ArticleCancers2021
Tumor Margin Contains Prognostic Information: Radiomic Margin Characteristics Analysis in Lung Adenocarcinoma Patients.
Article in Cancers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Initial experience of deep learning reconstruction algorithm in lung kernel: clinical usefulness for lung nodules at ultra-low-dose protocol.European radiology · 2026Article
- Assessing Invasiveness of Ground-Glass Nodules Using Ternary-Class CT Radiomics Models: A Multi-Center Study with SHAP Explanations.Cancer management and research · 2026Article
- Fully end-to-end automated bladder segmentation and risk assessment based on bladder morphology features for predicting upper urinary tract dysfunction.International journal of surgery (London, England) · 2026Article
- The value of different machine learning radiomics based on DCE-MRI in predicting axillary lymph node status of breast cancer.Translational cancer research · 2025Article
- Deciphering the intratumoral histologic heterogeneity of lung adenocarcinoma using radiomics.European radiology · 2025Article
- Enhancing Identification of High-Risk cN0 Lung Adenocarcinoma Patients Using MRI-Based Radiomic Features.Cancer research and treatment · 2025Article
- Computed tomography characteristics of cN0 primary non-small cell lung cancer predict occult lymph node metastasis.European radiology · 2024Article
- Quantitative peritumoral magnetic resonance imaging fingerprinting improves machine learning-based prediction of overall survival in colorectal cancer.Exploration of targeted anti-tumor therapy · 2024Article
- Artificial Intelligence-based Radiomics in the Era of Immuno-oncology.The oncologist · 2022Review
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
We aimed to investigate the relationship between tumor radiomic margin characteristics and prognosis in patients with lung cancer. We enrolled 334 patients who underwent complete resection for lung adenocarcinoma. A quantitative computed tomography analysis was performed, and 76 radiomic margin characteristics were extracted. The radiomic margin characteristics were correlated with overall survival. The selected clinical variables and radiomic margin characteristics were used to calculate a prognostic model with subsequent internal and external validation. Nearly all of the radiomic margin characteristics showed excellent reproducibility. The least absolute shrinkage and selection operator (LASSO) method was used to select eight radiomic margin characteristics. When compared to the model with clinical variables only (C-index = 0.738), the model incorporating clinical variables and radiomic margin characteristics (C-index = 0.753) demonstrated a higher C-index for predicting overall survival. In the model integrating both clinical variables and radiomic margin characteristics, convexity, a Laplace of Gaussian (LoG) kurtosis of 3, and the roundness factor were each independently predictive of overall survival. In addition, radiomic margin characteristics were also correlated with the micropapillary subtype, and the sphericity value was able to predict the presence of the micropapillary subtype. In conclusion, our study showed that radiomic margin characteristics helped predict overall survival in patients with lung adenocarcinomas, thus implying that the tumor margin contains prognostic information.
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