ArticleFrontiers in oncology2022
Combined Radiomic and Visual Assessment for Improved Detection of Lung Adenocarcinoma Invasiveness on Computed Tomography Scans: A Multi-Institutional Study.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed, 6 citations in OpenAlex.
- Development of a Risk Prediction Model for Bone Metastasis in Lung Adenocarcinoma With a T1 Primary Tumor Based on Intratumoral and Peritumoral Radiomics.Cancer medicine · 2026Article
- ADAM: automated digital phenotyping and morphological texture analysis of bone biopsy images using deep learning.JBMR plus · 2025Article
- Radiomics-based decision support tool with ground-glass opacity status of 5-year survival prediction for early-stage non-small cell lung cancer.Reports of practical oncology and radiotherapy : journal of Greatpoland Cancer Center in Poznan and Polish Society of Radiation Oncology · 2025Article
- Rank acquisition impact on radiomics estimation (AсquIRE) in chest CT imaging: A retrospective multi-site, multi-use-case study.Computer methods and programs in biomedicine · 2024Article
- The value of intratumoral and peritumoral radiomics features in differentiating early-stage lung invasive adenocarcinoma (≤3 cm) subtypes.Translational cancer research · 2024Article
- Moving from conventional to adaptive risk stratification for oropharyngeal cancer.Frontiers in oncology · 2024Review
- Radiomics to the Rescue.Annals of surgical oncology · 2022Article
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Authors and funding
14 authors at 8 institutions in 2 countries.
Funding
Abstract
Objective: The timing and nature of surgical intervention for semisolid abnormalities are dependent upon distinguishing between adenocarcinoma- Methods: The study comprised 268 patients from 4 institutions with resected (<=3 cm) semisolid lesions with confirmed histopathological diagnosis of MIA/AIS or INV. A total of 248 radiomic texture features from within the tumor nodule (intratumoral) and adjacent to the nodule (peritumoral) were extracted from manually annotated lung nodules of chest CT scans. The datasets were randomly divided, with 40% of patients used for training and 60% used for testing the machine classifier (Training D Results: The top five radiomic stable features included four intratumoral (Laws and Haralick feature families) and one peritumoral feature within 3 to 6 mm of the nodule (CoLlAGe feature family), which successfully differentiated INV from MIA/AIS nodules with an AUC of 0.917 [0.867-0.967] on D Conclusions: Addition of advanced image analysis
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