, 29 patients developed progression and 67 remained good controlled within two years. Among the clinical factors, the type of peritumoral RILI was the only independent risk factor for progression ( Conclusions: Compared with the clinical model, the radiomics model and the combined model can better predict the disease progression of early-stage NSCLC after SABR, which might contribute to individualized follow-up plans and treatment strategies.
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