ArticleCancers2022
Establishment of a Prediction Model for Overall Survival after Stereotactic Body Radiation Therapy for Primary Non-Small Cell Lung Cancer Using Radiomics Analysis.
Article in Cancers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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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.
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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
6 citing papers in PubMed, 8 citations in OpenAlex.
- CT-Based Radiomics Enhance Respiratory Function Analysis for Lung SBRT.Bioengineering (Basel, Switzerland) · 2025Article
- Harnessing Baseline Radiomic Features in Early-Stage NSCLC: What Role in Clinical Outcome Modeling for SBRT Candidates?Cancers · 2025Article
- 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
- Multicentric development and evaluation of [European journal of nuclear medicine and molecular imaging · 2024Article
- Developing a clinical-radiomic prediction model for 3-year cancer-specific survival in lung cancer patients treated with stereotactic body radiation therapy.Journal of cancer research and clinical oncology · 2024Article
- Recent Advances and Challenges in Stereotactic Body Radiotherapy.Technology in cancer research & treatmentArticle
Corrections and comments
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Authors and funding
9 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Stereotactic body radiation therapy (SBRT) for early-stage non-small cell lung cancer (NSCLC) leads to recurrence in approximately 18% of patients. We aimed to extract the radiomic features, with which we predicted clinical outcomes and to establish predictive models. Patients with primary non-metastatic NSCLC who were treated with SBRT between 2002 and 2022 were retrospectively reviewed. The 358 primary tumors were randomly divided into a training cohort of 250 tumors and a validation cohort of 108 tumors. Clinical features and 744 radiomic features derived from primary tumor delineation on pre-treatment computed tomography were examined as prognostic factors of survival outcomes by univariate and multivariate analyses in the training cohort. Predictive models of survival outcomes were established from the results of the multivariate analysis in the training cohort. The selected radiomic features and prediction models were tested in a validation cohort. We found that one radiomic feature showed a significant difference in overall survival (OS) in the validation cohort (
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