ArticleLife (Basel, Switzerland)2023
Predicting the Efficacy of SBRT for Lung Cancer with
Article in Life (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- Radiogenomic landscape of the hallmarks of cancer.Biomarker research · 2026Review
- A monocentric retrospective analysis of 10-year overall survival after stereotactic body radiotherapy for medically inoperable non-small cell lung cancer.Oncology letters · 2025Article
- The Effectiveness of SBRT for Solitary or Synchronous Multiple Pulmonary Nodules Suspicious of Early-Stage Lung Cancer Without Pathological Confirmation.Biomedicines · 2025Article
- [Application Value of an AI-based Imaging Feature Parameter Model for Predicting the Malignancy of Part-solid Pulmonary Nodule].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2025Article
- Machine Learning Enabled Prediction of Biologically Relevant Gene Expression Using CT-Based Radiomic Features in Non-Small Cell Lung Cancer.Cancer medicine · 2024Article
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
purposeto develop a radiogenomic model on the basis of
methodsA total of 123 patients with lung cancer who had undergone
resultsA total of 2042 radiomics features were extracted. Five radiomic features were related to the PFS stratification of lung-cancer patients with SBRT. T-stage and overall stages (TNM) were independent factors for predicting PFS stratification. AUCs under the ROC curve of the radiomics, clinical EGFR, and radiogenomic models were 0.84, 0.67, and 0.86, respectively. The calibration curve shows that the predicted value of the radiogenomic model was in good agreement with the actual value. The decision and influence curve showed that the model had high clinical application values. After Bootstrap validation, the mean AUC of the radiogenomic model was 0.850(95%CI 0.849-0.851).
conclusionsThe radiogenomic model based on
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.