ArticleBMC medical imaging2020
Using CT texture analysis to differentiate between peripheral lung cancer and pulmonary inflammatory pseudotumor.
Article in BMC medical imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it, 26 citations in OpenAlex.
- Revolutionizing diagnosis of pulmonaryFrontiers in microbiology · 2024Pooled it
- Artificial intelligence-assisted early screening of lung cancer and accurate diagnosis of pulmonary nodules: research progress and clinical prospects from radiomics to multi-omics integration: a narrative review.Journal of thoracic disease · 2026Review
- Exploring the evolving landscape of radiomics in lung cancer: a comprehensive bibliometric analysis [2008-2024].Quantitative imaging in medicine and surgery · 2025Article
- The Role of [18F]FDG PET Imaging for the Assessment of Pulmonary Lymphangitic Carcinomatosis: A Comprehensive Narrative Literature Review.Diagnostics (Basel, Switzerland) · 2025Review
- Image analysis:Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Differentiation of inflammatory pseudotumors and malignant pulmonary nodules using the time-to-peak in first-pass dual-input volume computed tomography-perfusion.Quantitative imaging in medicine and surgery · 2025Article
- Solitary fibrous tumor of the pleura: biphasic contrast-enhanced CT findings with emphasis on differential diagnosis.Frontiers in medicine · 2025Article
- Prediction of acute pancreatitis severity based on early CT radiomics.BMC medical imaging · 2024Article
- PET/CT radiomics and deep learning in the diagnosis of benign and malignant pulmonary nodules: progress and challenges.Frontiers in oncology · 2024Review
- Update of Diagnosis and Targeted Therapy for ALKCurrent treatment options in oncology · 2023Review
- Artificial Intelligence-Assisted Processing of Anterior Segment OCT Images in the Diagnosis of Vitreoretinal Lymphoma.Diagnostics (Basel, Switzerland) · 2023Article
- Prediction of histologic types in solid lung lesions using preoperative contrast-enhanced CT.European radiology · 2023Article
- Current status and prospect of PET-related imaging radiomics in lung cancer.Frontiers in oncology · 2023Review
- Combined model of radiomics, clinical, and imaging features for differentiating focal pneumonia-like lung cancer from pulmonary inflammatory lesions: an exploratory study.BMC medical imaging · 2022Article
- Analysis of the value of enhanced CT combined with texture analysis in the differential diagnosis of pulmonary sclerosing pneumocytoma and atypical peripheral lung cancer: a feasibility study.BMC medical imaging · 2022Article
- CT Texture Analysis of Pulmonary Neuroendocrine Tumors-Associations with Tumor Grading and Proliferation.Journal of clinical medicine · 2021Article
- Preliminary study of 3 T-MRI native T1-mapping radiomics in differential diagnosis of non-calcified solid pulmonary nodules/masses.Cancer cell international · 2021Article
- A subregion-based positron emission tomography/computed tomography (PET/CT) radiomics model for the classification of non-small cell lung cancer histopathological subtypes.Quantitative imaging in medicine and surgery · 2021Article
- Pre-Processing Method for Contouring the Uptake Levels of [18F] FDG for Enhanced Specificity of PET Imaging of Solitary Hypermetabolic Pulmonary Nodules.Journal of clinical medicine · 2021Article
- Comparison of the Detection Rates of Different Diagnostic Methods for Primary Peripheral Lung Cancer.Frontiers in oncology · 2021Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundThis study is to distinguish peripheral lung cancer and pulmonary inflammatory pseudotumor using CT-radiomics features extracted from PET/CT images.
methodsIn this study, the standard 18F-fluorodeoxyglucose positron emission tomography/ computed tomography (18 F-FDG PET/CT) images of 21 patients with pulmonary inflammatory pseudotumor (PIPT) and 21 patients with peripheral lung cancer were retrospectively collected. The dataset was used to extract CT-radiomics features from regions of interest (ROI), The intra-class correlation coefficient (ICC) was used to screen the robust feature from all the radiomic features. Using, then, statistical methods to screen CT-radiomics features, which could distinguish peripheral lung cancer and PIPT. And the ability of radiomics features distinguished peripheral lung cancer and PIPT was estimated by receiver operating characteristic (ROC) curve and compared by the Delong test.
resultsA total of 435 radiomics features were extracted, of which 361 features showed relatively good repeatability (ICC ≥ 0.6). 20 features showed the ability to distinguish peripheral lung cancer from PIPT. these features were seen in 14 of 330 Gray-Level Co-occurrence Matrix features, 1 of 49 Intensity Histogram features, 5 of 18 Shape features. The area under the curves (AUC) of these features were 0.731 ± 0.075, 0.717, 0.748 ± 0.038, respectively. The P values of statistical differences among ROC were 0.0499 (F9, F20), 0.0472 (F10, F11) and 0.0145 (F11, Mean4). The discrimination ability of forming new features (Parent Features) after averaging the features extracted at different angles and distances was moderate compared to the previous features (Child features).
conclusionRadiomics features extracted from non-contrast CT based on PET/CT images can help distinguish peripheral lung cancer and PIPT.
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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.