ArticleThe oncologist2018
Comprehensive Computed Tomography Radiomics Analysis of Lung Adenocarcinoma for Prognostication.
Article in The oncologist, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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24 citing papers in PubMed, 35 citations in OpenAlex.
- Radiomic model for differentiating parotid pleomorphic adenoma from parotid adenolymphoma based on MRI images.BMC medical imaging · 2021Trial
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- Enhancing Identification of High-Risk cN0 Lung Adenocarcinoma Patients Using MRI-Based Radiomic Features.Cancer research and treatment · 2025Article
- Histological Subtype Classification of Non-Small Cell Lung Cancer with Radiomics and 3D Convolutional Neural Networks.Journal of imaging informatics in medicine · 2024Article
- Predictive value of delta-radiomic features for prognosis of advanced non-small cell lung cancer patients undergoing immune checkpoint inhibitor therapy.Translational lung cancer research · 2024Article
- Value of radiomics in differentiating synchronous double primary lung adenocarcinomas from intrapulmonary metastasis.Journal of thoracic disease · 2023Article
- Preoperative diagnosis of solitary pulmonary nodules with a novel hematological index model based on circulating tumor cells.Frontiers in oncology · 2023Article
- CT-based radiomic phenotypes of lung adenocarcinoma: a preliminary comparative analysis with targeted next-generation sequencing.Frontiers in medicine · 2023Article
- Reappraising the clinical usability of consolidation-to-tumor ratio on CT in clinical stage IA lung cancer.Insights into imaging · 2022Article
- Radiomics for differentiating minimally invasive adenocarcinoma from precursor lesions in pure ground-glass opacities on chest computed tomography.The British journal of radiology · 2022Article
- Article
- The Value of CT-Based Radiomics for Predicting Spread Through Air Spaces in Stage IA Lung Adenocarcinoma.Frontiers in oncology · 2022Article
- Multivariate Analysis on Development of Lung Adenocarcinoma Lesion from Solitary Pulmonary Nodule.Contrast media & molecular imaging · 2022Article
- Radiomics-guided deep neural networks stratify lung adenocarcinoma prognosis from CT scans.Communications biology · 2021Article
- LncRNA LINC00337 sponges mir-1285-3p to promote proliferation and metastasis of lung adenocarcinoma cells by upregulating YTHDF1.Cancer cell international · 2021Article
- Tumor Margin Contains Prognostic Information: Radiomic Margin Characteristics Analysis in Lung Adenocarcinoma Patients.Cancers · 2021Article
- Lung cancer histology classification from CT images based on radiomics and deep learning models.Medical & biological engineering & computing · 2021Article
- Radiomics nomogram for prediction disease-free survival and adjuvant chemotherapy benefits in patients with resected stage I lung adenocarcinoma.Translational lung cancer research · 2020Article
- Radiomics as a personalized medicine tool in lung cancer: Separating the hope from the hype.Lung cancer (Amsterdam, Netherlands) · 2020Review
- Marginal radiomics features as imaging biomarkers for pathological invasion in lung adenocarcinoma.European radiology · 2020Article
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9 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIn this era of personalized medicine, there is an expanded demand for advanced imaging biomarkers that reflect the biology of the whole tumor. Therefore, we investigated a large number of computed tomography-derived radiomics features along with demographics and pathology-related variables in patients with lung adenocarcinoma, correlating them with overall survival. MATERIALS AND
methodsThree hundred thirty-nine patients who underwent operation for lung adenocarcinoma were included. Analysis was performed using 161 radiomics features, demographic, and pathologic variables and correlated each with patient survival. Prognostic performance for survival was compared among three models: (a) using only clinicopathological data; (b) using only selected radiomics features; and (c) using both clinicopathological data and selected radiomics features.
resultsAt multivariate analysis, age, pN, tumor size, type of operation, histologic grade, maximum value of the outer 1/3 of the tumor, and size zone variance were statistically significant variables. In particular, maximum value of outer 1/3 of the tumor reflected tumor microenvironment, and size zone variance represented intratumor heterogeneity. Integration of 31 selected radiomics features with clinicopathological variables led to better discrimination performance.
conclusionRadiomics approach in lung adenocarcinoma enables utilization of the full potential of medical imaging and has potential to improve prognosis assessment in clinical oncology. IMPLICATIONS FOR PRACTICE: Two radiomics features were prognostic for lung cancer survival at multivariate analysis: (a) maximum value of the outer one third of the tumor reflects the tumor microenvironment and (b) size zone variance represents the intratumor heterogeneity. Therefore, a radiomics approach in lung adenocarcinoma enables utilization of the full potential of medical imaging and could play a larger role in clinical oncology.
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